Arms Race (Finale)

Should AI be stopped? To find the answer, we conclude our story on the rise of game-playing AI systems – and how they spawned an artificial intelligence arms race. We also reveal the identity of Antonio Paine, perhaps the leading expert (and whistleblower) on artificial intelligence.

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Episode Transcript

Speaker 1 (00:03):
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(00:24):
and click join in the top menu. Help us save
America one story at a time.

Speaker 2 (00:32):
This episode is based on actual events. However, certain elements, characters,
and situations have been fictionalized for the purposes of storytelling.

Speaker 1 (00:42):
Previously on Red Pilled America.

Speaker 2 (00:44):
IBM's new computer was far more advanced. They called it
Deep Blue.

Speaker 1 (00:51):
Now we believe with advances in technology, it's just a
matter of time before Deep Blue can be Garry Kisperoff.

Speaker 2 (00:58):
Finally game day arrived.

Speaker 3 (00:59):
The first game, Gary just completely outplayed the computer in
a Beauty.

Speaker 4 (01:04):
To You Tonight.

Speaker 3 (01:06):
Gary started shaking his head, oh no, with.

Speaker 2 (01:08):
The computer playing a completely different style. In game two,
Casparov began suspecting foul play.

Speaker 5 (01:15):
If I'm reading you correctly, do you think there may
be some kind of human intervention on the part of this.

Speaker 2 (01:21):
Game, Gary Casparov no longer believed he was playing just
a computer.

Speaker 1 (01:29):
I'm Patrick Carelchi and.

Speaker 2 (01:31):
I'm Adriana Cortes.

Speaker 1 (01:32):
And this is Red Pilled America, a storytelling show.

Speaker 2 (01:36):
This is not another talk show covering the day's news.
We're all about telling stories.

Speaker 1 (01:42):
Stories. Hollywood doesn't want you to hear stories.

Speaker 2 (01:45):
The media mocks stories about everyday Americans that the globalist ignore.

Speaker 1 (01:50):
You can think of Red Pilled America as audio documentaries,
and we promise only one thing, the truth. Well, welcome
to Red Pilled America. We're at the third and final

(02:13):
installment of our series of episodes entitled arms Race. If
you haven't heard Part one and two, stop and go
back and listen from the beginning. We're looking for the
answer to the question should AI be stopped by telling
the story of the rise of game playing AI systems
and how they spawned an artificial intelligence arms Race. So,
to pick up where we left off in nineteen ninety seven,

(02:34):
IBM's Deep Blue stun Gary Kasparov and the world by
winning game two of their six game match. To Casparoff's astonishment,
the computer played differently than game one. The lightning fast
computer took an extraordinarily long time to make a decision,
then ultimately made a human like move. Casparov suspected, while

(02:54):
in its heavily guarded room, IBM's computer was getting some
help from a real life grandmaster. In other words, Casparov
believe the IBM TI team was cheating. After the third
game ended in a draw, Kasparov started to get vocal
about his hunch.

Speaker 6 (03:13):
Opinion that one three the chemical computer games two was
slightly well.

Speaker 1 (03:20):
I think we'll leave it at that, and just like that,
the gloves were off. Kasparov began demanding the computer logs
for game two to see how Deep Blue made some
of its inexplicable decisions, but the IBM team refused to
hand them over. The reigning world champion started to become

(03:43):
obsessed with the issue, and it no doubt impacted his play.

Speaker 7 (03:47):
The score is now even in the man versus machine
chess match. Last night's game four between world champion Garry
Kasparov and the IBM computer Deep Blue ended in a draw.
After five hours, Kasparov admitted he was too tired to
go want.

Speaker 1 (04:02):
With the match all tied at two to two, Kasparov
continued to press for the computer logs, but even as
game five arrived, the IBM team stood their ground, refusing
to provide the printouts.

