Cover for No Agenda Show 1157: Carbon Captions
July 21st, 2019 • 2h 55m

1157: Carbon Captions

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0:00
you can step over the body Adam Curry
0:10
this is no agenda
0:22
I'm Adam curry from Northern California
0:25
where we're waiting the female for and
0:28
hoping for the male afro I'm John C
0:33
Dvorak this is rather interesting as I
0:39
checked into the shot room of trolls
0:41
today and all I can see is them talking
0:44
about some character and it's gonna be a
0:46
woman and they said what's going on and
0:48
was pissed off what is happening
0:52
apparently Natalie Portman is going to
0:55
be the female for everyone's all upset
1:00
about this well it's fun as stupid as
1:03
anything I've ever heard of but okay
1:05
let's just make a female Hulk then
1:08
you're talking and actually that'd be
1:11
more believable yeah and isn't there
1:14
like a bat woman or something else just
1:17
everyone there's a lot of bat women I'm
1:24
just surprised like of all the things to
1:27
care about in the world this is not high
1:29
on the list no but I've seen this go on
1:34
for at least a while I got bumped off of
1:36
course because my Wi-Fi problem but it's
1:38
just been ongoing garbage garbage movies
1:46
yeah they're fun to watch on the
1:49
airplane well I don't know about that I
1:52
think nothing whole airplane viewing my
1:56
experience nothing holds a candle to the
1:59
Fast and Furious series and there's
2:05
always eight of them to watch so it's
2:07
great
2:08
oh how did I catch I think I was on a
2:11
plane once sitting next to somebody and
2:13
I was trying to watch some you know semi
2:15
good movie and the person next to me was
2:18
watching fast and furious five or
2:20
someone of the movement and I couldn't
2:22
keep my eyes off their screen exactly so
2:26
I had to put it on well this brings me
2:30
to some unscientific research I've been
2:33
doing about the use of closed captioning
2:36
amongst Millennials and a Generation Z
2:39
yes we got some notes about this I got a
2:42
number of notes I did some informal
2:45
polls over at No Agenda social calm I
2:48
you know been hunting around people
2:50
giving me unsolicited feedback which is
2:53
always nice and really it comes down to
2:55
three people are blaming it or
2:59
attributing it to three different things
3:02
and I have three emails that I've just
3:05
picked out here this is JV I watch
3:08
everything with the carbon cap at carbon
3:10
caption there you go Carbon captions
3:20
that's perfect I use watch everything
3:24
with closed caption / subtitles mostly
3:28
due to decades of frustrating sound
3:30
mixing movies and modern shows are mixed
3:33
with wide dynamic range room shaking
3:35
explosions and gunshots are immediately
3:37
followed by characters whispering
3:38
important plot details Game of Thrones
3:41
does this but it's nothing new also like
3:44
many Millennials I grew up with a big
3:46
family stuffed in a small home and I've
3:47
lived with roommates ever since any time
3:50
there would be a siren or scream or
3:51
anything else unpleasant my mom would
3:53
burst through the door and tell me that
3:55
baby was trying to sleep
3:57
well these are these are these are two
4:03
two together one and that I got a lot of
4:07
people saying hey you know they're
4:08
mixing it wrong the problem is trying to
4:11
jam Dolby 5.1 through stereo speakers
4:15
and therefore the balance is off
4:17
there maybe I think there's some
4:19
validity to that part oh I agree I think
4:22
there's plenty of validity to that
4:23
that's one of the reasons you have to
4:25
take things into your own hands and
4:27
you've got to either pull back on some
4:29
of these processing systems like Dolby I
4:31
go to straight stereo there's ways
4:33
around it I mean you're just running
4:36
amok we're just letting whatever happens
4:39
happens it threw a couple of squeaky
4:40
speakers on the TV yeah you're asking
4:43
for trouble
4:43
I agree with that 100% yes it seems that
4:46
there there's a real issue there and I
4:48
don't I've really never interests been
4:52
ain't very interested in surround sound
4:54
and and when it comes to television
4:56
never really set one I think I've had
4:59
one in the past because I yeah so I just
5:02
don't care for me it's just I turn it up
5:05
and then I can hear everything there's
5:07
nothing out now we get into some
5:09
interesting areas accents this is from
5:12
Jay every single time I watch TV with
5:14
people under 30 they have the closed
5:16
caption on I don't understand it either
5:18
I get it when it's something British and
5:20
they can't understand access but it's
5:22
always on
5:24
I dated a gal who was 28 and I tried
5:26
watching Emily with her with captions on
5:30
because it's in France and she fell
5:31
asleep instantly it was weird because
5:33
growing up we could focus on the film
5:35
and follow along with the translation
5:36
but for her it was like catnip every
5:39
single other thing she wanted to have
5:41
the captions on for it was infuriating
5:43
luckily that relationship failed pretty
5:44
fast okay
5:49
there's one other thing there is a new
5:52
disorder that has not been codified yet
5:55
in the dsm-5 or you know but it is a
5:59
real thing and it's only the the true
6:03
cause is not known although I have some
6:05
thoughts and that's auditory processing
6:08
disorder and this is closely related to
6:13
ADHD and AD D both attention deficit
6:17
disorders and what happens is you can
6:22
hear perfectly well but you are unable
6:24
to process your brain is either unable
6:27
or slow at processing what the sounds
6:31
are and turning them into words
6:34
now and I want to say a front that I
6:37
grew up with subtitles
6:39
Dutch television had English movies
6:44
they'd play the English soundtrack and
6:46
they would translate and deathy was very
6:47
professionally done they had hundreds of
6:49
people working at the state-run
6:52
subtitling organization and I learned to
6:55
deal with them perfectly well to ignore
6:57
them while I'm while I'm while I was
6:58
watching television I think it also
7:00
helped a lot of people learn English
7:02
extremely well versus Germany where they
7:06
always did an overdub but the auditory
7:09
processing disorder I think is something
7:11
that comes from people who already have
7:14
a potential deficit disorder cuz a lot
7:18
of feedback that I received said well I
7:21
really can't focus on the movie or the
7:24
whatever is on the television the whole
7:27
time and I look at my phone I'm you know
7:29
this the people are distracted this is
7:31
something we know the young people often
7:33
wake up in the middle of the night just
7:35
to check their phone so there's a
7:37
distract and also second-screen I guess
7:40
to a degree it's like oh let me look up
7:42
that actor or what's he doing you know
7:44
what's so what's going on is someone
7:45
tweeting about this particular show that
7:47
I'm watching and because of that many
7:49
people have said well they're really
7:51
helpful because I can just you know I
7:53
can catch up real quick and say look at
7:55
my phone oh I just I like and I could I
7:56
can follow along despite darting back
7:58
and forth and reading the subtitles and
8:00
I pause it to you as my research my
8:05
alert my posit to you as I as my
8:09
research will continue in this regard is
8:11
that because of the lazy use of
8:15
subtitles whether it's to hear things
8:19
that were drowned out by sound effects
8:22
and music whether it's to instead of
8:26
really focus on what's happening with an
8:29
accent and try and you drink and parse
8:31
things from all the information always
8:33
in this situation he's there he might
8:35
have said something like this
8:36
was it something I didn't understand
8:38
that people's brains have become lazy
8:42
and just like you have someone who may
8:45
be blind there
8:47
auditory skills increased astronomically
8:50
of vice versa
8:52
people who are deaf can often see things
8:54
much clearer or at least they process
8:56
the information in a different way so
8:58
the posit I have is because of the you
9:03
know some of the reasons that people
9:04
have resorted to closed captioning and
9:06
there abundant availability now due to
9:09
the American with Disabilities Act etc
9:11
that the brain is starting to lose some
9:15
of that auditory processing power
9:17
because I have asked people ok turn them
9:20
off and Heidi do it's like I really
9:22
can't handle it anymore I need the
9:24
closed captions I can't follow along and
9:27
I it sounds to me like what a deadly
9:29
said she said it sounded like what's the
9:32
little Woodstock from Charlie Brown
9:35
can't really process the audio so I
9:39
would say we have possibly auditory
9:42
processing disorder being created by use
9:45
of closed captioning which we will
9:48
counter that okay most of these kids and
9:53
I would question any one of them that
9:55
have this problem which especially with
9:56
the Woodstock the phenomenon do they
10:02
ever go to music concerts yep the music
10:07
constants are over amped they're way
10:09
above the legal limits of X number of
10:11
decibels I think one hundreds the max
10:13
and these things get 140 150 many of
10:16
them come home with ringing ears they're
10:17
all making themselves deaf at these
10:19
concerts very rarely do any of them have
10:23
the wherewithal to wear ear protection
10:25
they don't know that in for example in
10:27
California all the bars in California
10:30
that have bands in the bars they have
10:32
required to have ear protection behind
10:35
the counter that you can ask for and you
10:37
get it for free nobody wears the stuff
10:40
and they just listen to this music at
10:41
these outrageous decibel levels which
10:43
started in the 60s with with the
10:46
Marshall amp and bands like Blue Cheer
10:49
and others and now they're all deaf and
10:51
that's why they can't hear anything I
10:53
don't blame them before bringing up the
10:54
captions because they're all going to be
10:56
wearing your hearing aids desta your
10:59
investment of the 21st century
11:01
hearing aids well I'll add to that
11:04
because I'm not opposed to this theory I
11:06
would add to that the incessant overuse
11:09
of air pods which are hanging their ears
11:13
all day long now I don't know if that
11:16
can cause damage necessarily but for
11:20
sure an unsound injection but it also
11:23
changes your auditory processing because
11:28
once it's further away you're not used
11:29
to that anymore people listen to
11:31
podcasts thank God
11:32
listening to podcasts they turn on the
11:35
the microphone functions so they can
11:37
hear whatever's going when they just to
11:38
keep the earbuds in I'm taking a phone
11:40
call they're in continuously all day
11:43
long and they're intended that way I
11:46
think once you then remove that from the
11:50
direct in ear canal it may that it may
11:53
be still I'm gonna come down into a
11:54
processing issue I don't know if it's
11:56
actual hearing damage could be maybe
11:58
it's a combination but we need to
12:00
research further so I will keep my lab
12:03
coat on yes please
12:06
GUP I think it's a lot of hearing damage
12:08
going on I wish to I saw somebody the
12:10
other day somebody roaming around went
12:13
the store with the ear buds there's
12:15
those hanging things as little Apple
12:17
ones the little white ones that's the
12:18
ones the airport is there and they
12:20
looked like they were as zombies goo yes
12:25
goo but we're changing the subject or
12:27
going to goo keyword goo so I've got
12:30
this little battery that I found and I'm
12:32
charging it but I realized it's one of
12:34
these one of these devices and I have a
12:36
bunch of these things there Ryan does
12:39
and I'm not the only one oh 'test this
12:41
because this stuff has been around this
12:43
goo has been around for at least almost
12:46
20 years and I'm talking about the soft
12:49
spray that they put over devices it's
12:51
like a slightly rubbery compound that
12:54
they spray on top of cool associate ice
12:57
nice cool feels like a gives you instead
12:59
of a hard plastic you get this kind of
13:01
rubbery soft little coating that they
13:04
put on what would I find this on I'm not
13:06
sure I know it's a lot of stuff you find
13:08
again you'll find it when it's over ten
13:10
years old it turns to goo
13:13
and he got this sticky goo all over he
13:16
and you can't get it off you have to
13:17
take some solvent and maybe maybe it'll
13:20
come off you can kind of scrape it off
13:22
with your thumbnail his Ben and I had
13:25
like a Sony a DAT player that was coated
13:30
in this old felt so professional
13:32
although it's the professional coating
13:35
it's a professional coating and then now
13:37
it's like sticky you can actually know
13:39
if you grab it it's like it sticks the
13:42
finest mainly on electronic devices is
13:45
that we find a lot of this this claim a
13:47
lot of electronic devices you find on
13:49
this battery pack that I have it was a
13:51
high end battery pack John is be honest
13:53
is there a problem with your real doll
13:55
that you want to tell us about no it's
13:58
not even the same type of substance this
14:00
is some reliably informed well I'm
14:04
pretty sure this is always black is
14:07
always black I've never seen this in any
14:08
of the color dark you know just
14:11
perfectly pitch black and it's then it
14:14
was used and used and used cuz it was so
14:15
cool hmm I surprised you haven't run
14:19
into this I have I think but it's been a
14:23
while I mean I don't really have any
14:25
stuff that's I don't know well they
14:28
stopped I think they backed off on it
14:30
but I that's why I haven't noticed this
14:32
for a while - I found this battery which
14:35
had this stuff all over in this
14:36
batteries just sticky and gooey and it's
14:40
got this this stupid coating on it that
14:42
somebody decided was a good idea
14:45
doesn't seem like you to not at least
14:46
know the name of the coating that's
14:48
something that you would that's a good
14:51
point I think you got me there
14:52
I should know the name of this stupid
14:54
coating but I'm telling you it was a
14:57
dumb idea to begin with and apparently
14:59
it's not worked out and this is the
15:01
classy example for all you know it's
15:03
carcinogenic they just do the door look
15:07
what we got this is a great idea
15:08
I reminded uh how about like hand grips
15:12
on a bike would that be the same type of
15:13
stuff yes stuff the way that we approach
15:17
is the whole substance is softer rubber
15:19
okay
15:21
no it's a coating on hard plastic if the
15:25
hand grips on a bike or a hard plastic
15:27
yeah maybe but know that you can't get
15:29
him on there if they were but I'm
15:32
remanded I'm reminded of us I was at
15:34
some event and you remember this when
15:37
they were they took all this paper off
15:38
the market it was all the stuff that was
15:40
used it was it was heat sensitive it was
15:43
used for cash registers and the purple
15:46
paper that would that the people were
15:49
touching and getting sick from well they
15:51
weren't getting sick from but it was
15:52
carcinogenic that bisphenol or some damn
15:56
thing on it well I ran into the guy that
16:00
made a mint selling this the paper that
16:03
was sold to substitute for it oh and he
16:07
told me it kind of offed off to record
16:10
which apparently it's not off the record
16:12
anymore not anymore he says you know I
16:15
know that her stuff was bad but they
16:18
never ever tested this stuff but they
16:19
say just anything to substitute he says
16:23
for all we know this could be ten times
16:25
worse
16:26
instead of saying off the record just to
16:27
say because that sound then sound you
16:30
sound like a journalist who's cheating
16:32
oh I I'm I've never been a cheater okay
16:39
no I think it's there's a time limit off
16:43
the record oh okay under umberto I must
16:51
say that it was he was skeptical about
16:54
his own product because and as I'm
16:56
skeptical about this goo all right well
16:58
I'd love to know more about what it is
17:00
and how we can avoid it
17:01
well I can't avoid it they've used it
17:03
and used it and use it for years and now
17:04
it's all over the place and it's just
17:06
and if you if it's left in the hot Sun
17:08
after goooo Feist this gets really bad
17:11
as sticks it'll stick to whatever it's
17:13
touching you have a piece of paper stuck
17:14
to it oh it's unbelievable is that
17:17
people out there know what I'm talking
17:18
about yes
17:23
well we'd want to go for me you know I
17:25
have a little presentation I'd like to
17:27
do some some general news first
17:31
okay you have a story that you've been
17:34
following up on is that what we're
17:35
talking about yeah yeah yeah yeah I'm
17:38
interested in listening to this you want
17:40
to do this now cuz I figure we do a
17:41
couple you know top news items first and
17:43
then like what what's the top yes I
17:45
don't know what do you got I don't have
17:48
a clip for it bag just was reading this
17:50
morning about this situation in Canada
17:52
with the pipe liners going from Alberta
17:56
to BC and back and there's a big
17:58
squabble about it which I thought was
18:00
kind of interesting no clip though huh I
18:04
haven't no I don't have about the
18:11
Cummings in Cortes show that was
18:12
highlighted on Democracy Now okay I
18:15
don't really know what it is let's let's
18:17
talk about it and there is the come I
18:20
got a clip the Cummings in Cortes Show
18:22
okay there's testifying in front of
18:26
Congress is starts with duh the GAO is
18:30
testifying in front of Congress is that
18:33
the guy who's the homeland security or
18:39
this guy and he's trying to do a good
18:41
job and they're just killing him yeah
18:43
back on Capitol Hill House Democrats
18:45
grilled president Trump's acting
18:47
Homeland Security Secretary Thursday
18:49
over migrant family separations the
18:51
deaths of children taken into US custody
18:53
and reports of squalid overcrowded in
18:56
dangerous conditions in u.s. immigration
18:58
jails
18:59
this is House Oversight Committee Chair
19:01
Elijah Cummings questioning the acting
19:03
DHS chief Kevin McHale inan we're doing
19:10
our level best in a very G what does
19:11
that mean what does that mean when a
19:13
child come on man
19:22
what's that about
19:24
none of us also at Thursday's house
19:33
oversight here in Congress member
19:34
Alexandria Castillo Cortez confronted
19:37
Kevin Mack Elena and over hateful
19:38
messages shared by thousands of current
19:41
and former Border Patrol agents on a
19:43
private Facebook group the group's
19:45
online discussions exposed by ProPublica
19:47
earlier this month are full of
19:49
homophobic anti-immigrant and
19:51
misogynistic content about migrants and
19:53
asylum seekers as well as racist attacks
19:55
on Texas Congress member Veronica
19:57
Escobar and on Acacio Cortez who's
20:00
depicted in a photoshopped image being
20:02
sexually assaulted by President Trump
20:04
this is Congress member Acacio Cortez
20:06
questioning Mack Elena
20:08
did you see the posts mocking migrant
20:12
children's deaths I did did you see the
20:15
post planning physical harm to myself
20:17
and congresswoman Escobar yes and I
20:19
directed an investigation within minutes
20:21
of reading the article did you see the
20:24
images of officers circulating
20:29
photoshopped images of my violent rape
20:31
yes I did are those officers on the job
20:36
today
20:36
and responsible for the safety of
20:38
migrant women and children so there's an
20:41
aggressive investigation on this issue
20:43
proceeding you've heard the chief of the
20:45
ball roll the most senior female
20:47
official and law enforcement across the
20:49
entire country say that these posts do
20:51
not meet our standards of conduct and
20:53
they will be followed up aggressively is
20:55
this still about the secret group that
20:57
they were posting on yeah she brought
21:00
back up cuz it's about her that's why
21:02
she brought it back it's about her it's
21:04
about her that's what it's got to be
21:05
about me yeah I love Elijah Cummings
21:09
who's just yelling and screaming come on
21:11
man that human beings was let's not give
21:14
a shit about the human beings dying on
21:16
the streets of Los Angeles San Francisco
21:19
Boston
21:21
Seattle all right if she'll let me
21:24
follow this up first let's go to some
21:27
questioning about about immigration and
21:30
there's no one better than campus reform
21:32
to go out and talk to this country's our
21:35
country's brightest future the new kids
21:38
who are just ready to graduate and
21:40
they're thinking about these things and
21:42
here's a test
21:43
let's go read them something Obama said
21:45
and let's tell them that Trump said it
21:48
and then see what their reaction is and
21:50
then let's spring the truth on them
21:52
you've seen the gag before it's
21:55
hilarious these could be pre-selected I
21:57
mean select they are selected could be
21:59
selected only to show idiots but still
22:02
the sampling is astounding so have a
22:05
quote for you here that's been making
22:06
the rounds on social media about the
22:08
deportation of criminal illegal aliens
22:10
we are a nation of laws undocumented
22:12
workers broke our laws I believe they
22:14
must be held accountable especially
22:15
those who may be dangerous that's why
22:16
over the past six years deportations of
22:18
criminals are up 80 percent and we're
22:20
going to keep focusing on threats to our
22:21
security what's your thought on that
22:23
quote in that policy in general
22:25
I think that policy comes from a place
22:27
of like white American nationalism
22:29
Donald Trump has kind of like embraced
22:31
this rhetoric of like racism and
22:33
xenophobia that is not beneficial to our
22:35
country at all I don't think that that
22:37
quote really stands true this
22:38
administration has totally not done
22:40
anything immoral this is really awful
22:42
amnesty does not necessarily mean that
22:44
we're losing border security I think
22:46
that Trump feels that way I think that's
22:48
a bad decision
22:49
because like the United States should be
22:51
open to like immigrants like it's like
22:54
they call it lands with the free for a
