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