Speaker 8 (04:14):
IBM's chess playing supercomputer Deep Blue out maneuvered the world's
highest ranked human player, Gary Kesparov, into accepting a third
consecutive draw. The outcomes stunned the experts. The draw came
after forty nine moves and just over four hours of
often intense play that kept many spectators on the edges

(04:34):
of their seats.

Speaker 1 (04:36):
After game five, the tension in the press room was palpable.
Kasparov's accusations started to chip away at the IBM team.
You see, the human chess champ had become kind of
a folk hero, mankind's last hope against the machine. When
Kasparov entered the press room, he was welcomed with a
standing ovation. On the other hand, IBM, now viewed as

(05:01):
the villain, entered the room to a sprinkling of booze.
The corporation wanted to tamp down the building heat, so
they buckled, agreeing to give the computer logs to an arbitrator.
At the end of the match. Everyone in the pressroom

(05:26):
could see that Kasparov seemed exhausted, even defeated.

Speaker 4 (05:30):
Tomorrow, I will have to face another difficult challenge.

Speaker 9 (05:33):
Probably the only.

Speaker 4 (05:34):
Good thing is not to resign in advance. Here is
being almost said, the matches all over. It is a big,
big game Smorrow. Are you gonna be watched tomorrow?

Speaker 10 (05:43):
Or are we going to be looking away?

Speaker 4 (05:45):
With the black pieces not side this match up? I'll
cime the b best.

Speaker 11 (05:48):
Movies, one game each and three draws going into the
final match, which the Blue has the advantage of making
the first move. The future of humanity is on the
line now the weather.

Speaker 1 (06:00):
As Game six kicked off, it became clear that Gary
Kasparov was headed for the unthinkable.

Speaker 3 (06:06):
Another disgusting move by Gary.

Speaker 12 (06:08):
Again he's being faced with a situation where he has
to react.

Speaker 13 (06:13):
What are we missing something on the chessboard?

Speaker 5 (06:15):
Now that Kasparov sees, he does not look.

Speaker 14 (06:18):
He looks disgusted.

Speaker 3 (06:21):
Whoas off?

Speaker 4 (06:23):
After the four has resigned.

Speaker 1 (06:29):
After just nineteen moves, Casparov walked away from the chessboard.
IBM's Deep Blue won the game and the match.

Speaker 13 (06:38):
Call it a blow against humanity. After six games over
nine days, Deep Blue, the IBM computer, beat Gary Kasparov,
considered to be the best chess player in the history
of the game.

Speaker 1 (06:50):
In the postgame press conference, Casparov was bitter.

Speaker 4 (06:53):
The match was the world champion.

Speaker 15 (06:57):
That's it.

Speaker 4 (06:58):
I think there are very good and very profound prisons
for such a result. I think the competition just started.
I made one made mistake. Before this match, there was
nothing to do about thorough.

Speaker 6 (07:14):
Investigation of computer potentials chess. There was one zeal to
be gearing a form, and while the big corporation was
unlimited resources, would like to do so.

Speaker 4 (07:29):
There are many ways to achieve the results.

Speaker 1 (07:31):
IBM stock Rosa reported fifteen percent after the match it
was an undeniable win for the computer company. Casparov challenged
Deep Blue to a rematch, but IBM declined. Whether Casparov's
suspicions were valid or not, the world would never know.
IBM reportedly never provided the computer logs. Nevertheless, the match

(07:54):
was no doubt an inflection point for humanity. A game
once thought to be the proving grounds for intelligence now
had a computer as the effective raining World champion.

Speaker 2 (08:04):
Kasparov's loss sent shockwaves throughout the world. Many began to
argue that machines were now smarter than humans, but one
man saw the entire affair in a completely different light.

Speaker 14 (08:23):
Of course, this was a watershed moment in AI when
in the late nineties IBM's Deep Blue beat Kasparov in
a six game chess match.