22:55
reason we'd have to advocate for those
22:57
kind of people with people like in
22:58
Congress like Ocasio Cortes who is
23:00
helping people overcome these kinds of
23:03
things crimes do not nullify your
23:05
humanity and people are coming here in
23:07
search of opportunity I'll show you the
23:10
person who said that quote is that
23:15
surprising yeah a little bit why is that
23:18
surprising because I thought it was the
23:20
Trump administration or something like
23:21
that yeah it's quite surprising why did
23:27
you not expect you to be Obama um
23:29
because I just I guess I dunno like it
23:34
just never it never occurred to me that
23:36
it could be Obama is that surprising
23:37
that it's a quote from President Obama I
23:39
was surprising for sure yeah do you
23:41
think it's still a practice of white
23:42
nationalism though to deport criminal
23:44
illegal aliens
23:45
I think the way Trump's doing it is what
23:47
but to this point in Trump's presidency
23:50
Obama actually deported more people
23:52
though so it's in practice there was
23:54
more from Obama though what's your
23:56
question I say my understanding of Obama
24:01
versus Trump is that just that Obama was
24:04
more liberal as far as amnesty and
24:08
border security
24:09
I expected that quote to come from Trump
24:12
does that change your opinion of the
24:13
practice to know that President Obama
24:15
did the same thing actually to this
24:16
points presidency deported more people
24:18
than President Trump at at this point no
24:21
again I just think that there's a moral
24:23
way to do it and
24:25
I don't know a ton about Obama's
24:28
deportation policies but I imagine that
24:30
they were a lot more humane than the
24:32
ones currently going on there you go
24:34
there you go everybody that's your mind
24:36
control at work the programming is
24:38
successful KITT can't even when they
24:40
hear was Obama he was more boy he must
24:43
have done it a more moral way than drum
24:45
kabob yes education system now what's
24:50
interesting with all this undocumented
24:53
illegal immigration going on is that
24:55
when I look around in Austin Texas the
24:58
people who I see who are unholy peeing
25:01
on the street and panhandling I don't
25:02
know if they're actually homeless
25:03
they're panhandling for sure are mostly
25:07
white and they seem to be on drugs we've
25:11
been following and this is going to lead
25:13
right back to the illegal immigrants
25:15
only in California so bear with me for a
25:18
second in Austin we changed the
25:20
Community Guidelines the the local rules
25:24
where you can now sit and lie whatever
25:27
you want you can camp wherever you want
25:29
as long as it's not in front of City
25:30
Hall you can do it on you know Congress
25:32
if you want no problem in front of
25:33
businesses but not in front of City Hall
25:34
everywhere else you can camp
25:36
you can lie down you can Panhandle and
25:38
from the school's bus stops anywhere you
25:40
want and the reason for this is that it
25:42
was unfair and not taking into
25:44
consideration the challenges of life of
25:46
the unhoused and now we are a couple
25:49
weeks into the policy
25:51
let's check in this is a report from
25:52
Lucca station KXAN complaints side to
25:55
Austin's homeless population are growing
25:58
two and a half weeks after new city
26:00
rules were approved to decriminalize
26:02
homelessness some business owners say
26:05
they're seeing even more problems with
26:07
the buzz of his blades Oscar Rivera can
26:10
clean up any mess but he'll say his
26:13
customers hair line isn't the area that
26:15
needs the most help sometimes you'll see
26:18
people land right here right on the
26:20
ground in
26:21
and when you wake when you come early in
26:22
the morning you have to tell them to get
26:23
away please shop
26:25
gallery 44 right off at 290 and man
26:27
check isn't the only business effect
26:29
it's nothing against those people but
26:31
when you're trying to build an
26:32
establishment it's hard to go further in
26:35
and grow just down the street
26:38
straight music has its own problems VP
26:41
Clint Strait has added locks to the
26:43
bathrooms and dumpsters and told me he
26:45
regularly finds needles in the parking
26:47
lot community leaders say this is an
26:49
issue of accountability and the people
26:51
of Austin need to be willing to give out
26:53
more resources if they want to see
26:55
change it's hard to hold homeless people
26:56
accountable for not wearing away their
26:57
trash when they don't have receptacles
26:59
to put them in it's it's tough to hold
27:02
people accountable when they're allowed
27:03
to camp for using the bathroom in public
27:05
places when they don't have a place to
27:06
use the bathroom the bottom line this is
27:08
a complex issue which requires
27:10
thoughtful solutions and it's up to
27:12
stakeholders just like those businesses
27:15
near the homeless camps to provide their
27:17
input one of the things that we can all
27:18
do is work together to come to some sort
27:21
of comprehensive solution next month
27:23
austin city manager spencer Cronk will
27:25
make recommendations on how to better
27:26
tweak the new rules it's expected to
27:28
include reasonable limitations on
27:30
camping and potential changes to the
27:32
city NY ordinance we wanted to get a
27:35
sense of how big of a problem this is
27:37
for people in Austin here's a look at
27:40
the numbers from 3 1 1 calls for service
27:42
requests with the keyword homeless or
27:45
transient show more than 2,700 calls so
27:49
far this year that's almost more than
27:51
all of the calls from last year but it's
27:54
also important to note that the rate of
27:55
homeless people is increasing in Austin
27:58
on the right you'll see the point and
28:00
time count from Ecco while the number of
28:03
people living on the streets or in
28:05
shelters has increased that rate has
28:08
been much more gradual compared to the
28:11
number of complaints and I would say
28:14
extrapolating from that it's because the
28:16
rules change so now there's more
28:17
problems
28:18
small business owners both of those guys
28:22
were
28:24
Latino now be damned who gives a shit
28:27
way to go Austin fantastic
28:29
now dr. Drew Pinsky has been on a tear
28:33
about what's happening in Los Angeles
28:36
he's talking on his radio show he's been
28:38
I think he went on Fox and CNN I know if
28:41
he's been on MSNBC but in two short
28:44
clips he explained not only what the
28:46
real problem is but also what the
28:49
solution is and we've talked about some
28:51
of the historical reasons for this and I
28:53
just wanted to share those he was on
28:55
with scott adams
28:55
which is a rare thing I think for scott
28:58
adams to have someone call in on his
29:00
periscope and technically it's just a
29:02
huge nightmare i guess it proves the
29:06
content good doesn't really matter that
29:08
much I think we've been pretty clear in
29:11
the past that what we see is this is a
29:13
drug problem that's why I like saying
29:15
unhoused I don't know if someone's
29:16
homeless but they're not housed when
29:18
they're sleeping on the ground in Los
29:20
Angeles has a huge problem with this and
29:23
dr. drew who at the office used to but
29:28
he ran a drug addiction clinics he says
29:31
this is all about drugs so even if we
29:35
had if we had more housing if suddenly
29:38
you know housing just appeared out of
29:40
nowhere and it was free these people
29:42
wouldn't necessarily even take a free
29:44
house would they correct that the the
29:47
part that is now driving me to my grave
29:50
I think on this problem is that this is
29:54
a population that if you walk up to them
29:57
and say let's go I've got a great place
29:58
to live the majority and the vast
30:01
majority will refuse and people don't
30:04
believe this but when you are
30:06
chronically mentally ill unless you have
30:08
treatment it's very difficult to live in
30:10
four walls if you're a drug addict you
30:13
seek the streets so there's a attachment
30:17
to this lifestyle that is not being
30:19
addressed the other thing is not only
30:22
would they do not want housing the
30:24
housing is not the problem los Anthem we
30:26
just absorbed in the last year or so
30:28
about conservatively 800,000
30:31
undocumented indepence we're a sanctuary
30:33
city we welcome them in none of them are
30:36
on the streets they all found a place to
30:39
live
30:40
800,000 people in a year found a place
30:43
to live so the government continued to
30:45
focus on the housing it's a hoax and I
30:48
can't understand why they're focused on
30:50
it yes I'm so happy he said this because
30:54
it's true
30:56
you have all these illegal immigrants
30:58
finding a place to stay they're not on
31:00
the streets of LA either so just another
31:03
data point that this is that there's
31:05
something else going on but all all the
31:08
politicians can talk about is that same
31:10
in Austin affordable housing need
31:12
affordable housing now you've actually
31:16
provided the reasoning behind this in
31:18
the past John it's part of dr. Drew's
31:22
solution so I'll get right to it and
31:24
then when we're done you'll remember no
31:26
it's easily solvable it just doesn't fit
31:29
an ideology there's something called the
31:31
lentement tetris act which is what
31:33
allows us to treat patients again you've
31:35
got to read this book called American
31:36
psychosis the language Petros I came out
31:39
in the 1960 and all throughout human
31:42
history when people had chronic
31:44
psychiatric illness or addiction the
31:46
system would determine need for care if
31:50
somebody met criteria for what was
31:53
called need for care
31:54
they were cared for they were put in a
31:56
hospital and cared for and stabilized
31:57
and returned to their life in the 1960s
32:01
there was a guy named Robert Felix that
32:03
that convinced President Kennedy that
32:05
chronic psychiatric illness didn't
32:08
really exist that state hospitals caused
32:10
it
32:11
yes we have these crazy books that were
32:14
going out that made the idea of putting
32:16
people in psychiatric hospital inhumane
32:18
and they passed something called the
32:20
Lanterman tetris act which moved need
32:23
her care to the criteria for care as
32:26
simply harm to self or other and if you
32:29
weren't saying I'm gonna kill myself or
32:31
I'm gonna kill somebody else or I'm
32:33
Napoleon I'm so severely gravely
32:35
disabled which there's a definition that
32:37
we have to work on and we could only
32:39
hold you for 72 hours which doesn't
32:41
accomplish much of anything so we could
32:45
so leave we could help people with with
32:47
harm to self or others in 72 hours
32:50
but gravely disabled could do nothing so
32:52
we must change the definition of great
32:54
be disabled we must expand
32:56
conservatorships we must modify front 47
32:59
so we can start to prosecute drug laws
33:01
again so we can motivate drug addicts to
33:03
get treatment from a drug addicts is
33:05
they go one of three places institution
33:08
which we've taken away prison which
33:10
we've taken away or they die so we're
33:14
leaving drug addicts to die which is
33:16
exactly the policy and now I'm thinking
33:19
it may be on purpose if those are the
33:22
three options it's clear that Los
33:24
Angeles San Francisco Chicago Orlando
33:27
Portland Austin are choosing the die
33:30
option let him die that could be and
33:36
this goes back to your story about
33:38
Reagan One Flew Over the Cuckoo's Nest
33:40
in the sixties was the there was a big
33:43
stink in the 60s the year of Vietnam War
33:47
and hippies and in rock music
33:51
psychedelic scene LSD you name it and it
33:55
was that there was that they bought into
33:58
the idea that he just discussed which
34:00
was that we don't these psychiatric
34:01
places are just terrible and there you
34:06
know nuthouse as it were and they
34:08
started trying to shut him down but
34:10
there was some pushback I probably from
34:13
the Republican side of things but once
34:15
Ronald Reagan got in he went with it and
34:18
so now you don't have the pushback from
34:19
the Republicans because he is a
34:21
Republican and Reagan pretty much
34:23
following the lead of the Liberals shut
34:27
down all the I'd call him I don't want
34:30
to call him internment camps but he shut
34:32
down all the insane asylum since
34:33
California let everyone on the street
34:35
and the problem worsened and worsened
34:37
and worse and from that moment on and
34:39
now it's at the point where it's just
34:40
nuts and they if they don't reopen these
34:43
places you're right you just have a
34:44
bunch of corpses everywhere and I guess
34:46
maybe that is what they want but in the
34:48
meantime there's a couple of things we
34:50
can do okay the most important of which
34:52
if you're in cities like San Francisco
34:53
or Los Angeles or Port Newark or
34:55
Portland find those cities like Austin
34:59
and tell people how liberal they are
35:01
about LA
35:10
it's interesting because here I keep
35:13
telling people that this is there's no
35:15
future and they need to go to San
35:17
Francisco
35:18
specifically I recommend Berkeley area
35:23
well Berkeley's not really as great a
35:26
place as Austin if you got free needles
35:29
on the streets
35:30
well there's free needles everywhere but
35:33
Boston is the most liberal of the places
35:37
that allow gonna let you do it stop I
35:41
don't want all those dead I don't want
35:43
the corpses everywhere that's what it's
35:45
gonna be anyway don't let anyone tell
35:49
you differently this is about drugs it's
35:51
it's a part of the opioid crisis as well
35:53
yeah it's a big part of it
35:56
opioids meth it's it's very much that
36:00
special about Seattle the death of
36:03
Seattle is dying Seattle is dying
36:06
there's some good stuff in there because
36:09
they have a bunch of regular characters
36:11
apparently who they you know the cops
36:13
can't do anything about and the guys are
36:15
strung out and they brag about being
36:16
loaded all the time on one thing or
36:19
another yeah meth is a problem meth is a
36:22
huge problem especially in the Pacific
36:24
Northwest not just the opioids meth
36:26
which is a cheap drug and it gives you
36:28
kind of buzz these guys were looking for
36:30
I suppose and yeah
36:33
fentanyl and math these are terrible
36:35
products yeah yeah so there's there's
36:40
the solutions yeah I don't know about
36:42
prop 47 we don't have that here but I
36:45
wish dr. drew could come and speak at
36:48
the Austin City Council meeting and give
36:51
him a little piece he was invited he
36:53
probably show up nice too he's got
36:55
enough problems in his own backyard
36:57
we'll just keep playing clip see if
36:59
anyone cares nobody cares they just
37:01
assume that everyone drop dead yeah you
37:07
can step over the body hey hey hey
37:12
you can step over just step overs don't
37:16
be just step over and come into the shop
37:19
at the very end of our last show I
37:25
received the clip of the day which I
37:28
thought it was a good clip but I was man
37:31
hindsight like of course I deserve clip
37:33
of the day and that turned into a little
37:37
bit of research I'll refresh your memory
37:39
this is from the Google and censorship
37:42
I'm sorry is laughing at your self
37:46
assurance about clip of the day sure I
37:49
deserve clip of the day just throw it in
37:53
in the number one financial supporter of
37:55
the Hillary Clinton way I need to set it
37:57
up more this is the clip that I receive
37:59
clip of the day for and it's a senator
38:03
Cruz interviewing doctor Robert Epstein
38:06
who has done research and claims that in
38:09
the 2016 election at minimum about two
38:12
and a half but possibly 10 million votes
38:14
were swayed to vote for Hillary Clinton
38:18
based upon Google's algorithm ik bias in
38:23
their search results say some before you
38:25
play the clip nobody
38:28
and there's shows that specialize in
38:30
google podcasts have played this clip I
38:34
was looking for it I haven't heard
38:36
anyone play this clip it was on the
38:37
hearings and there's a there's one show
38:39
that's a Google show and they were
38:41
playing clips from the hearing and they
38:44
never played this clip what Google show
38:45
was that material we know these Google
38:49
shows it's like do you trust Jeff Jarvis
38:51
to be objective on Google in general
38:56
people who do who have if you're allowed
38:59
in the building at Google you're not
39:01
gonna say anything bad about Google
39:04
no it's a conflict of interest thank you
39:07
so here's the clue we have no conflict
39:09
of interest because everyone hates us
39:11
and we taken a vow of poverty it's a
39:14
twofer the number one financial
39:15
supporter of the Hillary Clinton
39:17
campaign in the 2016 election was the
39:19
parent company of Google alphabet now
39:21
who was our first witness they were her
39:23
number one financial donor and your
39:25
testimony is through their deceptive
39:27
search methods they moved 2.6 million
39:30
votes in her direction I would think
39:32
anybody
39:34
whether or not you favor one can't do or
39:36
another should be deeply dismayed about
39:40
a handful of Silicon Valley billionaires
39:42
having that much power over our
39:44
elections to silently and deceptively
39:47
shift vote outcomes again with respect I
39:51
must correct you the 2.6 million is a
39:53
rock bottom minimum the range is between
39:58
two point six and ten point four million
40:01
depending on how aggressively they used
40:03
the techniques that I've been studying
40:04
now for six and a half years Wow could
40:07
you say that again please just two point
40:09
six million is a rock bottom minimum the
40:14
range is between two point six and ten
40:17
point four million votes depending on
40:20
how aggressive they were in using the
40:22
techniques that I've been studying such
40:25
as the search engine manipulation affect
40:28
the search suggestion effect the answer
40:31
bot effect and a number of others they
40:34
control these and no one can counteract
40:37
them these are not competitive these are
40:41
tools that they have at their disposal
40:44
exclusively there you go now of course
40:47
when you hear that you're like even I'm
40:49
going yeah I think I said it on the show
40:52
I don't know if it really swayed ten
40:55
million people to you know to vote for
40:58
Hillary over Trump if they were you know
41:00
they're on the fence and I think I was
41:04
reciting with it with the professor I
41:07
think it probably could have and you
41:09
were very skeptical so this is better
41:11
that you do this report yes you took it
41:13
on I will mention this just as kind of
41:16
as an aside
41:17
let's just say the max is true here and
41:20
the max is true about the illegal
41:23
immigrants that are brought into the
41:24
country to vote for the Democrats if you
41:27
could sway 10 million votes and and have
41:30
maybe I don't know another 10 million
41:32
immigrants all voting for Hillary and
41:35
she still loses how much people really
41:39
hate this woman or or how much does he
41:42
decide or how many how much do people
41:44
really want to vote for Trump
41:46
I mean you could look at it either way
41:47
so a little a little background on on
41:51
the professor is a PhD and senior
41:55
research psychologist research scientist
42:00
media professional author of 15 books
42:02
and reading from his own bio more than
42:04
300 articles on psychology related
42:06
topics including empirical studies and
42:08
science nature psychological science and
42:11
the Proceedings of the National Academy
42:13
of Science as a PhD from Harvard
42:15
University under BF Skinner dr. Epstein
42:18
is this father yeah do you know him BF
42:21
Skinner oh yeah he's the famous
42:22
behaviorist
42:23
okay well the most famous guys ever
42:26
Epstein is the founder and the director
42:29
emeritus of the Cambridge Center for
42:31
behavioral studies he's also hosted
42:33
several radio shows etc he is a
42:36
registered Democrat he voted for Hillary
42:38
Clinton and he started a nonprofit which
42:43
is the American Institute for behavioral
42:45
research and Technology 501c3 I checked
42:48
the 990 filings has about a hundred and
42:54
fifty thousand dollars on hand raised
42:56
about forty thousand dollars every year
42:59
for the past couple of years a little
43:01
bump in 2016 the only people in this
43:04
organization were three Epstein himself
43:08
Tyler Healy who is the the technology in
43:12
fact if you read his bio you know
43:13
exactly what his technology director
43:15
cybersecurity expert full stack
43:17
developer so he basically put together
43:20
all the tests and then Brian Meredith a
43:23
managing director he came to the AI BRT
43:27
after a much honored advertising agency
43:29
career spanning three continents four
43:31
a vice president of Benton Bowles
43:33
founding member and director of the
43:35
International creative team and
43:37
mccann-erickson these are big
43:38
advertising agencies he passed away in
43:41
2017 I think he was probably the
43:44
original funder of of this organization
43:49
and clearly not a lot of funding has
43:52
come in but of course if you're fighting
43:53
Google you might have a lot of enemies
43:56
the Google has I think is the largest
43:59
Lobby er in DC at the moment may be
44:02
battling with China who the hell knows
44:04
maybe they work together I don't know so
44:07
I looked at the research and he has a
44:09
number of research beyond what we're
44:12
talking about here which is search
44:13
engine manipulation effect the SEM he
44:16
he's also looked at the search
44:18
suggestion effect the answer bot effect
44:22
anybody's done many other things would
44:24
that come to psychological behavior and
44:26
I think having an advertising guy in
44:28
early which probably hey how do we use
44:31
today's technology to sell products and
44:33
that's probably he stumped yeah so
44:35
probably the most fair research you get
44:38
because they were looking to figure out
44:39
what manipulates people towards choices
44:42
the research as as published in the PNAS
44:46
which is a Official Journal not only was
44:49
it accepted reviewed it has been
44:51