Speaker 2 (08:32):
That's Demis Hasabis. At the time that Kasparov lost a
Deep Blue, Demis was a computer science major at the
University of Cambridge. He was also a chess master himself.
Born and raised in London, Demiss began playing chess at
the age of four and quickly showed signs of being
a prodigy. By the time he was ten, he was
already dreaming of going pro Like most chess whizzes, Demist

(08:56):
began to think about how to improve at the game,
which eventually led to the pondering of how to improve
the process of thinking.

Speaker 14 (09:04):
So that leads you to thinking about thinking, how is
your brain coming up with these ideas? Why is it
making mistakes? How can you improve that thought process.

Speaker 2 (09:13):
By the mid eighties, game companies were selling computerized chess games.
They were physical boards where you could actually play the
computer within the board. They weren't nowhere near as strong
as IBM's chess playing computers, but it gave players a
chance to practice without a human opponent. Demis bought one
of these computerized chess boards, and.

Speaker 14 (09:32):
I remember thinking this amazing. You know, how has someone
programmed this chess board to play chess.

Speaker 2 (09:37):
Demis had already bought his first computer with some chess
tournament winnings, so he began teaching himself how to program chess.

Speaker 14 (09:45):
Has been hand in hand with AI from the beginning
of the whole field, right, so, I think every AI practitioner,
starting Withturing in Claude Shannon and all those, the sort
of forefathers of the field, tried their hand at writing
a chess program. I sort of realized on an intuitive
level that this computers are kind of special type of machine.
You know, most machines, like cars and planes, they extend

(10:08):
our physical capabilities. You know, cars allow us to move
fast and then we can run. Planes allow us to fly.
But I think computers do that. But in the realm
of our minds, they really extend the capabilities of the brain.

Speaker 2 (10:20):
He'd eventually enter the University of Cambridge to study computer
science in nineteen ninety five, so by the time Deep
Blue best at Kasparov two years later, Demis had been
thinking about computers and artificial intelligence for over a decade,
and he had a contrarian view on the results of
that match.

Speaker 14 (10:37):
But I remember coming away from that being more impressed
by Kasparov's mind than I was by Deep Blue, Because
here was Kasparov with his human mind. Not only could
he play chess more or less to the same level
as this route of a calculation machine, but of course
Kasparov can do everything else humans can do, ride a bike,
talk many languages, do politics, all the rest of the
amazing things that Kasparov does.

Speaker 2 (10:58):
As incredible as Deep Blue was at playing chess, it
couldn't do anything else. It couldn't even play the much
simpler game of Tic tac toe. Demis saw IBM's computer
for what it was, a brute force calculator that relied
entirely on human input. In his eyes, the so called
artificial intelligence of the time wasn't that smart. So he

(11:19):
went on a journey to develop a rare combination of
skills that could help him unlock the secrets of machine intelligence.
After graduating from Cambridge, Demis went into what had become
the cornerstone of artificial intelligence development games. In Demis's case,
he became a video game developer in two thousand and four,

(11:40):
Demis created a game called Evil Genius, and his description
of it would give a peek into the mindset of
the creator.

Speaker 14 (11:47):
Basically, the gameplay involves you playing the parts of an
evil genius. So you have your own tropical islands, yeah, absolutely,
and you get to build your own kind of layer
there and you're what you're ultimately trying to do is
build doomsday device and then threat in the world with it,
and then so that they've been to your will.

Speaker 9 (12:02):
And extract as much money out of any money everything that.

Speaker 2 (12:06):
By two thousand and five, Demis hasabis decided to pivot
away from gaming and move back towards the area that
captured his heart and mind when Deep Blue defeated Kasparov,
the world of artificial intelligence. Demis went back to school,
enrolling at the University College of London to pursue a
PhD in cognitive neuroscience. The move was a calculated one.

Speaker 14 (12:29):
And really this was the final part of the puzzle
for me in terms of launching an effort to solve AOI.
So I wanted to gain inspiration from how the brain
solved some of these hard problems of intelligence to come
up with new inspirations for new types of algorithms that
we could implement.

Speaker 2 (12:49):
Demis wanted to bring together his knowledge of computer science
and games with the study of the mind to understand
how to simulate human intelligence, and by doing so, he
followed in the footsteps of the founders of modern artificial intelligence.

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Welcome back to Red Pilled America. In the early nineteen fifties,

(14:29):
the world of computing was in its infancy, and a young,
ambitious mathematician named John McCarthy was forging his path in
this nascent field. After earning his PhD from Princeton University
in nineteen fifty one, McCarthy joined the faculty at Dartmouth College.
It was there that he began to explore the potential

(14:50):
of machines to perform tasks that required human like intelligence.
Around the same time, Claude Shannon, an electrical engineer and
the father of information theory was already making waves with
his groundbreaking work on digital communication. Shannon, who earned his
PhD from MIT in nineteen forty, was a renowned figure

(15:13):
in the world of computer science. He was fascinated by
the game of chess and its potential for exploring machine intelligence,
and wrote a seminal paper on the topic. In nineteen
fifty five, McCarthy, Claude, Shannon, and two others wrote a
proposal for a conference that would bring together the brightest
minds to explore the possibilities of artificial intelligence. Their vision

(15:36):
was ambitious, aiming to explore if machines could be made
to simulate aspects of human intelligence, such as learning, problem solving,
and even creativity.

Speaker 9 (15:45):
The Dartmouth Conference was a seminal event in the history
of artificial intelligence.

Speaker 1 (15:50):
Again, that's Antonio Paine, perhaps the world's top expert on
artificial intelligence.

Speaker 9 (15:55):
Officially known as the Dartmouth Summer Research Project on Artificial Intelligence,
it was held at Dartmouth College in Hanover, New Hampshire,
between June eighteenth and August seventeenth, nineteen fifty six. The
goals of the conference were to investigate ways to make
machines use language form abstractions and concepts solve the kinds
of problems reserved for humans and improve themselves. The organizers

(16:19):
believed that the significant progress could be made if a
carefully selected group of researchers worked together on these problems
over an extended period.

Speaker 1 (16:28):
John McCarthy, Claude Shannon, and their fellow organizers brought together
an extraordinary group of researchers from many different fields, including
computer science, cognitive psychology, organizational theory, mathematics, information theory, and economics.
As the conference unfolded, ideas were exchanged, collaborations were forged,

(16:48):
and the seeds of modern artificial intelligence research were sown.
It was during this conference that John McCarthy first coined
the term artificial intelligence, cementing its place in the scientific lexicon.
The impact of the nineteen fifty six Dartmouth Conference was
far reaching. It sparked a wave of research funding an
innovation in the field of artificial intelligence, laying the foundation

(17:11):
for many of the advances in AI that we take
for granted today. The collaborations and ideas born during that
summer in Hanover, New Hampshire would echo through the decades.
With John McCarthy, Cloud Shannon and their fellow visionaries at
the forefront of a revolution in human machine interaction, a

(17:32):
revolution that IBM would later lead with their chess playing computers.
But like many first movers, IBM would eventually be surpassed
by an evil genius.

Speaker 11 (17:48):
This is Jeopardy, the IBM challenge.

Speaker 9 (17:54):
Here we go, Brad.

Speaker 11 (17:58):
If you're ready make your first choice.

Speaker 16 (18:00):
Let's take alternate meanings for two.

Speaker 2 (18:01):
Hundred eles eleven, IBM's artificial intelligence team graduated from chess
to a much more generalized game, the quiz TV show Jeopardy.
They created a supercomputer they named Watson and challenged two
of the winningest players in Jeopardy history to a three
game match televised over as many days. Whoever accumulated the

(18:23):
most money by the end of the match would be
crowned the winner, and, much like their battle with Gary Kasparov,
the event captured the imagination of the media.

Speaker 4 (18:32):
More on that showdown.

Speaker 10 (18:33):
On one of America's most beloved game shows, Jeopardy, two
human champions going brain to brain with a supercomputer named Watson.