replicated in Germany this is a big deal
44:54
if you go look at all these bullcrap
44:55
studies everywhere look and see if there
44:57
was a replication of it the replication
45:00
crisis is rampant in in certainly in the
45:04
psychological sciences this you know
45:07
that people can't recreate these studies
45:09
and but still there except what are you
45:10
eating what are you doing it's it's
45:12
distracting me sorry but you're ripping
45:14
paper or I am actually going I'm looking
45:18
for some notes for one don't tear paper
45:21
I wasn't tearing paper when I'm in a
45:23
yeah I'm gonna name the mica so don't
45:24
talk to me name the movie I looked at
45:29
his research it is large groups tens of
45:33
thousands of people double-blind study
45:36
you're tearing paper yet double bluff
45:38
taking people out of a pile
45:41
like don't deem Mike I need you to be
45:44
able to interrupt for a reason I'm just
45:48
here's a pile of paper I take it paper
45:50
what are you looking for something are
45:52
you doing crosses already gone
45:55
is your chair squeaking to your paper
45:58
and they're talking back to each other
46:02
normally you're bitching and moaning my
46:04
interrupting because I'm not
46:07
interrupting please go back alright so
46:14
the research is very deep it is done
46:18
with all of us I mean I'm not a
46:20
scientist but I've seen a lot of
46:21
research throughout 11 years of doing
46:23
the show and it really looks like Keith
46:26
he did all the business the way it
46:29
should be done and in 2014-15 but
46:32
particularly in 2016 he did a number of
46:34
studies and again in 2018 where he would
46:38
look at the results based upon political
46:42
questions that came in from Google query
46:45
from Bing and from Yahoo and the results
46:50
for Google were significantly different
46:52
I don't I think that you know their
46:55
their algorithms are different but the
46:58
research really focused on and this is
47:00
what I found to be interesting is that
47:03
there's the bias of what people click on
47:06
in search results so when you search for
47:12
you propose a question to Google the top
47:15
two results receive all of the clicks
47:18
with 50% going to the top one 30% going
47:22
to the second one it drops off quite
47:24
dramatically interestingly the last the
47:26
bottom one on a page gets more more
47:31
clicks than you know the the five or six
47:33
above it and you can probably figure out
47:35
why we've all done that let me scroll
47:36
down the bottom I'll click this one I'll
47:38
go to the next page but the the click is
47:41
pretty much always on the top one or the
47:44
top two and depending on what is driving
47:48
the results he found that amongst
47:52
undecided voters this is key
47:54
people who are really on the fence and
47:56
it could be you know undecided voters
47:58
can be 10 percent can be 15 percent can
48:00
be 20 percent of an election people on
48:02
the fence that that the the choices
48:08
people make benches usually over 20
48:12
percent the undecided voters are always
48:15
very high right into the election so
48:16
really what kind of percentage we
48:18
talking I've seen as high as 40 so he
48:22
claims and that given an a/b choice that
48:26
the top two links determined people's
48:29
choice and regardless of what that
48:32
content is well obviously it's it's it's
48:36
pointing towards a or B if it's a 20 to
48:39
90 percent will choose a over B just
48:43
because they were the top two links so
48:45
we understand the research because
48:46
that's really what all his research is
48:48
saying his research is saying unlike
48:51
anything else when you have a choice
48:54
between the two candidates and you
48:56
oppose a question the top two link
48:59
answers that you click on that on the
49:01
top of the page will determine who
49:04
you're going to vote for in aggregate
49:06
over your research I should stop you
49:09
mm-hmm I remember it because what you're
49:13
what he's doing is maybe deconstructing
49:16
what has already been done at Google
49:18
because I will remind myself that Sergey
49:21
Brin used to come on the silicon spin
49:23
show one a lot and he one time said to
49:26
me you know we have the most ph d--'s of
49:29
any company in the world and one of
49:33
those ph d--'s doing their they're
49:36
probably trying to figure out well they
49:38
can do stuff to manipulate things or is
49:42
that really what they're doing cuz I
49:44
have I have some some thoughts about
49:46
what is what the where the bias has come
49:49
from but I have a bunch of clips most of
49:51
them are about 50 to 60 seconds long but
49:54
they do tell the story as from different
49:57
interviews that I've put together and
49:59
the first one is Epstein introducing
50:02
himself and giving a brief overview of
50:05
see
50:05
which is the search engine manipulation
50:07
effects this is what he proved in his
50:09
research by the way the whole PDF of his
50:12
research is in the show notes I've been
50:13
researching all kinds of new methods of
50:16
online influence the Internet has made
50:18
possible my first discovery was of
50:21
something called seem SEMA the search
50:24
engine manipulation effect which is the
50:27
impact that a biased search results have
50:31
on opinions and votes when I first
50:34
started doing experiments on this which
50:36
was more than 6 years ago I thought the
50:39
impact would be very small it turns out
50:41
tündi impact is its enormous it's one of
50:45
the largest behavioral effects ever
50:48
discovered in the behavioral sciences
50:50
and I published my first report on this
50:53
effect in the Proceedings of the
50:55
National Academy of Sciences that was in
50:57
2015 and that report has since been
51:00
accessed or downloaded more than 200,000
51:03
times and that's that's a lot for a
51:05
scientific paper and since then I've
51:07
discovered about seven other effects
51:09
these effects are so powerful that if
51:12
they're in the hands of people who have
51:14
particular political leanings together
51:17
they can shift upwards of 15 million
51:21
votes in a presidential election without
51:25
anyone knowing that they're being
51:27
manipulated and without leaving a paper
51:29
trail for authorities to trace so that's
51:33
kind of the same background or I gave
51:35
but it may be more succinct language so
51:37
2016 he decided to monitor searches this
51:40
is before the election of leading up to
51:43
the election and to compare if Google
51:45
was delivering biased search results to
51:49
those top two positions versus
51:50
competitors 2016 I actually set up the
51:54
first ever project to monitor the search
51:58
results that Google Bing and Yahoo were
52:01
showing users prior to the election when
52:04
they conducted election related searches
52:07
and I found that the search results were
52:11
strongly biased in favor of Hillary
52:14
Clinton whom I supported by the way I am
52:16
NOT a conservative
52:18
and so they shifted votes lots of votes
52:22
away from Donald Trump toward Hillary
52:24
Clinton but in a way that people
52:26
couldn't see because the way this works
52:28
is people trust and click on search
52:32
results that are higher in the list so
52:35
50% of all clicks go to the top two
52:37
items in the low why well sure and what
52:42
Google was doing was putting items high
52:44
in the list that led people to webpages
52:48
that looked that made Hillary Clinton
52:51
look a lot better than Donald Trump
52:54
and over time that shifts the opinions
52:57
and votes of undecided voters and of
53:01
course in close elections its undecided
53:03
voters who determine the winner in this
53:05
particular case we calculated based on
53:08
the bias that we found that Google could
53:12
quite easily have shifted two to three
53:15
million votes toward Hillary Clinton
53:17
just using this manipulation without
53:20
anyone knowing that they were doing it
53:23
and they're not that the part of without
53:25
anyone knowing they're doing it is is
53:27
important because this is not just a
53:30
clear bias that stands out it has a name
53:33
they can shift millions of votes using
53:38
what they themselves call ephemeral
53:41
experiences that in other words things
53:43
like news feeds and search suggestions
53:47
and search results answer boxes these
53:51
are ephemeral because they appear only
53:53
for a second or two they affect your
53:56
thinking they disappear they're not
53:57
stored anywhere no one can go back in
54:00
time and retrace them and Google
54:02
employees and you know we've seen in
54:04
leaks recently say they are well aware
54:07
that they can use ephemeral experiences
54:10
to shift votes and opinions and they do
54:13
this deliberately I've proven it with my
54:16
monitoring projects
54:19
so the ephemeral experiences means it's
54:23
just it for an instant it's just there
54:25
your search results something a search
54:27
box your your autocomplete all these
54:30
things are really not trackable by you
54:32
even in your mind because it's the way
54:35
you've done use the Google product for
54:37
ever since you've been using it now
54:39
there was a political article that he
54:43
wrote explaining all this after the
54:46
election which was followed up by a the
54:50
top research scientist at Google search
54:52
in Politico I think with the only audio
54:57
report I could where video report I
54:58
could find out it was from RT and they
55:01
they of course say this is total
55:04
bullcrap the tech jaundiced admit
55:05
dismissed his previous finding saying
55:07
that its algorithms are politically
55:09
blind
55:10
we have never reracked search results on
55:12
any topic including elections to
55:14
manipulate political sentiment moreover
55:17
we do not make any ranking tweaks which
55:19
is specific to elections or political
55:22
candidates period we always strive to
55:24
provide our users with the most accurate
55:27
relevant answers to their queries Google
55:30
completely disagrees with you I should
55:31
say first of all like they said there's
55:33
no way this this can be true but you
55:35
disagree is it really possible the
55:37
results of Google searches can influence
55:39
the way people vote well there's no
55:40
question about that I've been doing
55:42
randomized controlled studies for more
55:46
than six years measuring quite precisely
55:49
the impact that they can have on
55:51
people's thinking and behavior and
55:53
purchases and elections but this
55:56
monitoring project that I conducted that
55:59
should this shows beyond any question
56:01
that there was significant liberal bias
56:05
in google search results but not in
56:08
search results from a Bing and Yahoo
56:11
unfortunately about 90% of the search is
56:15
conducted on Google not big in Yahoo so
56:19
Google really is the deciding factor in
56:23
in close races in fact we calculate that
56:26
upwards of 25% of the national elections
56:29
in the world are being decided
56:32
without people's knowledge by Google's
56:35
search algorithm this is important to
56:38
know and he says they're influenced he's
56:40
not saying that they that is their
56:42
political agenda that they are putting
56:43
in there and if you listen carefully to
56:46
Google's rebuttal of his article they
56:49
say we don't rear ank results that's not
56:53
what his research is calling for he
56:55
actually had something to say about it
56:56
himself that disclaimer that denial that
57:00
you just played from Google we have to
57:02
listen very very very carefully to what
57:05
they're saying they're saying they don't
57:06
rear Inc they're very careful you know
57:09
in their their denials we don't rear a
57:11
cup I've never claimed they rear ank
57:13
anything I'm just recording on what they
57:14
actually show people and what they show
57:17
people is dramatically biased enough and
57:21
our 2018 elections to have shift shifted
57:23
upwards of seventy eight point two
57:26
million votes spread across different
57:28
races in the US and 2018 so they're not
57:33
being completely honest with their
57:35
answer but they're not really lying
57:37
either I don't think their rear anka I
57:39
don't think Sergey Brin is sitting there
57:41
saying oh let's only make let's make
57:42
this the top results take that Trump I
57:46
don't think that's what's happening well
57:48
no I mean they the professor agrees with
57:50
that but the point is is that if you
57:52
said your algorithms up right and you do
57:55
our bias let's face it I mean Sergey and
57:57
then the whole team over there were in
57:59
tears after Hillary lost they'd it's on
58:02
to the Internet the videos are out there
58:04
and they're weeping over this law saying
58:07
we didn't do enough so there's no reason
58:10
to rear ank when the whole thing is is
58:13
rigged and to begin with and and it is
58:16
and I think I can explain what's going
58:17
on first let's go to 2018 he had some
58:21
very surprising results this was our
58:23
most recent election not a presidential
58:25
election and gitmo nation it was for the
58:28
house and for the Senate we did this
58:30
very very carefully we had a field
58:33
agents focusing on three congressional
58:35
races in California which were very
58:38
hotly contested races and Republican
58:41
districts and we gave to these field
58:44
agents about
58:45
500 election related search terms each
58:49
one had different search terms for
58:51
different districts where there are
58:53
different issues of course and the point
58:55
is we we simply looked at what happened
58:59
of search results they received when
59:02
they were conducting election related
59:04
searches and we found very consistently
59:08
that on Google they ended up with search
59:12
results favoring liberals and favoring
59:16
liberal news sources and it was quite a
59:18
dramatic effect and I'm sure that some
59:22
of these House races were won by people
59:26
undecided voters researching receiving
59:28
biased search results so we're going to
59:31
take it as a fact that Google's search
59:34
results political search results have
59:36
been biased and have been left-leaning
59:39
that's just the fact that that's it's
59:41
been replicated there's no disputing
59:43
that but is it the algorithm or is the
59:46
data which one is it can we establish
59:49
any certainty just how much influence
59:51
what people see in their internet
59:54
searches what impact it has upon who
59:57
they vote for well yes that's what the
59:59
the scientific research has been all
1:00:01
about and we know that among people are
1:00:03
undecided on an issue if we show them a
1:00:06
search results that favor one cause or
1:00:10
favor one candidate like brexit for
1:00:12
example among people who are undecided
1:00:15
that easily can shift twenty percent or
1:00:17
more of them in the direction of the
1:00:19
bias upwards of up to 80 percent shifts
1:00:24
in some demographic groups people trust
1:00:26
algorithmic output they trust Google
1:00:29
they think it's because it's generated
1:00:31
by a computer they don't see the human
1:00:34
hand they think it's impartial and
1:00:36
objective and their opinions change so
1:00:38
we've measured that quite precisely now
1:00:40
in five national elections in in
1:00:42
multiple countries and so we know for
1:00:45
sure that that is occurring now around
1:00:48
the world without people's knowledge so
1:00:51
again is it the algorithm that's making
1:00:54
decisions or is the data now we're going
1:00:57
to move to two other scientists
1:00:58
we'll actually we'll start with Cathy
1:01:00
O'Neil she wrote the book weapons of
1:01:02
mass destruction she used to be a hedge
1:01:05
fund quant
1:01:06
which means she would write algorithms
1:01:09
to determine market moves and how you
1:01:12
can take its you know that's a lot of
1:01:13
the flash trading is based on on quanta
1:01:17
quant work
1:01:19
I think Kwan stands for what corner uh I
1:01:21
don't know what it stands for
1:01:22
quantitative analysis ok so a quant is
1:01:26
looking at an algo is programmed to look
1:01:28
for little moves and grab those and it
1:01:30
can be pennies or sometimes fractions
1:01:32
and enough of those over the course of a
1:01:34
day and you're making millions of
1:01:35
dollars and she got bored with that and
1:01:38
she went to go work in the in data
1:01:42
sciences for commercial companies for
1:01:44
insurance companies etc and she
1:01:47
discovered pretty quickly that she was
1:01:49
really separating people into classes
1:01:53
classes of standing at really the
1:01:55
opposite of a lot of what what America
1:01:58
is supposed to be and she did I became
1:02:03
interested in what she was saying
1:02:04
because I you know we've been following
1:02:05
algorithms for a long time on the show
1:02:07
I'm gonna paraphrase her algorithms are
1:02:11
automated opinions of the status quo so
1:02:16
there's an opinion that says anyone
1:02:17
living in this code zip code is
1:02:19
worthwhile to me for my marketing if you
1:02:24
see it on from the zip code taken right
1:02:25
away so that is an opinion and that is
1:02:28
my opinion at this very moment and the
1:02:30
algorithm from now on will make that
1:02:32
decision automatically without any
1:02:35
exception it will not change it and if
1:02:36
that's the only data it has it's only
1:02:38
going to find people in that zip code as
1:02:40
being valid your some of her talking
1:02:43
about this algorithms don't make things
1:02:46
fair if you just lively blindly apply
1:02:49
algorithms they don't make things fair
1:02:50
they repeat our past practices are
1:02:53
patterns they automate the status quo
1:02:56
that would be great if we had a perfect
1:02:58
world but we don't but the data
1:03:01
scientists in those companies are told
1:03:03
to follow the data to focus on accuracy
1:03:07
think about what that means when because
1:03:09
we all have bias it means they could be
1:03:11
codifying
1:03:12
sexism or any other kind of bigotry this
1:03:16
is all from Ted Radio hours I couldn't
1:03:17
get too much about the music they put
1:03:19
under everything and she's very
1:03:23
interesting talk that she has now how
1:03:25
our algorithms program they're
1:03:27
programmed with data and the data
1:03:30
ingress of teaching the algorithm you've
1:03:33
probably heard this term is machine
1:03:35
learning and depending on the data you
1:03:38
have you're going to feed that in you're
1:03:40
going to program the algorithm which is
1:03:42
very simple if this then that it's
1:03:44
really not much more than that it sounds
1:03:46
really really complicated but the
1:03:49
underlying data seems to be a much
1:03:51
bigger issue now we're going to talk to
1:03:53
MIT researcher who graduated for MIT it
1:03:57
you have to kind of get over her valley
1:04:00
kind of girl speak up talking cuz she is
1:04:03
very smart and she has done a lot of
1:04:06
research in this area joy woolum bulam
1:04:11
weenie and she ran into a data machine
1:04:16
learning issue very early on I when it
1:04:19
can she's black when it came to facial
1:04:22
recognition I am the founder of the
1:04:24
algorithmic justice Li so my personal
1:04:27
mission is to fight algorithmic bias yes
1:04:30
the algorithmic Justice League which is
1:04:32
a group of computer scientists and
1:04:34
coders who try to raise awareness about
1:04:36
the social problems that exist in
1:04:38
algorithms if something joy recently
1:04:40
demonstrated by using a basic webcam and
1:04:43
facial analysis technology it's a kind
1:04:46
of technology you might find when you
1:04:48
upload a picture on social media and so
1:04:51
what I do is I sit in front of the
1:04:53
camera hoping for my face to be detected
1:04:56
and I have pretty dark skin so I'm
1:04:59
sitting there with my face dark skin
1:05:01
there's no detection
1:05:03
then I pull on my friends face who has
1:05:06
much lighter skin that I've been ideas
1:05:08
she's Chinese and you see that her face
1:05:10
is immediately detected so then I switch
1:05:14
back to my face dark-skinned and
1:05:16
gorgeous not detected I put on a white
1:05:20
mask and after I put on the white mask
1:05:23
that's when I'm detected and I wanted to
1:05:26
show this as an example that in the same
1:05:29
conditions right a typically lit office
1:05:32
we were having a different experience
1:05:35
you're saying that that a lot of the
1:05:37
software doesn't detect black faces
1:05:40
absolutely this kind of technology is
1:05:42
being built on machine learning
1:05:45
techniques and machine learning
1:05:47
techniques are based on data so if you
1:05:49
have biased data in the input and it's
1:05:51
not addressed you're going to have
1:05:53
biased outcomes so this is a real-world
1:05:58
example you recall that there was a
1:06:00
outrage over google recognizing black
1:06:03
faces tagging them as gorillas I think
1:06:06
it was Google it may have been Facebook
1:06:07
doesn't matter and the reason for this
1:06:10
is when the algorithm was built it was
1:06:13
done with an initial data set that
1:06:15
developers probably a bunch of dudes
1:06:16
probably Asian and white dudes use their
1:06:20
faces to train the algorithm and they
1:06:23
just didn't have any black faces at the
1:06:25
time and lo and behold the machine
1:06:27
didn't know what they were cuz it had
1:06:29
never been given black face algorithmic
1:06:32
data and so it it went to whatever it
1:06:35
thought was the closest thing and sadly
1:06:37
it said oh this is a gorilla so it was
1:06:39
very embarrassing but it's because the
1:06:41
core base even at the development stage
1:06:44
didn't have the right amount of data
1:06:46
here's a fun game to play I want you to
1:06:49
picture John a shoe everyone can play
1:06:51
this at home so think of a shoe don't
1:06:53
tell me what it is just create an image
1:06:55
of your shoe in your mind now we're
1:06:57
going to feed that image into into an
1:07:00
algorithm and say this is a shoe
1:07:02
whenever you see this label this shoe
1:07:05
what image of a shoe did you have in
1:07:07
your mind John I had an image of a
1:07:10
beat-up the kind of flattened leather
1:07:12
shoe with Ruis
1:07:15
some shoestrings right so I had a
1:07:19
sneaker in mind and if you don't put the
1:07:21
sneaker in or a high-heeled or I did
1:07:23
this with Tina yesterday she was
1:07:25
thinking of a sandal I never would have