Speaker 16 (18:41):
Call it man versus Machine. Superstar contestants against the supercomputer
Watson was often running.

Speaker 9 (18:47):
Losing to him by one hundredth of a second?

Speaker 3 (18:51):
Watson, who is Michael Phelps?

Speaker 17 (18:53):
Yes, Watson, what is Glass?

Speaker 9 (18:55):
Judgment correct?

Speaker 17 (18:56):
Watson, who is Jean Valjean Correct?

Speaker 2 (18:59):
In the end, IBM's Watson handily defeated the two Jeppard
champions who is Brown?

Speaker 11 (19:04):
Silver?

Speaker 18 (19:05):
And the wave servant?

Speaker 16 (19:08):
Hello?

Speaker 18 (19:08):
Seventeen nine hundred, seventy three and forty one?

Speaker 16 (19:11):
Guy, Well, he'd just seen history made here on k
XA in this afternoon, Watson, the supercomputer wiping the floor
with the two greatest human champions ever to play Jeopardy.

Speaker 2 (19:21):
Like Caasparov versus Deep Blue, Watson's victory was a pivotal
moment for artificial intelligence, but not because it displayed the
future of AI. In hindsight, it punctuated a changing of
the guard. You see, like Deep Blue, IBM's Watson was
programmed to solve a very specific problem winning at Jeopardy.
It couldn't played chess, checkers, or, for that matter, a

(19:44):
million other things. What IBM was missing at the time
was that there was a new movement happening in artificial intelligence,
a movement they themselves helped agnite in their battle with
Gary Kasparov. When Demis Hasabis watched that nineteen ninety seven
chess battle. He was more impressed with the human than
the machine. Kasparov was playing on the level of a supercomputer,

(20:08):
but could do so much more than Deep Blue. Demis
wanted to create a new kind of smart computer that
accomplished more than just one specific task. He wanted to
create an artificial general intelligence or AGI that could actually
learn to solve any problem, not just one game. And
to do so, Demis concluded that the machine would have

(20:30):
to learn on its own through experience. It would have
to adapt to new situations, understand and handle abstract concepts,
and use knowledge to manipulate its environment. While IBM was
celebrating its win on Jeopardy, Demis was busy in London
with his new company he called deep Mind. Fresh off

(20:51):
receiving his PhD in cognitive neuroscience, Demis launched deep Mind
in twenty ten with the pioneer in machine learning named
Shane leg along with another family friendmis U's mission was grandiose.

Speaker 14 (21:04):
And you can think of deep Mind as Apollo program
effort if you like, for neuroscience inspired AI. What we're
instilled in is understanding natural intelligence, so the human mind,
but also recreating that intelligence artificially, and then step two,
we want to use that technology to help us solve
everything else.

Speaker 2 (21:24):
Demis and its Deep Mind team wanted to take a
radical shift from IBM's computer Chess team. Their inspiration was
more Gary Kasparov's Brain than deep blues programming. Instead of
packing a computer with very specific data on a particular subject,
they wanted their machines to learn through experience.

Speaker 14 (21:43):
So all the algorithms that we're insted in building at
deep Mind learn automatically from raw experience. They're not pre
programmed or handcrafted in any way. They learn directly from
the raw data. And they're also general in the sense
that a single set of algorithms or single system can
operate out of the box across a wide range of tasks.
And of course we have an example of such a system,

(22:05):
it's the human mind. But actually most AI around us
is not of this kind. Usually the AI, for example,
out for our phones or cars, or even for various
Internet and games things are actually usually bespoke pieces of
software that have been handcrafted for one particular task. They
don't learn or adapt, and they're not flexible. So we

(22:26):
call the type of AI we work on artificial general
intelligence to distinguish it from this normal type of AI.

Speaker 2 (22:33):
The team at deep Mind applied what is known about
how the human brain operates and applied those processes to
their algorithms. They wanted their AI to learn from the
environment that they were in with very little priming by humans.
Once they created their AI algorithms, they needed to start
testing their abilities, so they of course turned to games.

Speaker 14 (22:54):
So what we started off with was actually Atari games
from the eighties, which are really the first iconic platform
that had a lot of very popular, challenging games on it.

Speaker 2 (23:07):
The vintage Atari game system had dozens and dozens of games,
iconic ones like Space Invaders in Breakout, where the player
uses a paddle in the shape of a short thick
line and hits a digital ball against a wall of
bricks until all of the bricks are gone. Well. Demis
and his team connected their AI algorithms to the Atari
game system as if it were a player, and they

(23:29):
gave their algorithms access to the game controller and the
game screen, providing their AI visual sensors so that they
could see the raw pixels from the screen, and that
was it.

Speaker 14 (23:40):
It doesn't know anything about what it's controlling. It doesn't
know what the aim of the game is. It doesn't
know how to get points. All it's being told is
that it needs to. Its goal is to maximize the score.
Everything else is learned from scratch.

Speaker 2 (23:55):
They made their algorithms play dozens of different games to
ensure that they weren't being optimized for one specific game,
as Deep Blue for chess. At first, their AI bots
had no idea what they were doing. When they played
Space Invaders. For example, they randomly moved their ship around,
shooting wildly. They lost their three lives almost immediately. The

(24:17):
team then allowed their AI bot to continue playing Space
Invaders overnight.

Speaker 14 (24:21):
Now, if you leave the machine training overnight and you
come back the next day, the machine now is superhuman
at the game. So every single shot it fires hit something,
it can't be killed anymore. It's worked out that the
pink mothership at the top of the screen is worth
a lot of points. It does these amazing accurate shots,

(24:43):
and you can see that the model it's built of
the world is extremely accurate.

Speaker 2 (24:47):
Deep Minds algorithms were learning how to play the game
all on their own. Word of their work began to
spread and doors to some of the top minds in
technology began to open for demisisabus, including the door to Elon.
Demis made a trip to Elon's SpaceX rocket factory in

(25:11):
a suburb of Los Angeles. While there, the two talked
about each other's missions. Elon told Demis that he was
working on the most important project in the world, interplanetary colonization.
Demis snickered at the idea. My artificial general intelligence work
is the most important work on the planet, claimed Demis.
Elon thought he'd want up the AI entrepreneur. That's why

(25:35):
colonizing Mars is so important. Elon snapped back. Mars will
be a safe haven if AI ever turns on mankind.
That's when Demis caught Elon by surprise. AI will simply
follow humans to Mars. Demis responded. The exchange caught Elon's attention.
The SpaceX founder was already concerned about the threat AI

(25:58):
pose to humanity. In his view, artificial intelligence could go
rogue like Skynett did in the film The Terminator, looking
to dominate, perge, or even chase humans off the planet.
Demis's comments only amplified Elon's concerns over AI, so the
tech billionaire channeled Michael Corleone's philosophy keep your friends close,

(26:21):
but your enemies closer. Elon wanted to keep an eye
on the development of artificial intelligence, so he became an
investor in deep Mind. Now at the time, Elon wasn't
the only tech tycoon keeping tabs on artificial intelligence. His
good friend saw AI as the holy grail for search engines.

Speaker 19 (26:40):
Artificial intelligence would be the ultimate version of Google.

Speaker 2 (26:44):
That's Google's co founder Larry Page. In a two thousand interview.
At the time, Larry was asked what was in the
future for Google, and his first thought was artificial intelligence.

Speaker 19 (26:55):
So we had the ultimate search engine, and it would
understand everything on the web. Who would understand you, know
exactly what you wanted, and it would give you the
right thing. And that's obviously artificial intelligence. You would be
able to answer any question basically because almost everything is
on the web right and so we're nowhere near doing
that now. However, we can get incrementally closer to that,

(27:16):
and that's basically what we work on.