1:07:26
thought of a sandal so if you don't put
1:07:28
the sandal in it's never going to show
1:07:30
up in the algo as a shoe and this is how
1:07:34
the core data is biased it's not
1:07:36
necessarily left right
1:07:37
you know black/white the Republican
1:07:40
Democrat Democrat it's just missing data
1:07:43
that creates an inherent bias detected
1:07:48
well we have to look at how we give
1:07:51
machine sight computer vision uses
1:07:54
machine learning techniques to do facial
1:07:57
recognition so how this works is you
1:07:59
create a training set with examples of
1:08:01
faces is the face is the face this is
1:08:03
not a face and over time you can teach a
1:08:06
computer how to recognize other faces
1:08:09
however if the training sets aren't
1:08:11
really that diverse any face that
1:08:14
deviates too much from the established
1:08:16
norm will be harder to detect which is
1:08:18
what was happening to me so now let's go
1:08:21
to what's happening with political
1:08:23
questions and results in Google Google
1:08:26
is a left-leaning company it is a
1:08:28
liberal company so their base
1:08:31
algorithmic sets are always going to be
1:08:35
based on their inherent bias and I'll
1:08:37
give you an example if someone asks a
1:08:40
question about Hillary Clinton's emails
1:08:42
they need a result I let me take a look
1:08:46
what we have we have some Wall Street
1:08:47
Journal some Fox News oh here's a New
1:08:50
York Times article that says it was kind
1:08:52
of a big nothing burger it's the New
1:08:54
York Times so yeah I think that's well
1:08:55
put that at the top and we'll put number
1:08:57
two we'll put maybe some maybe something
1:08:59
from The Wall Street Journal little less
1:09:00
nuanced okay now you've just trained the
1:09:03
algorithm based on whatever it's
1:09:06
supposed to be doing that these are the
1:09:08
shoes for that kind of shoe question
1:09:10
take this one step further who is
1:09:13
training the machine learning of Google
1:09:16
is done by my future daughter-in-law we
1:09:20
talked about her sitting here on the
1:09:22
back porch just like tens of thousands
1:09:25
of other people who are reviewing search
1:09:27
results some
1:09:28
it's just the pure search results on
1:09:30
that's from hey Google they do it from
1:09:32
multiple companies then let's say
1:09:34
there's a question about Hillary's
1:09:37
emails that she'll be now let me go take
1:09:39
a look I don't think this answer is
1:09:41
right because it doesn't fit what I
1:09:42
think about it so I think that this
1:09:45
article over here from Politico that's
1:09:47
that's the right answer so I'm going to
1:09:48
tag this it's going to be inherently
1:09:51
biased towards left-leaning results
1:09:54
because of the data because of the
1:09:56
employees because of what Google is this
1:10:00
is not it's far worse than Sergey Brin
1:10:04
sitting there going hey let me change it
1:10:06
no it will never change because the
1:10:09
organization is inherently biased and
1:10:11
the data scientists are not real
1:10:14
scientists they're assholes who are not
1:10:16
checking their data they're not
1:10:18
replicating it checking it for any kind
1:10:20
of fairness bias etc that they're also
1:10:22
probably very left-leaning
1:10:24
and this is a mind-blowing problem above
1:10:28
some unfair shit they're putting in and
1:10:31
that's why they can easily say we're not
1:10:33
doing anything we're not rear Anki no
1:10:34
but all the people you hire who they
1:10:36
don't check they don't check these
1:10:38
people who are who are that being at
1:10:41
task to make sure the results are proper
1:10:44
did I do it just bring something they're
1:10:46
paying him 14 bucks an hour if even that
1:10:49
they don't care these reviewers often
1:10:52
well then you have to explain a couple
1:10:56
of things okay
1:10:57
first of all is no coincident that
1:11:00
coincidence that Google was the largest
1:11:03
contributor to Hillary's campaign and it
1:11:06
would be in their best interests as they
1:11:07
won yes or she won sure secondly how do
1:11:11
you you match up the Google employees
1:11:14
with the Yahoo employees which is what
1:11:16
one of the elements that he compared
1:11:17
with or the Bing employees up at
1:11:20
Microsoft ah the Microsoft employees in
1:11:22
fact I believe are probably much more
1:11:25
liberal I do not bias then they are at
1:11:28
Google I do not believe that they have
1:11:31
the same algorithms and the same data
1:11:34
that they're using google has vast
1:11:36
sources of data that they're using in a
1:11:40
variety of ways that that
1:11:42
Yahoo and Bing have no access to I don't
1:11:44
think that their algorithms are basing
1:11:47
it on thousands of people in fact I've
1:11:49
heard of no one who's reviewing content
1:11:51
choices for being or for Yahoo I don't
1:11:55
hear about that I hear it's for Google
1:11:57
Facebook and Apple and what's not
1:12:01
explained and you're not explaining it
1:12:03
is Yahoo if anyone doesn't know this
1:12:06
uses the Google engine I don't know I
1:12:09
don't know that I don't know what
1:12:11
they're using I don't know if they have
1:12:12
the exact same data see what they're
1:12:15
using is their Dave Dave they're using
1:12:18
the Google engine well whatever that
1:12:20
means you need to show me what that
1:12:22
means
1:12:23
because I don't believe so I don't think
1:12:25
it's the exact same thing Google is
1:12:26
using it just makes no sense why would
1:12:28
they even be contemplating building it's
1:12:31
the exact same thing cuz you're not
1:12:32
getting the exact same exactly but not
1:12:35
the sense the word is that that's what
1:12:38
they're using right it does not that
1:12:40
that by itself does not mean that
1:12:43
they're manually or or overriding the
1:12:47
algorithm because it would have been
1:12:49
what Yahoo output now I don't believe
1:12:51
that for a second this is a much bigger
1:12:54
problem
1:12:55
Google is biased the company is biased
1:12:59
whatever Google is is what they will be
1:13:01
presenting to people
1:13:03
the sad part is as it turns out that
1:13:06
bias swings large percentages of
1:13:09
undetermined voters that's the I mean
1:13:13
the only solution in the near term is to
1:13:15
make google searching illegal for some
1:13:20
period of time because it's it's it's
1:13:23
not stoppable it will not they cannot
1:13:27
stop but it is their system you can go
1:13:29
through it and you can look at every
1:13:31
every piece of the step logic to see
1:13:34
where the results comes there won't be
1:13:36
no injection of oh it's about Trump
1:13:39
let's give him something else no this is
1:13:41
happening throughout the entire system
1:13:43
the whole their whole data structure is
1:13:45
biased you cannot walk away from this
1:13:49
now the only other thing I'd like to say
1:13:52
in this presentation is that sadly this
1:13:55
is not just happening
1:13:56
with our elections this is happening
1:13:58
everywhere in your life and if you don't
1:14:00
know what's going on you're getting
1:14:02
screwed and you're at a disadvantage
1:14:03
back to Cathy O'Neil for a quickie
1:14:05
algorithms that sort through resumes or
1:14:07
algorithms that personality tests are
1:14:09
algorithms that decide who is a good
1:14:11
insurance risk they're very very similar
1:14:13
to in different companies so they're
1:14:15
sorting people in the same kind of way
1:14:17
and if you think about what that doesn't
1:14:20
a society level it's sorting winners and
1:14:22
losers in the standard old-fashioned way
1:14:25
that we've been trying to get over that
1:14:26
we've been trying to transcend through
1:14:28
class or gender through race and it's
1:14:30
against the American Dream you know it
1:14:32
is actually a social mobility problem
1:14:34
and that's what I realized I was like
1:14:36
I'm working on this
1:14:37
I left finance and now what I'm doing is
1:14:40
I'm I'm sort of codifying inequality and
1:14:44
just as a as a as a goof I was looking
1:14:48
around saying looking for search terms
1:14:50
such as how do I beat the the insurance
1:14:54
algorithm and there's a lot of
1:14:57
information people have figured out how
1:14:58
do I circumvent the job site algorithm
1:15:02
your if you put a resume in to a job
1:15:05
site your shit is not even getting to
1:15:07
people you're being pre sorted and let
1:15:10
me give an example one of the data
1:15:12
fields would be where did you hear this
1:15:14
commercial and I heard one just the
1:15:16
other day job site comm slash NPR please
1:15:19
go to job DICOM so job site comm slash
1:15:23
NPR as you know that in many cases the
1:15:27
algorithm will be looking only for those
1:15:29
people whether you listen to NPR or not
1:15:32
they got the code they put that in those
1:15:34
are going to be the smart people there's
1:15:36
the 1i they're the ones I want screw
1:15:38
everything else same with insurance
1:15:40
Colossus which is this big algorithm
1:15:43
that is used for almost all the
1:15:45
insurance companies and you know what it
1:15:47
all starts with you know what the number
1:15:48
one field is in all of these algorithmic
1:15:51
decisions the number one data point for
1:15:54
insurance for a job for I don't have to
1:15:57
tell you all the different things what
1:15:59
is it it's your credit score
1:16:03
another damn algorithmic piece of crap
1:16:06
determining your life so if you're
1:16:09
wondering why you're not getting a job
1:16:10
maybe you're not submitting it right in
1:16:13
fact we need to have our data scientists
1:16:16
our dudes and dudettes named Ben and
1:16:18
Bernadette you need to be whistle
1:16:20
blowing what's the bias within your
1:16:22
company you know it's happening you know
1:16:24
that something's going on you know that
1:16:26
results are being filtered out based
1:16:28
upon X Y or Z we need whistleblowers and
1:16:32
it may not even be malicious but it's
1:16:34
happening your life is being determined
1:16:35
by bad data choices anyone who's a data
1:16:39
scientist should be ashamed of their
1:16:40
field
1:16:45
hey I've said for years the Internet
1:16:47
should have been shut down well it just
1:16:50
really not as exacerbated as it is if
1:16:53
there was no internet it's correct well
1:16:57
I don't want to mention professor Ted
1:17:00
with that I'd like to thank you for your
1:17:02
courage and say in the morning to you
1:17:03
the man who put the C in the carbon
1:17:05
captions John see Dad mister I'm Korean
1:17:10
they're mourning all ships at sea boots
1:17:12
on the ground and subs in the water
1:17:15
there's any out there left and all the
1:17:18
Dames tonight's out there in the morning
1:17:19
to our troll room
1:17:20
hello trolls they had nothing but stupid
1:17:24
comments during that presentation very
1:17:26
demotivating but thank you very much and
1:17:28
no agenda stream com be looking at the
1:17:30
troll room where you're giving a
1:17:31
presentation like that it's interesting
1:17:33
you say that I have noticed I don't
1:17:36
usually by the way push stop you and
1:17:39
destroy room and I would say horror
1:17:41
which is the same way it's just like the
1:17:45
closed captioning I don't know what you
1:17:49
mean by that it's just like an addiction
1:17:51
is something because you did you just
1:17:52
need this these words up there
1:17:54
no actually fuck you know I I was I've
1:17:59
had it written down for a number of
1:18:01
episodes to bring this up there is
1:18:05
it's off to the side it's not my main
1:18:07
view it's always scrolling it's a small
1:18:09
terminal window and I always wonder why
1:18:11
I pick out certain one-liners or certain
1:18:14
things and I believe what is happening
1:18:16
is in my peripheral view I'm seeing this
1:18:20
my brain has developed over the course
1:18:22
of a decade develop some mechanism to
1:18:26
process in the little little side thread
1:18:29
processing what is being said there and
1:18:31
it actually alerts my active speaking
1:18:34
brain to look over there when there's
1:18:35
something that may be of interest that's
1:18:37
what's happening I'm just smart if this
1:18:41
is true I would recommend anybody in
1:18:44
their ol room to start selling atom
1:18:47
stuff okay because this looks like he's
1:18:50
open for subliminal suggestion that's
1:18:52
exactly what it is in the morning - Adam
1:18:55
at sea the artist who brought us the
1:18:57
artwork for episode 11 56 title of that
1:19:01
was pivotal now we chose an evergreen
1:19:03
for this and I got a really pissed-off
1:19:06
bunch of them a bunch of messages since
1:19:11
we artists yes I got it I'll tell you
1:19:13
exactly who it was from
1:19:15
the artist is hold on net Ned and this
1:19:22
was actually my favorite piece which was
1:19:24
the Charlie's Angels as the squad and he
1:19:28
was yelling and screaming and I said
1:19:40
well this is a passionate you know it's
1:19:42
very passionate I said it's interesting
1:19:44
because that was my favorite piece and
1:19:47
as you know what was I agree with that
1:19:48
we both have veto and you said no I've
1:19:51
seen it on the M's m5m news and you
1:19:54
didn't you thought it was you didn't
1:19:56
like it anyway but you said you would
1:19:58
seen it somewhere else and so you vetoed
1:20:00
it and you know I was like okay I
1:20:02
haven't seen that specific piece but I
1:20:04
saw that the reference to Charlie the
1:20:06
reference yeah the reference well he was
1:20:08
very corny to be honest about it well
1:20:15
just letting them know what the process
1:20:16
is so at least I also don't like the
1:20:18
idea of a quaity
1:20:20
here's here's my final rationalities
1:20:22
interested because we do have to
1:20:24
rationalize what we pick and the two of
1:20:27
us being the art critics that we are I
1:20:30
didn't think it was it was correct to
1:20:34
glorify this group of four in that way I
1:20:37
thought it was a glorification piece and
1:20:39
I didn't think it was appropriate well
1:20:41
you didn't say that as such but did you
1:20:44
I did I remember said I did I thought
1:20:46
you said you didn't like it for a number
1:20:48
of reasons and maybe you said course one
1:20:50
of the reasons were already agree you're
1:20:53
already looking at evergreens by the
1:20:54
time I came up with that it was a
1:20:56
glorification piece that was unnecessary
1:20:58
and improper
1:20:59
so Jimmy NAND and we look at these
1:21:01
things whenever you won't want to put a
1:21:03
person in the artwork we're gonna be
1:21:05
hypercritical so you know having a Oh
1:21:07
see it may be a funny joke to put a
1:21:10
little Mensa label there but that's not
1:21:13
artwork that I want to click on I know I
1:21:15
click on her and this the whole idea is
1:21:17
to get someone to click on it so when
1:21:19
you when you vilify people you know
1:21:22
there's a really a 50/50 chance it's not
1:21:24
even you know it's just the way it is
1:21:26
yeah you're asking for trouble well it
1:21:29
could be you make us laugh out loud I
1:21:31
mean there was the one instance I still
1:21:33
remembers to George Bush I think this
1:21:35
was done by Nick or or Martin JJ one of
1:21:38
them where George Bush had those big
1:21:42
coke bottle glasses on and the huge
1:21:44
eyeballs and that was all the art was
1:21:46
yeah but when you looked at it you
1:21:49
cracked up right now so we picked it
1:21:52
that's the one that got economic strip
1:21:54
bloggers all bent out of shape right
1:21:56
also a lot of if you there's a lot of
1:21:59
kind of like mainstream joke we have to
1:22:01
manage artists imagine this nightmare
1:22:04
hey that's your beat as far as I'm
1:22:07
concerned the artist you deserve always
1:22:10
been a nightmare for anybody who's worth
1:22:12
in the you know as an art director well
1:22:14
thank you very much Adam at sea and all
1:22:16
of the artists who always participate or
1:22:19
not it's okay it's totally understood I
1:22:22
get it it's a you spend a lot of time
1:22:24
you don't get chosen it sucks no agenda
1:22:26
art generator
1:22:27
that piece with the Charlie's Angels
1:22:29
thing was there was some effort put into
1:22:32
it it wasn't like it boss away no it
1:22:33
wasn't at all but that's why it was act
1:22:35
of course he was act yeah I just wanted
1:22:39
to make sure voice was hurt anyway I
1:22:41
made sure that he hates you now and not
1:22:43
both of us or means yeah yeah I'm the
1:22:45
I'm I'm the one responsible for veto you
1:22:48
need that that's another that particular
1:22:50
piece yeah
1:22:50
anyway we appreciate the work that all
1:22:53
of our artists do and we also really
1:22:55
survived by our executive producers
1:22:57
associate executive producers and all
1:22:58
producers who support us financially and
1:23:01
we like thanking those the top category
1:23:03
as quickly as we can't even though our
1:23:05
little behind today in the show so let's
1:23:07
see who we have on today's list well the
1:23:11
top of the list is our buddy sir on Imus
1:23:12
of Dogpatch and Louis oblivia this is
1:23:15
monthly yeah I believe so this is uh you
1:23:18
know it varies it never is never the
1:23:20
same nine hundred dollars is some sort
1:23:21
of code we're gonna figure this out one
1:23:24
of these days we know it's a long arc so
1:23:27
it's gonna maybe for code for somebody
1:23:29
else thank you to all the producers from
1:23:33
their continued support of this show I
1:23:35
would also like to thank your respective
1:23:37
spouses for their support and tolerance
1:23:40
of your work demands this year alone you
1:23:42
have worked on national holidays on
1:23:44
vacations and while traveling right Mimi
1:23:48
and Tina thank you both for your sharing
1:23:50
your spouse's the for the sharing of
1:23:52
your spouse's and what would otherwise
1:23:54
in what would otherwise be your time
1:23:58
personal experience having a spouse that
1:24:00
is most socially and politically engaged
1:24:02
provides insight you cannot achieve
1:24:04
independently non donors attention
1:24:08
please send some money or the show goes
1:24:11
the way of Alfred Newman it may take 30
1:24:15
years but one day the growing number and
1:24:19
sophistication of moneyed organizations
1:24:21
using all media outlets this way opinion
1:24:23
is lucrative to the creators of the
1:24:25
propaganda and we need to support those
1:24:28
that help us see through their agenda
1:24:31
with no agenda no previous wars were
1:24:35
over physical territory now advertisers
1:24:39
of all kinds including ideologues use
1:24:41
m5m and the social media wars to win the
1:24:44
territory of your mind using the old
1:24:47
saying generals always fight the last
1:24:49
war so politicians as social media fight
1:24:52
the last internet war it is the war of
1:24:55
ideas with growing expertise to win more
1:24:57
Minds giving the power to influence
1:24:59
culture society and politics it is more
1:25:02
infectious than measles
1:25:04
deconstruction is the best vaccine well
1:25:07
not as financially lucrative it
1:25:09
vaccinates the participants again
1:25:12
listeners donate wow there's all victims
1:25:15
of our own life experiences thank you
1:25:17
for sharing yours Wow sir on a massive
1:25:20
lowers LeBeau via of Dogpatch and louis
1:25:22
LeBeau via thank you that that's one of
1:25:24
his most succinct calls to action I've
1:25:27
heard yeah it may be reproducible and
1:25:29
and I think we kind of proved that in
1:25:32
our our search engine bias
1:25:36
conversation it is a war and you're
1:25:38
losing we're all losing we are losing
1:25:42
losing big time he never asked for it oh
1:25:47
you never does give him one yet yeah I
1:25:49
think that would be appropriate
1:25:51
you've got karma not one one he doesn't
1:25:59
have to take it
1:26:00
Ernest Selma summons Selman yeah Selman
1:26:06
ze OMI Ennis 33333 jobs Karma's
1:26:10
appreciated jobs jobs jobs and jobs
1:26:14
let's vote for job
1:26:17
you thought karma yeah yeah it looks
1:26:27
right I think it's Estonia yeah okay
1:26:31
number dumb man check it out somebody
1:26:33
out there top-level domain s EE Estonia
1:26:36
uh 333 am came in with three and of
1:26:42
$33.33 and his name is 333 3 3 hours and
1:26:49
33 minutes am I would like to keep my
1:26:52
name and location anonymous okay but
1:26:54
feel free to call me 333 a.m. last year
1:26:57
when I donate I asked for some
1:26:58
baby-making karma karma were works by
1:27:02
incoming the interest of helping others
1:27:04
who are struggling to conceive I'll
1:27:06
share what we did first this is step by
1:27:10
step should I be taking notes here or
1:27:17
donate to knowledge any it's all it
1:27:22
always helps can't hurt next as John and
1:27:25
Adam can no doubt attest all women are
1:27:28
different
1:27:28
but medicine tends to assume all women
1:27:31
are the same at least in reproduction
1:27:34
terms my wife started using the Crichton
1:27:37
model fertility care system which tracks
1:27:39
your normal biological markers the goal
1:27:41
of this system is to know when
1:27:42
baby-making is most likely and to see if
1:27:45
there are other issues from this
1:27:47
tracking we discovered my wife had
1:27:49
luteal phase defect and this was
1:27:52
confirmed by blood tests while not
1:27:54
uncommon some women have short cycles
1:27:56
and low progesterone for those that
1:27:59
don't know progesterone is the chemical
1:28:02
in the body that maintains the pregnancy
1:28:04
after it's started it's also used as a
1:28:06
birth control mechanism so between Cara
1:28:09
the Crichton model to know when
1:28:11
baby-making is most likely to be
1:28:13
successful and progesterone supplements
1:28:15
to keep the pregnancy going
1:28:17
we are expecting a child at the end of
1:28:19
this year all right
1:28:20
hashtag winning this has been a long
1:28:23
time coming and if I can help others
1:28:25
struggling to have a child and I hope
1:28:27
this note does that I want to say it was
1:28:30
great meeting at him in the
1:28:31
at the Des Moines Iowa Meetup four clips
1:28:34
I would like it it's true respect and at
1:28:38
the end of the day some triple or
1:28:41
quadruple strengths relationship karma
1:28:44
if you got it for Scott of the tall corn
1:28:47
who hit me in the mouth and who has been
1:28:49
on a long dry spell if you know what I
1:28:51
mean thanks for all you do 3:33 a.m.
1:28:54
yeah and I don't want to mention
1:28:58
something last night the keeper and I
1:29:00
went to Celeste Barbour she had a show
1:29:03
here in Austin at the Paramount that was
1:29:07
pretty packed you've probably never
1:29:09
heard of Celeste Barbour I wouldn't have
1:29:11
heard of her either nor would Tina she
1:29:14
has something on Instagram which I don't
1:29:16
look at but Tina follows her which is
1:29:19
the Celeste barber challenge and she's
1:29:22
from Australia and she's a good-looking
1:29:24
a woman but she's not not a model if I
1:29:28
can if that explains what I'm talking
1:29:29
about and so you know a model will be
1:29:32
doing something gracious you know
1:29:34
getting out of a Kate Upton actually
1:29:36
slipping out of a pool with her breasts
1:29:38
protruding and the water just slipping
1:29:41
off or svelte body and then she'll post
1:29:43
a picture of that and have her go down
1:29:45
of her doing the same thing which of
1:29:47
course is hilarious and so this was
1:29:51
really a comedy show probably more for
1:29:53
women than anything but I learned a lot
1:29:57
of things about giving birth and being
1:30:00
pregnant that I never knew about and if
1:30:04
your wife is expecting see if you can
1:30:06
catch a Celeste Barbour show you will be
1:30:08
both horrified and informed there's a
1:30:11
lot of things we as guys don't know and
1:30:13
it's probably it might have been better
1:30:15
we didn't yeah keep it that way
1:30:23
our ESP ICT but at the end of the day
1:30:27
they're backing them you know the
1:30:28
backing come on at the end of the day at
1:30:32
the end of the day John if someone wants
1:30:34
to get anyone they can get him you've
1:30:38
got karma on word to associate executive
1:30:45
producers starting with Abel Kirby uh
1:30:49
Abel car bak shoutouts to local seven
1:30:53
one nine four a great meetup Colorado
1:30:55
mr. Gillis Department of Transportation
1:30:58
for allowing the negligent building
1:31:01
frightened right negligent building
1:31:03
practices resulted in a collapsed
1:31:05
section of a highway on us 36 no sewer
1:31:09
chat but I'm not sure why let's Nick the
1:31:11
rat sewerage everybody loves a little
1:31:16
sewer chat about some Karma for that
1:31:18
you've got karma in Toronto we've got
1:31:24
Carolyn Blaney Dame Carolyn she's gonna
1:31:28
be Dame anyway Toronto two or three
1:31:31
thirty three and she said got her Dame
1:31:33
hood coming up please say hog story
1:31:36
mofos and play the reverend al resist we
1:31:40
much parallel universe jingle and a full
1:31:42
version of we don't there is no parallel
1:31:45
universe jingle we have the transition
1:31:47
machine but then we have to go into the
1:31:50
parallel universe which it's not really
1:31:52
a jingle that may not be usable and the
1:31:55
full version of slash and well we could
1:31:57
do that the end what is what is slash
1:31:59
and what is slash in I don't know anyway
1:32:03
whatever it is that she's confusing us
1:32:05
with this donation brings her to Dame
1:32:07
hood she needs some new stuff you get
1:32:12
your pencil out she requests extra
1:32:13
coffee and a hot sauce at the roundtable
1:32:16
okay god it's in Carolyn of Hogtown yes
1:32:21
do we get these Toronto Dame's you know
1:32:24
a couple of them now
1:32:26
fun fact Toronto is often referred to as
1:32:29
hog town in the 1890s that's what she
1:32:31
must be named Carolyn of hog town
1:32:33
because the Meatpacking was one of the
1:32:35
city's main
1:32:36
industries at the time the Chicago like
1:32:38
that's interesting because that was
1:32:39
Chicago's main industry for years in
1:32:44
fact when I was a little kid I remember
1:32:45
this they were still major Meatpacking
1:32:48
town I went in on the California Zephyr
1:32:50
and you go right through these giant
1:32:53
herds of animals so what's good this
1:32:56
slash and full jingle of Dvorak dot org
1:32:59
slash and a maybe no no no slash and it
1:33:03
says specifically /e and i haven't i've
1:33:06
no idea
1:33:07
hmm well why don't we do a karma will do
1:33:11
the will do a sharpen resist and maybe
1:33:13
someone can let me know what she's
1:33:14
talking abut exists we much we must and
1:33:18
we will much about that you've got karma
1:33:27
[Music]
1:33:37
two hundred dollars and two cents great
1:33:40
show last thursday Jon and Adam love
1:33:42
them Thursday's keep it short
1:33:44
I'm beginning becoming a night today
1:33:46
this is cool I will tell everyone in
1:33:49
Spanish if I can request a jingle please
1:33:51
have Bill Clinton who can do the most
1:33:54
again well what is that I don't recall
1:33:57
any jingle who can do the most it says
1:34:01
an oldie yeah well I don't know a
1:34:03
Hillary barking you got the hill I got
1:34:05
the barking yeah sure it's too hard to
1:34:09
pull which it is I'll settle for John
1:34:12
doing the foamer That's not me thanks
1:34:16
John for the night table if any left pot
1:34:19
and vinegar for the nights can you put
1:34:21
pot and vinegar on the list the pot and
1:34:22
vinegar added to the list for the
1:34:25
knights and dames out there i would like
1:34:27
to be known as sir zeke the EEC night to
1:34:32
late night vic night sir via velcome o
1:34:38
servic nights i got it
1:34:41
loving light and such pew pew that's
1:34:44
true that's oh he wants peace is it does
1:34:47
he want those jingles
1:34:49
I mean please if you're gonna do jingles
1:34:52
then give us the list and grousing it
1:35:00
the guys at night you should be nice I
1:35:01
grouse I grouse before he becomes a
1:35:04
night I just were you know what is I I
1:35:06
try to prepare these things and have
1:35:07
everything ready and then I get all
1:35:09
these curveballs
1:35:23
you did it fine and that would be our
1:35:26
last of our associate executive
1:35:28
producers for show 1157 Heinz 57 we got
1:35:33
Wow do we get an extra little thing in
1:35:37
our paycheck for that one so they want
1:35:48
to thank all these folks it makes this
1:35:49
show possible run uh especially on
1:35:52
Sundays or donations are not usually
1:35:54
that great and these are official
1:35:56
credits you producers an executive or
1:35:59
executive producers and associate
1:36:00
executive producers these things are
1:36:03
valuable I know
1:36:04
Cerrano miss of Dogpatch may not use
1:36:06
them for whatever reasons he has but
1:36:09
everyone else I'd suggest you put them
1:36:10
on your Linkedin you can put them
1:36:12
anywhere where it apparently it trips
1:36:15
algos when you're looking for a job
1:36:16
there you go support us for our thursday
1:36:19
show go to to Vollrath org slash and
1:36:26
[Music]
1:36:28
our formula is this we go out we hit
1:36:32
people in the mouth
1:36:34
[Music]
1:36:41
[Music]
1:36:48
Kouga so I've been putting a lot of
1:36:51
notes on Twitter demanding that they
1:36:53
start putting a Hillary on these polls
1:36:55
yes
1:36:57
I've you are now in the Democrat Hall of
1:36:59
Fame I believe that people really love
1:37:01
you trying to bring her in I think she
1:37:05
knows no gurus they don't want her
1:37:09
that's probably true maybe it's not true
1:37:11
all I know is that if you're gonna start
1:37:13
doing these comparisons about who can be
1:37:15
trumpeter who's gonna get the most votes
1:37:17
or who can get the nomination why isn't
1:37:19
she on there just as a test I'm with I
1:37:22
didn't was on there before he ever
1:37:23
declared to run yeah I'm with you I'm
1:37:26
with you okay so the reason would be
1:37:30
they hate her that even with Google's
1:37:34
biased algorithm in her favor she's
1:37:36
still lost would these be the reasons
1:37:40
that they don't want it happened
1:37:42
somewhere you know here's what my
1:37:46
thinking is cuz I've been trying to
1:37:47
figure this out myself I think that
1:37:50
Hillary was not a good candidate because
1:37:53
she really didn't she she just thought
1:37:56
she was gonna waltz in and she did they
1:37:59
no effort she had her same cronies
1:38:02
running the campaign that they were
1:38:03
doing that the loss to Obama they're
1:38:06
lazy campaigners and they didn't really
1:38:09
do a very good job of it because she cuz
1:38:12
they all were taught themselves into
1:38:14
believing like the Democrats do they
1:38:16
talked themselves into believing that
1:38:18
they're gonna run against some bonehead
1:38:19
this Trump guys joke in fact they wanted
1:38:22
him why a candidate they wanted him to
1:38:24
be candidates like oh please would be so
1:38:26
great we need him to be kidding the
1:38:29
media was all in on that so they kept
1:38:31
and I'll say it again according to
1:38:35
reports because I don't know the media
1:38:37
won't cover Bernie is that I less likely
1:38:41
to blame Schultz for the screw-up with
1:38:44
Bernie they're you know putting Bernie
1:38:46
on the side then I am the media the
1:38:48
media would cover every giant Trump
1:38:51
rally there were 25,000 people at a
1:38:53
trump rally let's say on average and
1:38:55
there was 50 people a hundred people 200
1:38:58
people at a Hillary rally and 25,000
1:39:01
or more at a Bernie rally they would not
1:39:04
cover the Bernie rallies no and they're
1:39:07
still not covering I understand I not
1:39:09
don't know for a fact because the media
1:39:12
is not covering it but I heard there was
1:39:14
a one Bernie rally recently with 50,000
1:39:16
people attending I wouldn't surprise me
1:39:18
this is this has been very consistent
1:39:20
with his campaigning but of course he's
1:39:22
not really a Democrat and they all know
1:39:24
it that's why they like he says he just
1:39:26
leeching off our ticket like a good
1:39:28
socialist would do there's an element of
1:39:31
that he's not a Democrat as an
1:39:33
independent and so now he corns in at
1:39:36
the end I can see them not wanting him
1:39:38
you know the guy's not even a Democrat
1:39:39
he's an independent just using us you
1:39:42
know using us he's a huge loser
1:39:44
socialist user socialist user has yeah
1:39:49
that's what he is
1:39:50
that's what he is okay so I just want to
1:39:54
see I just want just put Hillary on a
1:39:57
pole I mean you know double-l not and
1:40:02
not Elly yeah I love the story that
1:40:10
Bernie's campaign workers you know who
1:40:14
have a union yeah are thinking of
1:40:17
striking because he's not paying him $15
1:40:20
an hour which is what he's the very
1:40:22
proposal he's making for the rest of the
1:40:25
country yes oh it's bent I thought for
1:40:28
sure you would have had something about
1:40:29
that I miss oh this is great yeah
1:40:32
they're all great we work in 60 or 70
1:40:35
hours and we're making the equivalent of
1:40:37
about 1350 so you got it up it for us
1:40:41
you know we need to have 15 out of 15
1:40:43
dollars an hour this is what you're
1:40:44
proposing here labor fight Royals Bernie
1:40:47
Sanders campaign as workers demand the
1:40:50
$15 hourly pay the candidate has
1:40:52
proposed for employees nationwide no
1:40:58
Bernie Bernie when they busted him last
1:41:00
go-around when he was like there was
1:41:02
some sexism or some crap in the campaign
1:41:05
they throw it at him at the end I said
1:41:07
what word about this and what about that
1:41:08
Bernie's really good at deflecting this
1:41:11
stuff will come out and say I don't know
1:41:13
about
1:41:14
big stink about it and any of you look
1:41:19
into it may I just say you're Bernie's
1:41:23
pretty good to get in there Horowitz has
1:41:26
got it nailed interesting so see if I
1:41:34
Hey all right we got a few things going
1:41:35
there say yeah I got a weird gaff the
1:41:37
Judi had a gaff on PBS that was kind of
1:41:41
odd okay can we go straight to it yeah
1:41:45
and that's on healthcare Bernie Sanders
1:41:47
is out there saying let's move to single
1:41:50
payer doubling down on that saying
1:41:52
that's the way we take care of all
1:41:53
Americans but you have Al Gore doubling
1:41:56
down on Obamacare what does this add up
1:41:59
to yeah I mean the the Joe Biden was
1:42:03
said Al Gore hey overshadow but not that
1:42:10
much yeah Joe Joe Biden hmm what was
1:42:13
going on in her head I mean you can make
1:42:18
all kinds of different mistakes but Al
1:42:20
Gore she was probably first thinking of
1:42:24
VP you know cuz Biden was vice president
1:42:27
and then she probably thought of some
1:42:30
dick oh yeah it must have been a you
1:42:34
know I don't know something weird kind
1:42:41
of odd it's very odd well at least
1:42:43
someone caught or called her out on it
1:42:45
gee that doesn't even happen that often
1:42:47
anymore no usually they just keeps going
1:42:50
now there's the story that was under
1:42:52
Mockridge that worked I've said this
1:42:54
before democracy now is not a great news
1:42:57
outlet but but occasionally they do
1:43:02
stories that nobody else touches yeah I
1:43:05
agree with that that doesn't mean that
1:43:07
they're good or they're correct yeah
1:43:08
they do them but they at least I mean
1:43:11
and that you have to give them credit
1:43:12
for in fact that's one of the reasons
1:43:13
I'm always compelled to listen to the
1:43:15
show a lot of its eye rolling a lot of
1:43:18
it that they do they do the Association
1:43:20
stories where they talk about one thing
1:43:21
and switch to another to make you
1:43:22
associate I don't like that I have an
1:43:24
example of that today okay
1:43:26
but there's this one and to be honest
1:43:29
about it
1:43:30
I don't remember the story at all and
1:43:33
this is the story about Katherine gun
1:43:35
and only democracy now covered it it was
1:43:39
that she was a wish whistleblower for
1:43:42
GCHQ the British blew the whistle on the
1:43:46
first go-around did the British
1:43:48
intelligence
1:43:49
yeah they stands for government
1:43:52
communications headquarters and it's a
1:43:56
it's the NSA NSA of England and she was
1:44:04
a worker there and she blew the whistle
1:44:06
on some bullcrap that was going on some
1:44:09
blackmail which is what we'd say these
1:44:11
agencies are good for that was gonna
1:44:14
happen and she and this is before the
1:44:16
Iraq war came up with the idea well met
1:44:19
weapons of mass destruction they were
1:44:20
going to go in there first and they were
1:44:21
going to use these tricks a trick to get
1:44:23
in there without having to bullcrap the
1:44:26
public with their weapons of mass
1:44:27
destruction and so her story is just
1:44:32
discussed on Democracy Now cuz the other
1:44:34
ones have covered it and so let's listen
1:44:35
to this is a long clip is two minutes
1:44:37
low over two minutes
1:44:38
Katherine guns story DN as the British
1:44:41
government says it's identified the
1:44:43
person who leaked cables that forced out
1:44:45
the British ambassador to the United
1:44:46
States for calling President Trump inept
1:44:49
we look at the real-life political
1:44:51
thriller of a British intelligence
1:44:53
specialist who risked everything to blow
1:44:56
the whistle on us dirty strict on us
1:45:00
dirty tricks at the United Nations in
1:45:02
the lead up to the Iraq invasion in 2003
1:45:05
Katherine Gunn was working for Britain's
1:45:07
government communications headquarters
1:45:08
known as GCHQ the Intelligence Agency
1:45:12
similar to the National Security Agency
1:45:14
here when she opened a top secret NSA
1:45:17
memorandum the highly confidential memo
1:45:19
revealed the United States was
1:45:21
collaborating with Britain and
1:45:22
collecting sensitive information on
1:45:24
United Nations Security Council members
1:45:26
in order to pressure them into
1:45:28
supporting the Iraq invasion guided by
1:45:30
her conscience Katherine Gunn defied her
1:45:33
government and leak the memo to the
1:45:35
press setting off a chain of events that
1:45:37
jeopardized her freedom her
1:45:39
but also open the door to putting the
1:45:42
entire Iraq invasion on trial the claim
1:45:45
Pentagon Papers whistleblower Daniel
1:45:47
Ellsberg described Katherine guns action
1:45:49
is the most important and courageous
1:45:50
leak I have ever seen Dan Ellsberg said
1:45:54
no one else including myself has ever
1:45:55
done what Katherine Gunn did tale secret
1:45:57
truths a personal risk before in a
1:45:59
minute war in time possibly to avert it
1:46:02
well now Catherine Gunn's story is being
1:46:04
told in the new film Official Secrets
1:46:07
starring Keira Knightley at the time her
1:46:10
actions received very little attention
1:46:12
from journalists in the United States
1:46:14
unless Democracy Now
1:46:17
in 2004 Democracy Now interviewed
1:46:20
Catherine Gunn I asked her why she
1:46:23
decided to leak the memo when I saw this
1:46:27
email asking excuse helped to bug these
1:46:32
three nations to get a vote for the war
1:46:37
with Iraq I was very angry at first and
1:46:40
very saddened that it had come to this
1:46:42
and that despite all the talk from both
1:46:48
Tony Blair and George Bush about how
1:46:51
important it was to get the UN on on
1:46:54
board and legitimize any kind of
1:46:58
aggression that they were actually going
1:47:00
around it in such low handed manner so I
1:47:05
decided that the risk to my career was
1:47:10
minut compared to the coming war in Iraq
1:47:14
just for context the six nations that
1:47:17
the next clip okay then I'll be quiet
1:47:21
now the reason that the next clip is
1:47:24
they actually brought her on again she's
1:47:26
on the show now and they're asking her
1:47:29
about the six nations in this and they
1:47:30
have the director of the movie there and
1:47:32
a whole bunch of other people it's
1:47:33
actually a big group and she's there and
1:47:35
a couple of things I found it
1:47:37
fascinating which was one is that and
1:47:39
we've discussed this on the show and
1:47:43
this is discussed and a couple of new
1:47:45
items being developed whereby it turns
1:47:49
out that
1:47:51
the CIA has at least one or maybe two
1:47:55
people in every major news outlet in the
1:47:58
country and they put a stop to this sort
1:48:02
of thing so that's why the story was
1:48:03
never discussed in the American media it
1:48:06
just was killed he's bragging about it
1:48:10
democracy now did it because democracy
1:48:11
now if they have a spook in there is a
1:48:14
me yeah because I don't know anybody so
1:48:21
they have and they figure well we can't
1:48:24
get a spook in there we've had
1:48:25
situations where we know people that
1:48:31
have worked at certain organizations
1:48:33
that have discussed with us people that
1:48:37
are there that don't make that why are
1:48:38
they there or what are they doing there
1:48:40
what and they keep screwing with us
1:48:42
we're talking about within the NPR world
1:48:44
and every place is read me vo we
1:48:46
identified one working there we believe
1:48:49
and they're all over the place and I
1:48:51
always like to spot him and this is our
1:48:53
spot to spook game oh is that where
1:48:55
you're going this okay so we go you have
1:48:58
all these I just wanted to mention this
1:49:00
because people should know that the it's
1:49:03
really difficult on the No Agenda show
1:49:06
for anyone to get in to be a spook
1:49:09
because this is just a small operation
1:49:12
list I know I'm not a spook I know I'm
1:49:15
not a spook he knows he's done so
1:49:17
there's nobody that's why our show tells
1:49:20
stories about you know it's not like
1:49:22
it's not like within a giant
1:49:24
organization where there's a spook or
1:49:25
two that will quash store now you know
1:49:28
guys just the people aren't interested
1:49:30
in that they're not interested in that
1:49:31
once you talk about do we had a better
1:49:33
story than that so you like you know if
1:49:36
you don't do that story would you like
1:49:37
us to write a book for you and put your
1:49:39
name on it yeah exactly and you can go
1:49:41
on CNN
1:49:45
brought her on and they asked her about
1:49:47
the about the about the countries and
1:49:50
the she gives she won't she doesn't give
1:49:52
the laundry list but directed us and
1:49:54
then they imply it was all it was all
1:49:56
part of a blackmailing operation but
1:49:58
what did you see in your email well it
1:50:02
was a memo from a chap called frank cosa
1:50:05
who worked at the institute and i was
1:50:10
just a request from the NSA to for GCHQ
1:50:14
to assist them in bugging the domestic
1:50:16
and office communications of the six UN
1:50:19
Security Council delegates wait a second
1:50:22
in bugging in spying on in eavesdropping
1:50:25
wiretapping whatever yeah and who were
1:50:28
these six countries yes Angola Cameroon
1:50:37
Bulgaria um Chile Pakistan and Mexico
1:50:43
yeah and and six countries were the
1:50:46
non-permanent members on the UN Security
1:50:49
Council at the time and the idea was
1:50:51
they would figure out which way they
1:50:53
were going to vote so that they could
1:50:54
sway them well normal that idea was to
1:50:57
gather information that they could use
1:50:59
to bribe them or you know I'm threaten
1:51:03
them into voting YES for the resolution
1:51:05
the term is blackmail I don't know why