Speaker 2 (27:18):
By two thousand and seven, Larry Page was pushing Google's
AI research in the direction of a deep blue type model,
where brute force calculations were the focus.

Speaker 19 (27:27):
My prediction is that when AI happens, it's going to
be a lot of computational and not so much clever
black whiteboard kind of stuff. Clever algorithms, but just a
lot of computation.

Speaker 2 (27:38):
But Larry Page was working within an old framework. Deep
Blue was a purely computational machine, using brute force calculations
to find answers to the best chess move. However, artificial
intelligence that could serve a purpose like the human mind
actually needed clever algorithms that simulated the way the mind operates,
and that's what Demissabis was doing at deep Mind. Within

(28:06):
a few years of launch, it became clear that deep
Mind had bet correctly and was leading the way in
AI development. So Larry Page did what men in his
position do. On January twenty six, twenty fourteen, he bought
deep Mind for five hundred million dollars. Google now owned
the most promising artificial intelligence firm in the world. Larry

(28:29):
Page commented on deep Mind shortly after the purchase, well.

Speaker 3 (28:34):
I think, for me, this is kind of one of
the most exciting things I've seen in a long time.
The guy who started this company, Demas, has a neuroscience
hand a computer science background, and he went back to
school to get his PhD to study the brain. And
so I think we're seeing a lot of exciting work
going on that sort of crosses computer science and neuroscience

(28:55):
in terms of really understanding what it takes to make
something smart.

Speaker 2 (28:59):
Google's purchase of deep Mind spoot Elon musk. Larry Page
and him were actually good friends. They'd known each other
going back to the late nineties, before Google had received
a dime of venture capital funding. Elon would sometimes spend
the night at Larry's house when visiting Silicon Valley. Elon
would later recall his time with Larry Page.

Speaker 12 (29:23):
I talked to him late in the night about AI safety,
and at least my perception was that Larry was not
taking AI safety seriously enough. He really seemed to be
once sort of digital superintelligence, basically digital god, if you
will as soon as possible. He's made many public statements
over the years. The whole goal of Google is by

(29:45):
what's called AGI artificial general intelligence or official superintelligence.

Speaker 2 (29:48):
In other words, a digital superhuman that could think on
its own without human input. The potential dangers of an
AGI machine monster frightened Elon, and he began to ring
the alarm whenever he was given the opportunity.

Speaker 12 (30:06):
I think we should be very careful about artificial intelligence.
If I were to guess at what our biggest existential
threat is, it's probably that. So we need to be
very careful with the artificial intelligence. Increasingly inclined to think
that there should be some regulatory oversight, maybe at the
national and international level, just to make sure that we

(30:27):
don't do something very foolish. I mean, with artificial intelligence,
we are summoning the demon. You know all those stories
where there's the guy with the pentagram in the holy
water and he's like, yeah, you're sure he can control
the demon? Then work out.

Speaker 2 (30:40):
The issue was obviously weighing heavily on the SpaceX founder.
Then one night at a glamorous Napa Valley party, Elon
and Larry Page got into a heated debate about AI, and.

Speaker 12 (30:51):
Then at one point I said, well, what about you
know who we're going to make sure humanity is okay
here And then he called me a specist, and so
I was like, okay, that's it, Yes, I'm a specist. Okay,
you got me.

Speaker 19 (31:02):
So that was the last roll.

Speaker 12 (31:04):
At the time. Google had a quite deep Mind, and
so Google deep Mind together had about three quarters of
all the AI talent in the world. They obviously had
a trans amount of money and more computers than anyone else.
So I'm like, okay, where about unipolo world here where
there's just one company that has close to monopoly on
AI talent and scaled computing and a person who's in

(31:26):
charge doesn't seem to care about safety. This is not good.
So then I thought, what's the furthest thing from Google?
Would be like a nonprofit that is fully open because
Google was closed for profit.