1:51:08
she didn't use it the term is blackmail
1:51:12
and that's what is going on with all
1:51:14
this snooping and this is another
1:51:16
message that people need to understand
1:51:17
when you say well I don't care if they
1:51:20
listen in on what I'm doing cuz I'm not
1:51:22
doing anything wrong
1:51:24
that's not the point no it's not you
1:51:27
that's doing anything wrong it's
1:51:29
somebody else who can be blackmailed and
1:51:31
they would maybe vote against your
1:51:34
interests yes and that's the problem
1:51:37
with that's why privacy has to be a big
1:51:40
deal it's not because oh I'm not doing
1:51:42
anything wrong I don't care if they're
1:51:44
listening in now there's no such thing
1:51:46
as privacy you could have stopped a
1:51:48
horrible war which hundreds of thousands
1:51:51
of people were killed in but now
1:51:54
you didn't care about your privacy now
1:51:58
of course this ended up being they did
1:52:00
screw up the war's beginning so it had
1:52:03
to come up with the yellowcake and all
1:52:05
the other crap looming him to aluminum
1:52:09
I'm looking at the at the Wikipedia
1:52:13
entry for this story of Katherine gun
1:52:14
and it says Angola Bulgaria Cameroon
1:52:18
she'll a Pakistan but not Mexico it says
1:52:21
Guinea this guy said Mexico Mexico
1:52:24
Mexico Mexico is not in Wikipedia its
1:52:28
Guinea he said Mexico it's not in the
1:52:30
wiki end on the we have to look at who
1:52:32
was on the Security Council at the times
1:52:34
there's some as this brought urge that
1:52:36
tomorrow lots of people but who was the
1:52:38
swing it doesn't really matter I'm just
1:52:40
saying let's go out for a reason again
1:52:43
yeah Wikipedia had very trustworthy over
1:52:47
there at the Wikipedia with grey hair
1:52:49
blue blue this he's got a nice kind of a
1:52:52
Brooks Brothers jacket a blue shirt or
1:52:56
light blue shirt or even a white shirt a
1:52:58
tie kind of a regimental tie nothing
1:53:00
fancy use the white hair for some
1:53:03
unknown reason and that guy is the guy
1:53:08
who probably put a court cross that
1:53:21
unites arguing against it my theory oh
1:53:24
how could I absolutely right it's just
1:53:28
it's just sad how you made a good point
1:53:31
there it's like it is important what
1:53:33
people can hear what you're doing or see
1:53:34
what you're doing it is important it
1:53:36
does make a difference unbelievable well
1:53:41
that's me that's pretty good so they're
1:53:43
bringing her back and what was kind of
1:53:45
the whole point of this revisit this
1:53:49
really ties into the guy who got busted
1:53:51
that you know the British ambassador to
1:53:55
the US who said Trump was an idiot we
1:53:58
talked about this in the last show of
1:53:59
the show before this good character and
1:54:02
I guess you know this is just democracy
1:54:04
not trying to keep the flames bubbling
1:54:07
about so they can keep saying well he
1:54:09
said the Brett and Bowser said Trump was
1:54:11
inept I see I see what you're saying
1:54:16
okay I make sense I mean there's a lot
1:54:19
of these I don't know if you call it
1:54:20
conflating I think there's another
1:54:22
example in here where they're talking
1:54:25
about one thing and she has to throw
1:54:26
something else in she does this constant
1:54:29
here's here's a me changing the subject
1:54:32
I'm sure she did that expertly
1:54:35
Environmental Protection Agency
1:54:36
announced Thursday it will not ban the
1:54:39
widely used pesticide chlorpyrifos even
1:54:42
though the agency's own research shows
1:54:45
it can cause brain damage in children
1:54:48
the Obama administration said it would
1:54:50
ban the use of the toxic chemical in
1:54:52
2015 but the rule never took effect and
1:54:54
was suspended in 2017 by then EPA had
1:54:58
Scott Pruett this comes as the Trump
1:55:00
administration is preparing to roll back
1:55:02
government regulations on nuclear power
1:55:04
plants with staffers at the Nuclear
1:55:07
Regulatory Commission recommending
1:55:09
allowing the nuclear industry to carry
1:55:11
out more self inspections while slashing
1:55:14
the size and scope of radiation
1:55:15
protection and emergency preparedness
1:55:18
inspections and nuclear plants well very
1:55:21
a very slick got to do it the other a
1:55:23
very slick transit transition into
1:55:26
something I didn't really even hear
1:55:28
anywhere so while completely unrelated
1:55:31
I'd like that news at the end it was
1:55:34
interesting yeah taking a bit back by
1:55:38
but I've been hearing these these these
1:55:40
kind of stories like this where you talk
1:55:42
about one thing and somehow get it into
1:55:44
another topics we make a transition they
1:55:46
want the other example that is this one
1:55:48
you can't climate change transitions to
1:55:52
jew-hatred noise so why did the old
1:55:55
party environment mental committee
1:55:57
accuse the government of coasting on
1:55:59
climate change
1:56:01
I just wage or something here does it go
1:56:04
from denial from climate denial to
1:56:07
Holocaust denial oh it's a different
1:56:10
kind of transit oh okay well the pay
1:56:12
attention everybody so why did the all
1:56:14
party environment mental committee
1:56:16
accused the government of coasting on
1:56:18
climate change the government has a fine
1:56:22
record on climate change including our
1:56:25
[Applause]
1:56:27
emissions but then the Prime Minister
1:56:30
changed tack this morning more than 60
1:56:33
Labour members of the Lord's put their
1:56:35
names to an advert in The Guardian
1:56:37
newspaper they accused Jeremy Corbyn of
1:56:39
failing to tackle anti-semitism and said
1:56:42
he'd allowed a toxic culture to grow in
1:56:45
the labour party before the right
1:56:46
honourable gentleman stands up and
1:56:48
parades himself as the champion of
1:56:50
climate change or the champion of the
1:56:52
people or the defender of equality and
1:56:55
fairness he needs to apologize for his
1:56:58
failure to deal with racism in the
1:57:01
labour party to resume produced a copy
1:57:05
of the advert and began to read from it
1:57:08
welcome everyone
1:57:15
this is great we that was quick and she
1:57:21
just went from you know what you hate
1:57:23
Jews to you know the people or the
1:57:28
defender of equality and fairness he
1:57:31
needs to apologize for his failure to
1:57:34
deal with racism in the Labor Party
1:57:37
Theresa May produced a copy of the
1:57:40
advert and began to read from it the
1:57:42
Labour Party welcomes everyone
1:57:49
this is your legacy mr. Corben you still
1:57:53
haven't opened your eyes you still
1:57:55
haven't told the whole truth
1:57:57
you still haven't accepted your
1:58:00
responsibility you have failed the test
1:58:03
of leadership apologized now
1:58:06
[Applause]
1:58:08
the Labour leader said his party had
1:58:11
been the first to introduce anti-racist
1:58:13
legislation this party totally opposes
1:58:17
racism in any form whatsoever
1:58:22
anti-semitism has no place in our
1:58:25
society no place in any of our parties
1:58:29
and no pays place in any dialogue
1:58:35
[Music]
1:58:44
unbelievable truly I mean that's that's
1:58:49
I mean they I've seen the newspapers
1:58:51
talk about a little bit in the UK and
1:58:54
people but I mean just for Theresa May
1:58:56
to jump out like that that was that was
1:58:58
out of the blue cuz they're talking
1:59:00
about climate change that was just lame
1:59:02
but it was funny and I'm you see I
1:59:05
thought you see comedy bits about
1:59:06
there's one thing I remember from you
1:59:08
years ago on an old show called Fridays
1:59:10
we're actually Larry David was playing a
1:59:12
character and he comes in and there G
1:59:15
comes in as a substitute lawyer and then
1:59:18
he so he comes in as a substitute lawyer
1:59:23
into some case about about whatever the
1:59:26
case was he goes up in the first thing
1:59:28
he does is he accuses the witness of
1:59:30
being a lesbian for no reason for no
1:59:35
reason fantastic that was good that's in
1:59:39
fact I think that is worthy of a clip of
1:59:44
the day I'm gonna give that
1:59:45
[Music]
1:59:49
but that was truly bizarrely nutty and
1:59:52
what a way to shame someone just in a
1:59:55
horrific way you Jew hater apologize now
1:59:58
Jew hater what's the guy gonna do I'm
2:00:01
sorry it's not that different than what
2:00:04
did calling everyone a racist if a Jew
2:00:07
hater is next level shit man now there
2:00:11
was something going on there was a bunch
2:00:12
of protests being discussed on democracy
2:00:14
now I want to play these two clips real
2:00:16
quick cuz one of them is neither one of
2:00:19
these stories keep cropped up but the
2:00:20
second one is very strange to me because
2:00:23
when you hear the second one you're
2:00:24
gonna wonder what is going on but this
2:00:27
is the undiscussed protester says NIN's
2:00:29
but of nuns there's a nun protests going
2:00:32
on in Washington DC
2:00:34
Capitol Police arrested 70 Catholic nuns
2:00:37
and clergy Thursday as they held a
2:00:39
non-violent sit-in protests inside the
2:00:41
Russell Senate office building against
2:00:43
the Trump administration's inhumane
2:00:44
treatment of immigrants and asylum
2:00:46
seekers more than a dozen protestors
2:00:49
stood in a circle holding the
2:00:50
photographs of migrant children who've
2:00:52
died in US custody reciting their names
2:00:56
the latest protest came as immigrant
2:00:58
communities across the country have
2:00:59
prepared for reported ice raids that
2:01:02
were scheduled to begin last weekend but
2:01:05
have largely not materialized nearer
2:01:09
never getting materialized but that's
2:01:10
the thing is a conflict of interest the
2:01:12
Catholic Charities and the whole thing
2:01:14
and we come on nuns oh yeah totally
2:01:17
this is a very good point and people
2:01:19
need to understand that the Catholic
2:01:21
Charities receive over a billion dollars
2:01:23
just recently actually it was two
2:01:25
billion for all of them but these the
2:01:27
Catholic Charities and the charities it
2:01:29
would locate it here in Austin I think
2:01:31
it's because it's a Christian or
2:01:33
Catholic I don't remember there one is
2:01:35
Catholic says Catholic but they are
2:01:37
religious organizations who are
2:01:39
receiving this government money to to
2:01:43
take care of the kids take two kids and
2:01:49
then have the nuns protesting yeah that
2:01:51
is pretty sickening thinking that they
2:01:53
may be just protesting for money for
2:01:56
their organization it's not where was
2:01:59
the mention of that I'm sure that I'm
2:02:01
sure Amy came back and said something
2:02:02
about
2:02:03
that didn't you know okay well now yet
2:02:06
but that what came up next was this and
2:02:08
listen to this carefully and tell me
2:02:10
have you heard anything about this and
2:02:13
why does it what what is what's the deal
2:02:16
this is the last clip that says I know
2:02:19
what it says is will Indian asylum
2:02:21
something you see it yeah I see you got
2:02:24
meanwhile a group of Indian asylum
2:02:27
seekers in El Paso Texas have launched a
2:02:29
hunger strike from inside a nice
2:02:31
immigration Jail demanding they be
2:02:32
released while they appeal their
2:02:34
deportation orders one of the men told
2:02:36
the Texas Monthly if I go back to India
2:02:38
I'll be tortured and killed
2:02:39
I can die here it's the second time this
2:02:42
year that Indian men have led hunger
2:02:44
strikes at the El Paso processing center
2:02:47
oh I think we should just open the
2:02:49
borders for all Indians so you know what
2:02:52
this is about
2:02:54
what are they protesting if they go back
2:02:57
to India they're gonna be tortured and
2:02:59
and they're gonna die there what well
2:03:01
you know anything about Indians being
2:03:03
tortured in India and coming over here
2:03:06
somehow getting over here and ending up
2:03:08
in El Paso well I'm pretty sure they
2:03:11
have millions of slaves in India people
2:03:13
are in slave labor situations in the
2:03:16
caste system it's a very divided culture
2:03:20
they have so yeah I'm pretty sure if
2:03:23
you're in the in the lower tiers that
2:03:25
you just screw you you're no good
2:03:28
well I'd like to get more information on
2:03:31
this story she didn't provide anything
2:03:34
she's just a vague it was just a vague
2:03:36
general story like as I said we all
2:03:38
supposed to know about this I don't know
2:03:40
anything about this life I was
2:03:42
distressed hmm somehow I get a short
2:03:47
picture of you really being distressed
2:03:49
but I understand I see the show I so I
2:03:52
can offer since we're in the topic okay
2:03:55
latest racist remarks ooh that may be a
2:03:58
good one we'll have more unprecedent
2:04:00
Trump's latest racist remarks remarks
2:04:03
it's you didn't clip it very well a
2:04:05
little thing at the end more on
2:04:06
precedent Trump's latest racist remarks
2:04:10
so now I gotta commit I had a great clip
2:04:14
at the end
2:04:15
mmm it's a poor consideration we've been
2:04:19
talking about the face app that changes
2:04:22
your face that once again went viral and
2:04:26
even though we discussed it clearly in
2:04:29
detail on the last episode as to why
2:04:31
this is a problem your government not
2:04:34
yours maybe mm but here in America we
2:04:37
got Chuck Schumer one of the leading
2:04:39
Democrats the the copied a cootie copy
2:04:44
of all the Democrats certainly in on
2:04:48
Capitol Hill telling us the American
2:04:50
people's that you should very Fred be
2:04:53
very proud of his up but his reasons hi
2:04:56
everybody and I'm here to give by the
2:04:58
way this is how he talks to young people
2:05:00
hi everybody this is what he thinks is
2:05:02
hip so it's like hi everybody hi
2:05:04
everybody and I'm here to give a warning
2:05:06
for all Americans
2:05:07
millions of people have been downloading
2:05:10
facia it seems like fun it applies a
2:05:13
little AI to a selfie to make your face
2:05:16
look older younger add a beard whatever
2:05:18
what seems like a new social media fed
2:05:21
however may actually not be benign at
2:05:24
all recently it came to light that the
2:05:27
parent company the app wireless labs is
2:05:30
based in st. Petersburg just as
2:05:33
worrisome it came to light that the app
2:05:35
not only takes your picture but retains
2:05:38
the right to keep your photos or even
2:05:40
your search history it allows quote
2:05:42
perpetual irrevocable and worldwide
2:05:45
license to your photos name or likeness
2:05:48
so this is a breathtaking and possibly
2:05:52
dangerous level of access and it raises
2:05:55
substantial privacy concerns the risk
2:05:58
that your facial data could also fall
2:06:00
into the hands of something like Russian
2:06:02
intelligence or the Russian military
2:06:04
apparatus is disturbing I'm
2:06:08
flabbergasted by this for a number of
2:06:12
reasons one he could have been reading
2:06:15
the terms and services of Instagram
2:06:17
Facebook or any other social network
2:06:19
they all have that irrevocable worldwide
2:06:23
license in Perpetua in perpetuity to use
2:06:26
your name likeness they all have that
2:06:29
they can all do that and then because
2:06:32
the company is headquartered in state
2:06:34
you didn't even say Russia at the
2:06:35
beginning saint-petersburg oh now we
2:06:37
have to be worried about your face it
2:06:39
data falling into the hands of the
2:06:41
Russians
2:06:42
shut up Schumer what a nincompoop the
2:06:47
real issue is that app is tracking your
2:06:50
ass all over the place it's got us
2:06:52
trackers in it actually I got so pissed
2:06:56
off when I heard this clip I went to
2:06:58
find I did I went I'm like who who are
2:07:01
these data brokers we've talked about
2:07:03
this a lot the data brokers the data
2:07:06
brokers do you have any idea who that
2:07:07
data brokers are well other credit card
2:07:11
companies our credit companies that
2:07:13
Viking guys are there's a lot of them
2:07:15
actually the number one aggregator of
2:07:18
data broker content and data is Oracle
2:07:23
yeah and there you go and you know why
2:07:27
they had a couple they had ad tech all
2:07:29
in 2014 they were buying up little ad
2:07:31
tech companies but then they made a
2:07:33
whopper they acquired axiom ACX io m
2:07:38
you've never heard of axiom you're not
2:07:40
supposed to hear of axiom but they have
2:07:43
and it's a very old company they've been
2:07:45
around since the early 60s and they are
2:07:49
the original Mac Daddy of collecting
2:07:51
your purchase history they have an
2:07:53
entire network of getting offline
2:07:55
information they sell this back to
2:07:57
Google I'm sure Google has its own
2:07:59
competencies and everyone's building
2:08:01
their own databases and profiles on you
2:08:03
but they use the right term spicey a
2:08:10
yeah in fact yes it is a dossier
2:08:15
nine of the top ten automotive companies
2:08:19
use them 8 out of 10 financial companies
2:08:22
the credit card companies the
2:08:24
pharmaceutical companies these are the
2:08:26
people who have the dossier on you and
2:08:29
they can take all your disparate data
2:08:32
source for a long time very valuable
2:08:35
company they were purchased by Oracle
2:08:37
for almost 3 billion dollars and this is
2:08:40
all being stored in Oracle databases you
2:08:42
know
2:08:42
use Oracle databases yeah the government
2:08:44
in fact in 2001 Acxiom pitched the
2:08:48
Department of Justice to start scouring
2:08:52
the internet and websites and people's
2:08:54
profiles I mean the dossier for keywords
2:08:57
now the government rejected them at
2:08:59
least according to this letter but they
2:09:01
the government was also spoke very
2:09:03
highly of their capabilities and was
2:09:05
finding oh there was some conflict of
2:09:07
interest you know the thing you're
2:09:09
overlooking and you shouldn't mention it
2:09:11
you know you just know how accurate
2:09:15
these dossiers are about you and you can
2:09:18
attest to that as you usually get pulled
2:09:20
over every time you'd left the country
2:09:22
no you know and once you're in the
2:09:23
dossier it's hard to get something out
2:09:25
of the dossier if there's bad data in
2:09:27
there they used to allow you to look at
2:09:31
what they have in your in their dossier
2:09:33
on you they've closed that down although
2:09:36
they do say that beginning 2020 they're
2:09:39
bringing back a new customer portal they
2:09:42
also bought something called the I think
2:09:45
was live was at livewire I gotta find
2:09:49
this they bought some other company and
2:09:51
you can opt out of that opt-outs yes
2:09:56
here it is that company would live ramp
2:10:00
there you go live ramp and so that's how
2:10:03
they get a lot of their data from the
2:10:04
web which you apparently can opt-out of
2:10:07
what I thought would be interesting is
2:10:10
to listen to their marketing pitch they
2:10:12
have a you know a marketing video and
2:10:14
it's it's gobbledygook marketing poop
2:10:17
but it's interesting because from my
2:10:21
research over the weekend into these
2:10:24
guys it's real what they can do they
2:10:26
really have the goods on all of us
2:10:37
across the globe always connected
2:10:40
consumers expect seamless omni-channel
2:10:42
personalized experiences from the brands
2:10:45
they engage
2:10:46
maxxium the data and technology
2:10:48
foundation for the world's best
2:10:50
marketers is a trusted advisor helping
2:10:52
brands build an open garden reality that
2:10:55
unifies data at the foundation layer
2:10:57
connecting the marketing ecosystem for
2:10:59
the ultimate customer experience and
2:11:01
that means consumers when with more
2:11:04
relevant content and timely offers that
2:11:07
align with their needs axioms robust
2:11:10
suite of offerings combine 50 years of
2:11:13
data-driven marketing experience and
2:11:15
leadership and ethical data use and
2:11:17
privacy enabling brands and their
2:11:19
partners to create powerful experiences
2:11:21
across every consumer touch point in
2:11:24
more than 60 countries across Asia
2:11:26
Pacific Europe and the Americas
2:11:28
maxxium provides clients access to
2:11:31
two-and-a-half billion consumers and
2:11:33
two-thirds of the world's digital
2:11:35
population
2:11:36
maxxium helps marketers by delivering a
2:11:38
suite of superior services and
2:11:40
omni-channel data environment solutions
2:11:42
omni-channel solutions leverage the best
2:11:45
data identity management strategy and
2:11:48
analytic services combined with a
2:11:50
unified data layer framework connecting
2:11:53
mark tech and ad tech at the data layer
2:11:55
to deliver quantifiable business impact
2:11:59
by providing the most advanced program
2:12:01
for data ethics and governance axiom
2:12:04
always puts the consumer first axiom
2:12:07
brings a powerhouse of data-driven
2:12:09
marketing expertise to serve the world's
2:12:12
best brands
2:12:14
axiom powerful capabilities centered
2:12:17
around a trusted unified data foundation
2:12:20
that advances that data-driven
2:12:22
next-generation of marketing these
2:12:24
capabilities unlock more value and new
2:12:27
opportunities that unify first second
2:12:30
and third party data at scale and
2:12:32
protect privacy across the ecosystem
2:12:34
what experience matters brands trust
2:12:38
axiom that's a two minute video
2:12:41
explaining how they're spying on you
2:12:42
what I find kind of sad is if they get
2:12:45
something wrong some data is incorrect
2:12:47
somehow or you made a choice that was
2:12:49
different this is the this is what
2:12:52
everyone pulls that pulls our
2:12:54
information from this is where they get
2:12:55
it from and you know throwback to our
2:12:59
call back to our algo and machine
2:13:02
learning example this company could
2:13:05
screw up your life you might not even
2:13:07
know it absolutely
2:13:09
these are the guys and so and the thing
2:13:12
is they're the thing and the government
2:13:14
itself will be more and more reliant on
2:13:17
these databases yet for one good reason
2:13:19
their FOIA proof ooh yes no freedom of I
2:13:25
want to do a dossier they can let you
2:13:27
these guys do a dusty and they use it
2:13:29
and you can purchase it legally it's
2:13:31
that simple and it's just so I don't
2:13:34
have to invoke his name I think the only
2:13:37
people actually read the User Agreement
2:13:39
lawmakers in Washington journalist that
2:13:42
are writing about it today but I just a
2:13:43
quick quick point to make when you sign
2:13:45
up for this app it asked to enable your
2:13:47
entire photo album and so that's the big
2:13:50
question why do they need permanent
2:13:51
access before these app yeah everybody
2:13:53
does it so how can sighs so how could
2:13:56
you really are they
2:14:02
I have news for you unless you live like
2:14:04
the Unabomber in a cabin in the woods by
2:14:14
now yeah who's been talking to steady
2:14:17
Hitler yeah I usually say professor Ted
2:14:20
would be proud but now I mean okay so
2:14:22
they calmed the Unabomber like you're
2:14:24
crazy
2:14:31
[Music]
2:14:40
[Music]
2:14:43
we do have a few people thank to thank
2:14:46
for show 11:57 9:57 yo stop that sir
2:14:53
Nils moniker in Hamburg Hamburg
2:14:56
Deutschland hundred eleven dollars and
2:14:57
11 cents Elisabeth burrows --n I think
2:15:01
she's a dame is she yeah Dame Beth yep
2:15:05
hey boys love you want to go to do love
2:15:10
what you do to the moon in back okay
2:15:12
later
2:15:14
Standish Oh Robert Ronald Shull 8:08 Sir
2:15:21
John
2:15:23
Horner Horner and Bay City Bay st. Louis
2:15:27
got a birthday call out for a
2:15:29
smoking-hot wife Sarah cozy we got that
2:15:32
listed for you and an IT M from an
2:15:35
impromptu in a meet-up
2:15:36
of the Helena Arkansas local 1360 so a
2:15:40
shout-out to Sir Rocket Man Baron of the
2:15:42
bass or Terry of Crowley's Ridge and sir
2:15:44
one night in Bangkok well we do have the
2:15:47
108 dollar donation to make a correction
2:15:49
it actually came from yes this is from
2:15:53
sir spud sir spud the mighty yes but the
2:15:57
mighty explain I that was a we went back
2:16:00
and forth there was a mistake mistake
2:16:02
was that for the last show so sir spud
2:16:05
the mighty it was for him and he wanted
2:16:07
to and you wanted to call out some other
2:16:09
people who were thin yes I got confusing
2:16:11
okay we haven't really made it good in
2:16:13
that case
2:16:15
it was composing them he call that
2:16:17
mostly coal items oh yeah you want to
2:16:19
read the I don't have the note in front
2:16:20
of me so I guess it's not good we still
2:16:24
have to go to the next show to fixers
2:16:26
but the might I do have the note
2:16:27
somewhere I will get to it later good
2:16:29
tomorrow next show
2:16:31
Ian Nicholas Lindbergh in Stockholm 5555
2:16:38
there's not a big list people although
2:16:41
we do have a lot at $50 donors which was
2:16:42
the theme donation for this show 50
2:16:45
years anniversary of the moon landing
2:16:47
Blake Farrell in Arlington Texas double
2:16:50
nickels on the Dymaxion wash I'm sorry
2:16:52
I'm sorry I'm sorry Blake needs an F
2:16:55
cancer for his good friend Mike and his
2:16:57
wife Yvonne in benbrook texas mike hit
2:16:59
me in the mouth several years ago I've
2:17:01
been an anonymous freeloader ever since
2:17:02
no more so it needs a D douching as well
2:17:05
I have to do these you've got karma we
2:17:17
need to cause I think we get so many F
2:17:19
cancers I think the shill has to code
2:17:21
them on the spreadsheet so we don't
2:17:22
overlook them no usually catch him I
2:17:25
don't think we've missed too many Maxine
2:17:27
widened there's enough codes on here
2:17:30
Maxine Waters gravel 50/50 turned
2:17:33
another year older on the 18th hole is
2:17:36
this gravel gravel is I don't know and
2:17:39
but the gravel at dunkey a-- in dutch at
2:17:42
the end there so the gravel is
2:17:43
multilingual could be alright music in
2:17:47
Greensboro North Carolina the following
2:17:49
people will be $50 donors either if
2:17:51
normal $50 donors are celebrating the
2:17:53
50th anniversary of the moon launch the
2:17:55
moon shot Andrew gusik in Greenville
2:17:58
North Carolina Robert case in Mill
2:18:00
Spring North Carolina
2:18:02
Thomas tallit in Shawnee Oklahoma is a
2:18:05
birthday coming up for his his boy
2:18:07
Daniel Lee boy and Bath Michigan John
2:18:11
Knowles
2:18:12
John Luke Oh Matthew Hawkins and Mabel
2:18:17
Mill Arkansas Dennis Stark Oh sir Josh
2:18:22
Mandel in Greenville South Carolina
2:18:26
Jeffrey
2:18:28
Radwin jeremy oh yes jeremy Redwyne Carl
2:18:35
reset the eyeball Carl hamburger it's
2:18:44
funny because I think if laces I know
2:18:47
from like a clip you know and you say
2:18:50
well it says this and I can clearly see
2:18:51
what it's then I know your eyeball needs
2:18:53
resetting but I need to get be able to
2:18:55
get your cue to say eyeball reset
2:18:57
because it's not it'll be in the book
2:19:02
but the this is not discussed by the
2:19:06
your ophthalmologist this eyeball
2:19:08
situation but I was talking to over my I
2:19:11
have two neighbors that live next door
2:19:12
to me two neurologists and I expand they
2:19:16
had a friend who had a cataract
2:19:17
operation then something he committed
2:19:19
suicide some because he was seeing
2:19:20
things or something like that I said you
2:19:22
know is I said you get this crazy it's
2:19:27
not like it's not like LSD level
2:19:30
hallucinations but you get these just
2:19:32
just semi hallucinations worthy and my
2:19:35
explanation is as follows and the two
2:19:37
neurologists agreed with the theory and
2:19:40
it is that when you're you got a
2:19:42
cataract and you're living with this
2:19:45
cataract for sometimes years and years
2:19:47
and years to the point where it's just
2:19:48
kind of a blurry eye and but you still
2:19:51
have the two eyeballs working and so the
2:19:53
one eyeball is doing a lot of
2:19:54
interpolation I'm gonna use that word me
2:19:57
me call me out on it
2:19:59
interpolation which it means this dream
2:20:01
is making images that aren't there
2:20:02
because your brain read your eyeball
2:20:04
read this to see as much as your brain
2:20:05
makes it see you know but it puts the
2:20:07
love these images together and that's
2:20:10
how I we see the old tube the old TV
2:20:14
tube was you never there was no image on
2:20:15
it take a photo of one and it was just a
2:20:18
stripe of something and your eyeball put
2:20:20
it together yeah interlaced yeah we was
2:20:24
not only interlaced but it was really
2:20:26
there's not a lot of info at any one
2:20:27
time what was the word you used again
2:20:29
interpolation what
2:20:32
Interpol yeah interpolate interpret
2:20:35
interpolate mm-hmm now that's why by the
2:20:40
way and people should note this this is
2:20:42
one of our turning into a segment I like
2:20:45
it dogs dogs could never see the
2:20:48
television when you had an old screens
2:20:51
the old screens the old of tubes the
2:20:53
dark wood dogs and cats couldn't see
2:20:55
that but they can see it LCD TVs that's
2:20:58
where there's dogs watch TV now if they
2:21:01
see another dog they'll bark at it the
2:21:03
do all these things they never used to
2:21:05
do because there was no full image up
2:21:07
there that just took up just stood on
2:21:10
there like a picture like they are today
2:21:11
so it's a different phenomenon well your
2:21:14
eyeball used to use to dream and stuff
2:21:16
up so when you get your cataract removed
2:21:17
and put a clear beautiful lens in there
2:21:20
sees everything it's it's thinking well
2:21:24
maybe there's more to it and so it
2:21:26
starts the dream stuff up while your
2:21:28
brain yeah so you see stuff sometimes
2:21:31
that is a that isn't there or you're you
2:21:34
interpret something wrong I was watching
2:21:35
a tennis match with Serena Williams and
2:21:38
it for one split second she had three
2:21:40
arms she doesn't have three arms and I
2:21:49
realized it was just I caught an image
2:21:51
of something in the background I my
2:21:53
eyeball decided it's kind of you know it
2:21:55
was good it was working so hard with the
2:21:57
cataract I said let's do so have some
2:21:58
fun and so it put the three arms on the
2:22:01
woman and so I said this is not right it
2:22:03
didn't last that long but if you you
2:22:05
could be unnerved by this that's perhaps
2:22:08
the most insane a sentence you've just
2:22:11
you've ever spoken what well I'll play
2:22:15
it back to you after the I mean you were
2:22:17
like I can't believe it the woman had
2:22:19
three arms and then I did my I've just
2:22:21
like wow
2:22:22
this is that was that was very druggie
2:22:24
of you ladies and gentlemen John C
2:22:27
Dvorak where the C stands for Columbo oh
2:22:30
where were we
2:22:31
for a Josh Mandel in Greenville South
2:22:34
Carolina Jeremy Redwyne and uh in J and
2:22:37
K obviously Carl hamburger in fifty
2:22:42
these are all $50 donors many there's
2:22:44
not that many cuz
2:22:45
running out Erik Faris John Helmer in
2:22:50
Shawnee Kansas Ralf Massaro George wood
2:22:56
sir I believe in Universal City Texas
2:22:58
and that's it boom this was a very short
2:23:01
list nobody wanted to celebrate the 50th
2:23:03
anniversary of the moonshot and per week
2:23:09
surprise surprise thank you anonymous
2:23:12
and the other executive producers yes
2:23:15
surprise surprise the irony of all this
2:23:19
is that you actually because you've you
2:23:22
know less people participated in sending
2:23:25
us some value the irony is kind of that
2:23:29
you get more show somehow you do get
2:23:32
more show the story about three arms
2:23:35
Serena yeah well maybe people couldn't
2:23:37
have straighten that out for the
2:23:39
Thursday show but thank you very much
2:23:40
everybody who did come in and help
2:23:42
produced episode 1157 that's over $50
2:23:47
very big thanks to everyone under $50
2:23:50
that is for most just to be anonymous
2:23:53
and to be anonymous a lot of people take
2:23:55
out a subscription that just continues
2:23:57
so they continue to support the show
2:23:58
we've got a a number of them and please
2:24:00
go check them out at Vollrath org slash
2:24:05
[Music]
2:24:09
meet-ups
2:24:10
this is something that seems to be
2:24:13
working very well for people's overall
2:24:15
mental health their their friendship
2:24:18
their relationships because you can go
2:24:20
to a no agenda meet up you can meet
2:24:21
people you've never met before people
2:24:24
you probably would never ever bump into
2:24:26
it's a very diverse group and you could
2:24:28
talk to everybody about whatever you
2:24:29
want and people don't get triggered
2:24:31
because their amygdalas are healthy we
2:24:34
have on the books for the 26th of July
2:24:36
st. Louis in Portland Oregon Buffalo New
2:24:39
York and Frisco Texas on the books for
2:24:40
July 27th Central Florida July 28th
2:24:43
August 1st Seattle Washington we still
2:24:46
have the lot festival in Ravens Bergen
2:24:48
in Germany
2:24:49
which I need I still need clarification
2:24:51
says august 2nd through 4th that seems
2:24:54
like a pretty long meet up or in Orange
2:24:56
County California August 3rd Murphysboro
2:24:58
tennis
2:24:59
see August ninth the 10th in Chicago
2:25:02
August 18th Victoria British Columbia
2:25:04
the 22nd is Charleston South Carolina I
2:25:07
believe that's their their six-week
2:25:09
cycle and then the 23rd is Salem Oregon
2:25:13
those are your meetups
2:25:14
go to no agenda meetups dot-com to find
2:25:17
out more about these individual listings
2:25:21
and if there isn't one there that is
2:25:23
near you then you can set one up you can
2:25:25
you can get it on the calendar and get
2:25:27
it going and it's again it's just
2:25:29
something fantastic to purchase I would
2:25:31
like to make a comment Jeff I was
2:25:34
offered that flight from the Orange
2:25:37
County meetup back to which is coming up
2:25:39
back to the Bay Area by our Baron mark
2:25:46
Tanner mm-hmm and I have to mention the
2:25:49
people and I've mentioned this to Nadia
2:25:50
who's gonna be there just meetups on
2:25:55
Saturday there's no way I can do show
2:25:57
prep and then get back and do a show on
2:25:59
Sunday if the meetups on Saturday if
2:26:01
you're gonna do me up that you want me
2:26:02
to attend even though I'm gonna attend
2:26:04
and then he said maybe one or two I will
2:26:07
do this southern the Silicon Valley one
2:26:09
shortly and which hasn't been set up it
2:26:12
has to be on Thursday or Friday nights
2:26:14
otherwise it's just not possible to do
2:26:16
the meet up and do the show yeah really
2:26:18
if you want to separate unless you do so
2:26:21
more is more important now where would
2:26:23
this meet it meet up be there's gotta be
2:26:25
in Orange County somewhere you can't
2:26:27
just stay there and that and do your
2:26:29
take your mobile rig and do it from
2:26:30
there
2:26:31
no I all day saturday doing clips I got
2:26:34
I got I could do the news there on
2:26:39
Friday but then there's clip day I got
2:26:40
to do all my clips and they got to
2:26:41
produce the clips it's not possible it's
2:26:43
not even close to being possible yeah
2:26:45
that in the John and I have a little
2:26:48
different schedule I'm different
2:26:49
timezones helped a lot I am prepping
2:26:53
throughout the we both are prepping
2:26:54
throughout the entire week and I pretty
2:26:56
much have a have a puzzle that I get up
2:27:00
a Thursday in SATA and Sunday mornings
2:27:02
at 5 o'clock 5:30 5 to be exact for some
2:27:05
reason and and then I start assembling
2:27:08
everything so I that's when I record the
2:27:11
clips
2:27:11
that's when I put packages together with
2:27:14
the the outline the show notes and so
2:27:17
that takes me you know up until 11 a.m.
2:27:20
when we start the show and I'm usually
2:27:22
working right up until that deadline
2:27:24
when I'm in Europe I can get almost
2:27:28
everything done on on Sunday and I can
2:27:33
start a little bit later I can get I
2:27:34
start at 10:00 a.m. and then I have
2:27:36
until 5:00 in the afternoon before we
2:27:38
start the show on it you know it's basic
2:27:40
basically they're 12-hour days for me
2:27:42
outside of the producing but John's
2:27:44
schedule which has been for a while now
2:27:47
is you know he does all of that
2:27:49
production work Saturday sends me his
2:27:52
clips
2:27:52
I guess they come in here around 1:00 in
2:27:54
the morning 2:00 in the morning Sunday
2:27:56
when you go to bed you get a bit around
2:27:57
midnight I think typically yes right and
2:28:00
I wake up I like the idea of having
2:28:08
everything done and then not having to
2:28:09
do a bunch of hurried up clips in the
2:28:11
morning which I used to do I used to get
2:28:13
up earlier because then you know
2:28:15
sometimes I want to produce a clip I'm
2:28:17
gonna spend it I would spend more than
2:28:19
five seconds doing the clips I want to
2:28:21
edit it I want to do something to make
2:28:23
it sound you know with some extra oomph
2:28:25
and every once in a while and I can't do
2:28:28
that in the morning and I'm also like it
2:28:30
gets me up too early and I'm groggy and
2:28:32
I also like to look at the current news
2:28:36
because I got busted a couple of times
2:28:37
for breaking stories that took place
2:28:38
that morning I didn't know it was like
2:28:40
and I will occasionally send in a late
2:28:42
clip but it's written for context we
2:28:46
probably play between 35 and 40 clips on
2:28:49
every single show we have 50 or more
2:28:52
every single show go make 50 clips come
2:28:57
back and tell me what that was worth
2:28:58
here
2:29:00
[Music]
2:29:06
21st
2:29:08
thousand 1958 belated birthday wishes
2:29:11
going out to Maxine Waters gravel who
2:29:13
celebrated on the 18th age undetermined
2:29:17
and maybe we don't want to know
2:29:18
also happy birthday today to view for K
2:29:20
turns 58 Sir John Warner says happy
2:29:23
birthday to his smoking-hot wife Sarah
2:29:25
cozy studly celebrating tomorrow and
2:29:27
Thomas Tollett says happy birthday -
2:29:29
he'll be turning fifteen years old on
2:29:31
the 26 happy birthday from the staff and
2:29:33
management and back office here at the
2:29:35
best podcast in the universe so we have
2:29:40
one knighting one daming so that means
2:29:44
we need the main the fimo source we got
2:29:47
him that's right
2:29:50
parkstar a and Simon Levay Zeus key step
2:29:55
I thought both and you are about to join
2:29:56
the illustrious group of the knights and
2:29:58
dames of the No Agenda round table for
2:29:59
your contributions the amount of $1,000
2:30:02
or more it makes me incredibly proud to
2:30:04
pronounce the case Dave Carolyn Hogtown
2:30:07
and table we've got hookers to blow red
2:30:13
Boyz and the Chardonnay and we've got
2:30:15
extra coffee and hot sauce pot and
2:30:17
vinegar Mac but enough we got kebab and
2:30:19
Persian wine harlots and Haldol
2:30:21
pepperoni ELLs and Pale Ales cowgirls
2:30:23
and coffin varnish breast milk and
2:30:25
powdered sparkling cider and escorts
2:30:27
ginger ale and gerbils bong hits and
2:30:28
bourbon and mutton and Mead it's always
2:30:31
the fan favorite go to No Agenda
2:30:33
nation.com slash rings and I had enter
2:30:37
all your information so we can get those
2:30:38
out to you as soon as possible and
2:30:39
welcome to the round table our brand new
2:30:42
night and our brand new Dame and thank
2:30:44
you for your courage and thank you for
2:30:45
supporting the show we can't do without
2:30:47
you and it is incredibly highly
2:30:49
appreciated you get the rundown the
2:30:55
thing that's still kind of happening
2:30:57
boiling is this ship ship seizures
2:31:02
they've been going on in the Persian
2:31:04
Gulf
2:31:05
I'm glad you tracked because you know I
2:31:07
was listening to the BBC and they were
2:31:10
kind of pooh-poohing it a little bit
2:31:12
they didn't seem like all freaked out
2:31:14
and warm it was none of these these are
2:31:20
too out of control but let's play the
2:31:22
background here which is ship a seizure
2:31:25
by raft she's the worst
2:31:33
tensions in the Persian Gulf have
2:31:35
escalated sharply today with reports of
2:31:38
Iran ceasing to oil tankers Britain says
2:31:41
one was British flagged the other was a
2:31:44
Liberian flag ship operated by a British
2:31:47
concern the vessels were stopped in the
2:31:49
Strait of Hormuz and diverted to Iranian
2:31:52
waters Tehran confirmed the first
2:31:54
seizure but denied the second earlier
2:31:57
Iranian officials also denied that the
2:31:59
u.s. warship boxer destroyed an Iranian
2:32:03
wrong yesterday they deny the drone just
2:32:07
thing and they had this guy on PBS
2:32:10
newshour this jarv ad serif' who's the
2:32:13
UN ambassador from Iran mm-hmm and they
2:32:16
did had a little discussion with him at
2:32:18
there's some information there's a long
2:32:19
clip but there's information in here I
2:32:22
think that's valuable I think we should
2:32:23
play it
2:32:24
Minister Zarif thank you very much for
2:32:25
talking with us good to do again let's
2:32:28
start with the downing yesterday by the
2:32:31
United States of the Iranian drone in
2:32:33
the Strait of Hormuz President Trump
2:32:35
says that this was just the latest in a
2:32:38
series of provocative and hostile acts
2:32:41
by Iran is that how you see it well
2:32:46
first of all to the best of our
2:32:48
information we didn't lose any drones
2:32:51
yesterday so it doesn't look like that
2:32:54
they shot one of our drones
2:32:56
maybe they shot one of their drones the
2:32:59
reports that they thought probably
2:33:00
somebody else's drums but provocative it
2:33:05
even if it were our drone we are in our
2:33:09
own neighborhood the US naval vessel is
2:33:13
about six thousand miles away from its
2:33:16
shores so I would ask you what's wrong
2:33:19
with you man
2:33:20
don't you know who we are heart being
2:33:22
provocative the Trump administration
2:33:25
official line is that the u.s. is not
2:33:27
looking for war with Iran do you believe
2:33:31
that no we didn't come to the Gulf of
2:33:32
Mexico they came to the Persian Gulf now
2:33:36
they have to watch that they should not
2:33:40
undermine our sovereignty our
2:33:42
territorial integrity or our security
2:33:45
and then we won't have a war you've been
2:33:49
saying this week mr. minister that if
2:33:52
the
2:33:54
us that if that Iran may be prepared to
2:33:58
change the course of your uranium
2:34:01
enrichment no we're not we're not we
2:34:04
have an agreement that we negotiated
2:34:06
with the United States it doesn't matter
2:34:09
which government of the United States
2:34:11
because the outside world considers the
2:34:15
government sitting in Washington
2:34:17
representing there as representing the
2:34:20
United States there is a provision in
2:34:23
the current agreement that is in 2023
2:34:26
we're supposed to ratify the Additional
2:34:30
Protocol which requires us to put all
2:34:33
our facilities under UN inspections for
2:34:37
life that would be permanent and it
2:34:40
would also require the United States to
2:34:43
lift its sanctions by Congress
2:34:46
permanently that is a provision that we
2:34:49
already negotiated he wants to do better
2:34:52
he can implement that provision right
2:34:55
now and rest assured that Iran will
2:34:57
never produce nuclear weapons if that is
2:34:59
his objective he can do it now 2023 we
2:35:02
are prepared to to bring that forward we
2:35:06
need to go to our Parliament our
2:35:07
Parliament needs to ratify it we could
2:35:09
bring it forward so that President ROM
2:35:11
could make history by making sure that
2:35:16
the relations between the two countries
2:35:18
would change forever Wow let's do it
2:35:23
yeah I didn't know that that sounds that
2:35:26
sounds spot-on I mean that's first of
2:35:30
all the Iran nuclear deal was never
2:35:33
ratified by the by Congress Ryan by
2:35:36
sanity and it is like it was a piece of
2:35:39
paper promise from Obama it wasn't much
2:35:41
more than that so but but if this guy is
2:35:44
saying hey will in perpetuity you know
2:35:47
have everything open you can check it
2:35:49
won't build nuclear weapons lift the
2:35:51
sanctions sounds like a great idea I
2:35:53
mean it's like this is what an olive
2:35:56
branch is move it forward from 23 to 20
2:35:59
but the problem is Saudi Arabia and I
2:36:02
think Trump has put too many marbles
2:36:04
over there including that troops now
2:36:05
going over
2:36:06
- standby cuz that's what this is all
2:36:08
about it's the Saudis and the Iranians
2:36:11
they hate each other - you know at a
2:36:14
fundamental level
2:36:16
yeah well fundamental fundamental the
2:36:19
religions are disparate well I call that
2:36:22
fundamental yeah fundamental from the
2:36:24
middle yes
2:36:26
damned well maybe maybe he's holding us
2:36:29
on the I mean he has the president has
2:36:31
said consistently he hey we want to talk
2:36:34
we want to talk to him yeah he's one of
2:36:37
the few that Dex you will go talk to
2:36:39
people he stepped in North Korea
2:36:41
well this to me isn't almost a
2:36:46
no-brainer except again what do we do
2:36:48
with Saudi Arabia lets you and I think
2:36:49
this through because the part of Saudi
2:36:52
Arabia doesn't want to want a Ron to be
2:36:55
a nuclear power they also don't want
2:36:58
them in the oil market I think they can
2:37:04
both be in the oil market but maybe it's
2:37:06
just the oil but they're not the big
2:37:08
players I mean Saudi Arabia still is the
2:37:10
big plug the big dog all right I thought
2:37:13
we were the big dog now well yes as
2:37:15
we're pumping like crazy but I think in
2:37:18
terms of but your reserves usually count
2:37:21
include the shale and all the rest of
2:37:24
the stuff I don't think we have there
2:37:26
just the raw oil as much as Saudi Arabia
2:37:28
I had to look into it I don't know for
2:37:30
sure now that's a great clip I'm keeping
2:37:31
that on standby because the guy is very
2:37:34
clear you saying it right there hey we
2:37:36
can fix this right now you want to be
2:37:38
here oh let's be a hero and by the way
2:37:41
I'm pretty sure that Trump gave
2:37:43
operational control of that reading to
2:37:45
the Brits right he said hey you guys
2:37:48
take care that you guard that we're
2:37:49
not--we're no longer operationally
2:37:51
controlling anything in that area if I'm
2:37:54
not missing possible I think he gave
2:37:56
that he said okay Brits it by the way we
2:37:58
have no real dog in the hunt other than
2:38:01
protecting the damn Saudis for some
2:38:03
reason no even after we know that all
2:38:07
the 9/11 hijackers pretty much came from
2:38:10
Saudi Arabia so on for some reason we're
2:38:12
still protecting there's a reason we
2:38:15
just don't know exactly what it is now
2:38:17
was a shooting in Tokyo can I just stay
2:38:19
with with that for one second because I
2:38:22
can jerk right into the 9/11 Victims
2:38:24
Compensation Fund with a follow-up to my
2:38:28
to what we talked about on the last show
2:38:30
also this is very much right yeah this
2:38:33
is the Jon Stewart yes the Jon Stewart
2:38:36
the debate yes he consistently is called
2:38:40
it the first responders fund when it
2:38:43
hasn't it is the Victims Compensation
2:38:45
Fund it's for much more than first
2:38:47
responders as we're about to learn this
2:38:51
is very much a third rail like talking
2:38:53
about if you talk about Jeffrey Epstein
2:38:55
and how possibly there's an agency it
2:38:57
may be Mossad you know we didn't really
2:39:00
even say it but the you know we're
2:39:03
anti-semites Jew haters you know you
2:39:06
talk about 9/11 your asshole although
2:39:09
many people yeah I think Carly pretty
2:39:12
plus more on Twitter as she got what I
2:39:15
was trying to say and said hey you know
2:39:17
there's so many disasters that take
2:39:19
place you know Katrina or the BP
2:39:22
disaster you know where does the
2:39:24
compensation and for those people well
2:39:26
much quicker much shorter notices and so
2:39:29
why why is this fun not only been in
2:39:32
place according to I got some numbers
2:39:35
here follow-up numbers according to the
2:39:40
Los Angeles Times the fund has already
2:39:44
paid out thirty eight billion dollars to
2:39:47
9/11 victims someone else reminded me
2:39:50
that back in the day this was also
2:39:53
quietly called the airline bailout fund
2:39:56
the reason for that is if you took money
2:40:00
from the victim's compensation fund you
2:40:03
signed away your right to sue the
2:40:05
airlines or participate in any action
2:40:09
suit against and the airlines not
2:40:11
everyone took the the Victim
2:40:12
Compensation Fund money and did sue the
2:40:15
airlines they've been billions has been
2:40:17
paid out but the fear was amongst other
2:40:19
things that you needed to have this fund
2:40:22
in place otherwise the airlines would
2:40:24
have been bankrupted and and they might
2:40:27
not have ever gotten out of that hole
2:40:29
which would have been another
2:40:30
issue four I guess our national security
2:40:32
and our transportation security etc hate
2:40:36
all you want on me I'm just trying to
2:40:38
give you some facts with two short
2:40:42
pieces of testimony that are pertinent
2:40:43
to this fund and where the all you've
2:40:47
probably heard on the news is that
2:40:49
asshole Rand Paul just want to get mind
2:40:52
about the fog a little more behind it
2:40:56
let's find out first about the program
2:41:01
okay there are three changes as this is
2:41:04
about to be re-upped and these changes
2:41:07
are extremely important and I think the
2:41:10
reason why some people are questioning
2:41:13
to what end
2:41:15
there have been I think four major
2:41:17
changes for I'm sorry
2:41:19
in the VFC over the last few years from
2:41:22
what Congress saw when it last we
2:41:24
authorized this bill in 2015 and
2:41:27
allocated the seven point three seven
2:41:28
five billion dollars the first is that
2:41:31
the total number of claims that have
2:41:33
been filed has increased significantly
2:41:35
in the first five years of the fund from
2:41:38
2011 to 2016 we had just over nineteen
2:41:42
thousand compensation forms filed in the
2:41:44
last two and a half years we've received
2:41:46
twenty eight thousand more and the
2:41:49
reasons for those I think are three the
2:41:52
first is that there is a significant
2:41:55
increase in the number of claims being
2:41:57
filed on behalf of victims who have died
2:41:59
as a result of their 9/11 related
2:42:02
conditions as we get further away from
2:42:04
the attacks but as the seriousness of
2:42:08
the illnesses become more apparent we
2:42:10
see more and more of these claims at the
2:42:12
end of 2015 we had just six hundred
2:42:15
deceased claims we now have well over
2:42:17
two thousand of them the second thing is
2:42:20
that the number of claimants with cancer
2:42:22
conditions continues to increase we have
2:42:25
found over eighty eight hundred
2:42:27
claimants eligible because of a cancer
2:42:30
condition and we have made over seven
2:42:32
thousand five hundred awards due to
2:42:35
cancers
2:42:36
in 2015 we have seen only have
2:42:38
action of that number and the third is
2:42:41
that we are seeing a substantial
2:42:43
increase in claims filed by the survivor
2:42:45
community those who lived worked or went
2:42:48
to school in the area in the first five
2:42:51
years of the program survivor claims
2:42:53
were just 14% of the awards that were
2:42:56
made now they account for almost 40% of
2:43:00
the claims that are being filed and we
2:43:02
think that's due to two things the first
2:43:04
are the increase in cancer rates and the
2:43:07
second is that the VFC suffered from a
2:43:09
significant information gap in the early
2:43:11
years of the program many many people in
2:43:13
the New York area were under the
2:43:15
assumption that the program was only for
2:43:17
first responders and as we have been
2:43:20
able to do more outreach as the World
2:43:22
Trade Center Health Program has been
2:43:23
able to do more outreach partly because
2:43:25
of the reauthorization of the bill in
2:43:27
2015 we have been able to reach more
2:43:30
people who are sick more people who are
2:43:32
dying and those claims are now coming in
2:43:34
so there's some new information in there
2:43:36
that I was not aware of that cancers are
2:43:40
if you got cancer and I guess you can
2:43:43
then prove it was from 911 but let's not
2:43:45
sue silverstein or anybody who had
2:43:48
asbestos in the buildings you know let's
2:43:50
forget about the give it from the
2:43:52
American people I think that's what the
2:43:54
Los Angeles Times is trying to say is
2:43:56
that even though the initial budget was
2:43:58
seven point six billion they've promised
2:44:00
all this money to people and it's
2:44:02
totaling up to 38 I think it's higher
2:44:05
than that billion dollars the the the
2:44:09
way that they calculate a lot of these
2:44:11
it's also it's I guess it's fair but is
2:44:14
it really calculate these payments so if
2:44:18
you were a fireman and you died on 9/11
2:44:24
you were making thirty five thousand
2:44:27
maybe forty thousand dollars a year they
2:44:29
will calculate what you mitt would have
2:44:31
made at that same level over your
2:44:33
lifetime adjusted for inflation and
2:44:34
that's your that's the payment to your
2:44:36
widow your descendants if you were a
2:44:39
hedge fund manager you already
2:44:42
understand what happens some of those
2:44:43
people some widows and orphans received
2:44:46
a seven eight million dollars
2:44:49
or a life but because that life
2:44:52
apparently was making more money it's
2:44:54
worth more than another life that went
2:44:56
in on almost slave wages to go and save
2:44:58
people so by itself that I can
2:45:01
understand where there's anger from Jon
2:45:02
Stewart but it may be a middle Oh
2:45:03
misdirected and they think it's unfair
2:45:05
of him to focus it only on the first
2:45:07
responders so the second clip short one
2:45:10
here is how many do they expect to enter
2:45:14
the program because it is now open-ended
2:45:16
as it stands that's why Rand Paul is
2:45:18
saying hold on a second how are we going
2:45:21
to pay for this because it specifically
2:45:22
states in the bill this does not fall
2:45:24
under the pay and go system and the
2:45:27
pay-go system says if you are going to
2:45:30
pay something out you have to show where
2:45:32
the money is going to come from right
2:45:33
away that pretty much means you can have
2:45:36
to scrap something else and that is
2:45:38
excluded from this by law in the
2:45:41
language of the law so how big can I get
2:45:43
how many more people do you think would
2:45:44
be at risk of developing 911 related
2:45:46
illnesses including cancers in the next
2:45:49
25 to 50 years and is it possible to
2:45:52
know the exact number of people who
2:45:54
develop illnesses at this point in time
2:45:55
it's not possible to know the exact
2:45:58
number but based on the rates that are
2:46:00
increasing there are going to be 10 to
2:46:03
20,000 more cancers I would estimate
2:46:05
into 20,000 more cancers yes most other
2:46:08
diseases plus other diseases and look as
2:46:11
we heard about sarcoidosis which is a
2:46:14
fairly rare disease but is common in
2:46:16
World Trade Center exposed individuals
2:46:19
we're going to see folks who have lung
2:46:24
diseases that may require lung
2:46:26
transplants there have already been a
2:46:28
number of individuals in the World Trade
2:46:30
Center health programs that have
2:46:31
required lung transplants due to
2:46:33
scarring of the lungs from the glass and
2:46:37
the concrete and everything else that
2:46:39
caused a reaction in their lungs so
2:46:41
there's an order of magnitude you said
2:46:42
what about 3040 thousand maybe you know
2:46:45
what it's hard to predict but based on
2:46:47
the rates and the number of folks that
2:46:49
were exposed it that numbers is accurate
2:46:53
there you go you're paying for it
2:47:00
seem so seem so I find it to be yet I
2:47:04
didn't know it was so broad well I
2:47:07
didn't know that anyone who can get you
2:47:09
on Stuart gets cancer 20 years later can
2:47:12
still claim that it's 911 related I
2:47:14
don't know
2:47:15
cancer rates are on the rise is all I've
2:47:16
heard across the across America across
2:47:19
the world so we'll pay for it with that
2:47:22
somehow no health care changes could
2:47:27
make a difference in that regard that I
2:47:29
think that may be taken into account so
2:47:32
there's two clips they have left to have
2:47:33
the final clip well they do have this
2:47:36
mass shooting clip that's another
2:47:37
example of a Democracy Now Playing a
2:47:39
story nobody else plays or at least an
2:47:41
element of a story nobody else plays but
2:47:42
then again they don't go into it so I
2:47:44
have no idea what the hell's going on in
2:47:48
guys no mass shooting in Kyoto Japan 33
2:47:53
people were killed Thursday after a man
2:47:55
burst into an animation studio doused
2:47:58
the three-story building with a
2:47:59
flammable liquid and set it on fire
2:48:01
police arrested a 41 year old man after
2:48:05
the arson attack on the Kyoto Animation
2:48:06
company witnesses reportedly heard the
2:48:09
suspect shout they stole my ideas and
2:48:12
they copied my novel as police arrested
2:48:14
him if convicted the man could face the
2:48:16
death penalty
2:48:17
well that's the first I'm hearing of
2:48:18
that yeah I didn't know that he was
2:48:21
claiming copyright yeah nobody else
2:48:25
reported that they just reported that
2:48:27
some maniac oh it's good gun control Wow
2:48:31
huh yeah but now there's no details
2:48:34
what's the details something somebody
2:48:35
must talk to this guy what did he what
2:48:37
specifically did they steal
2:48:40
I don't know there's no reporting there
2:48:44
it's well I guess it's kind of worth it
2:48:50
would have been knows nice to know I
2:48:52
guess I'm just maybe somebody'll come up
2:48:55
with something somewhere somehow so you
2:48:57
have any more clips cuz I do have one
2:48:59
finishing clip I think we're up to show
2:49:00
nicely I'm ready to rap if you are sir
2:49:02
so this is Walter Cronkite in 1969 it's
2:49:06
kind of an inspiration at least to me
2:49:08
this is an inspirational commentary cuz
2:49:10
we didn't talk about the moon shot today
2:49:11
because it's not really something we've
2:49:12
talked about it before and it was a
2:49:14
celebration you know everyone made a big
2:49:16
deal so I found this one clip that I
2:49:18
thought was you know it was it addresses
2:49:20
concerns it's got an element of futility
2:49:23
that I think applies to you in
2:49:25
particular and I just found it very
2:49:28
inspirational because this is a 1969
2:49:31
this is Walter Cronkite talking about
2:49:34
the the late takeoff of the moon shot
2:49:38
the Saturn 5 rocket reporting now from
2:49:42
CBS News Apollo headquarters at Kennedy
2:49:44
Space Center correspondent Walter
2:49:46
Cronkite they resumed the countdown on
2:49:50
schedule two minutes after the hour
2:49:52
which puts them three hours at that time
2:49:55
and 30 minutes 3 hours and 30 minutes
2:49:57
from launch time 9:30 2 a.m.
2:49:59
ever since they first rolled this is a
2:50:02
Saturn 5 out of the Vehicle Assembly
2:50:04
Building right by us here at the press
2:50:07
site three miles from the launch pad put
2:50:09
on that big launcher to take it out to
2:50:11
the pad things have gone exceedingly
2:50:13
well but with the flight of Apollo 11
2:50:16
now there have been only one or two
2:50:18
small glitches oh thank you it was the
2:50:26
adrenaline shot I needed to come back
2:50:28
and do it all over again on Thursday
2:50:31
1969 the first glitch bullcrap statement
2:50:35
by Walter Cronkite thank you very much
2:50:38
nailed it
2:50:40
it's just a glitch who cares that will
2:50:44
do it for today's program episode 1157
2:50:49
of the best podcast in the universe
2:50:51
proud to bring that to you and to do it
2:50:54
all over again on Thursday please
2:50:56
remember us and support the program at
2:50:59
Dvorak org slash na so we don't go the
2:51:03
way of Mad Magazine I'm coming to you
2:51:06
from the frontier of Austin Texas FEMA
2:51:08
region number six and all the
2:51:09
governmental maps Here I am saying in
2:51:13
the morning everybody I'm Adam curry
2:51:14
from northern Silicon Valley I'm John C
2:51:17
Dvorak coming up next on no agenda
2:51:19
stream we've got Hogg Story number 32
2:51:22
eating birthday in celebration of Dame
2:51:24
Carolyn and thank you to our end of show
2:51:27
mixers Gallup Tom Starkweather and
2:51:30
circumference until Thursday everybody
2:51:36
and such
2:51:39
[Music]
2:51:47
boria in time with the zephyr or what we
2:51:50
started the whole thing just as this
2:51:53
effort here is the 10 car train - for
2:51:55
some reason I kept seeing these 10 car
2:51:58
trains for the last week and I'm
2:52:00
thinking as I've been looking at these
2:52:02
trains again why do they take our
2:52:03
syllabus and they're always been to me 1
2:52:06
2 3
2:52:10
it's not early I'm hearing it honking I
2:52:13
think it's gonna blow me I can hear the
2:52:16
honk from every building on these days
2:52:19
[Music]
2:52:28
right on
2:52:32
whoo-hoo everything me the Judy and
2:52:34
there goes
2:52:36
[Music]
2:52:38
going
2:52:41
December
2:52:45
for thank goodness there we go home art
2:52:48
she is a really pretty
2:52:50
[Music]
2:53:05
dislike you
2:53:17
[Music]
2:53:24
[Music]
2:53:27
hello
2:53:29
[Music]
2:53:34
she has gorgeous features yes to all of
2:53:43
your friends all of your neighbors but
2:53:45
everyone come here if you can't just
2:53:50
stay cool get some fans to stay cool
2:53:53
some people just don't know where to
2:53:55
turn the state for the summer scorcher
2:53:56
turning downright dangerous
2:53:58
look at these temperatures triple digits
2:54:02
and in some places the scorching heat
2:54:04
and humidity are going to get worse on
2:54:05
the hottest day of the year people are
2:54:07
not the only ones feeling this heat so
2:54:09
our utilities which means well you're
2:54:10
gonna feel the heat today the feel like
2:54:12
temperature and half the country will be
2:54:13
over 100 degrees no relief or 190
2:54:17
million of us this weekend I think right
2:54:18
now we're gonna challenge our
2:54:20
infrastructure unlike anything we've had
2:54:21
in years over the next few minutes I'm
2:54:24
gonna see what happens to the human body
2:54:26
as it starts heating up wear
2:54:28
loose-fitting clothing
2:54:30
water on your face works cold showers
2:54:33
called back in this hot weather
2:54:34
continues who's going to keep all the
2:54:37
caps and the hole in the future it's
2:54:40
going to be hotter and a Wilson and we
2:54:43
do need to
2:54:48
[Music]
2:55:02
[Music]
2:55:18
mofo Dvorak org slash and a will have
2:55:24
more unprecedent Trump's latest racist
2:55:26
remarks
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