Speaker 1 (31:38):
To fight the AI of a big, monolithic corporation like Google,
Elon thought the solution was to democratize AI, to create
a nonprofit, transparent artificial intelligence company so that people could
see how the technology was developing. So Elon gathered a
group of high tech heavyweights, including Peter Thiel and Sam Altman,

(31:59):
who at the time led the influential tech startup funding
firm hy Combinator. On December eleventh, twenty fifteen, Elon founded
a nonprofit artificial intelligence company called Open Ai, and Elon
pledged and reported one billion dollars to fund the operation.
The AI arms race had begun Elon sat on the

(32:23):
board of open Ai with Sam Altman and a few
AI experts who'd run the company, and shortly after its launch,
Elon hinted that Google was the reason he launched the project.

Speaker 12 (32:34):
I think if we fret to say that, like, not
all AI futures of a nine and so, if we
create some digital superintelligence that exceeds us in every way
by a lot, it's very important that that be be nine.
And so actually, with a few others, I created open ai,
which is an AI it's a nonprofit. Actually there's no

(32:55):
I think the governance structure is important. So you want
to make sure that there was not some fiduciary duty
to generate profit off of the AI technology that's developed.
So open ai has a very high sense of urgency
and the talent. I think that the people that have
joined are are really really amazing. And the intent with
open ai is to democratize AI power, and so I

(33:19):
think it's important if we have this incredible power of
AI that if not be concentrated in the hands of
a few and potentially lead to a world that we
don't want. I don't know a lot of people who
love the idea of delivering under a.

Speaker 17 (33:30):
Despot, and the despot would be the computer for the.

Speaker 12 (33:33):
People controlling the computer.

Speaker 10 (33:35):
And do you worry specifically about any of these companies
I mentioned, who've all seemed to now kind of be
pivoting towards this as the battleground in the next ten years.

Speaker 12 (33:47):
I wouldn't name a name, but there is only one.

Speaker 10 (33:49):
There's only one you're worried about.

Speaker 1 (33:51):
There's only one, and that one company was Google. To
effectively compete against the search behemoth, open ai needed computing
power and data, lots and lots data, so they aligned
with one of the largest tech firms that the world
has ever known, Microsoft, and in November twenty sixteen released

(34:12):
interview Sam Altman answered Microsoft's question on why he chose
their company to partner with.

Speaker 20 (34:18):
I think Microsoft is the large technology company most aligned
with us in terms of the goal of democratization to
AI technology, and this is our most important goal. I
think we are going to create an incredibly powerful technology
and that should belong to everyone in the world. We
don't want to see that concentrated within a single government,
certainly not a single company. And Microsoft's commitment to democratization

(34:41):
of AI technology, it was our number one reason.

Speaker 21 (34:47):
Number two is that.

Speaker 20 (34:49):
We thought, after we looked at everything, Microsoft had the
strongest technology platform, hardware, and software, that you have very
significant computing power.

Speaker 2 (35:02):
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Speaker 1 (35:17):
Welcome back to Red Pilled America. As Elon's Open AI
was just getting started, Google's Deep Mind was doing this
seemingly impossible. The company had trained its artificial intelligence algorithm
to play the board game Go again Antonio Paine.

Speaker 9 (35:35):
The Game of Go is an ancient board game that
originated in China over two thousand, five hundred years ago.
It is one of the oldest continuously played board games
in the world and has a rich history deeply rooted
in Chinese culture and philosophy. Go is played on a
grid of intersecting lines, typically nineteen by nineteen, with two

(35:57):
players taking turns to place black and white stones on
the board's intersections. The objective of the game is to
surround more territory than one's opponent. Despite its simple rules,
Go is known for its strategic depth and complexity, resulting
in an immense number of possible board configurations.

Speaker 1 (36:13):
Checkers has tended the power of twenty possible outcomes. Chess
has tended the power of forty outcomes. Go, on the
other hand, has tended the power of three hundred and
sixty possible outcomes. This is an unimaginably huge number. The
total number of atoms in the known universe is estimated
to be ten to the power of eighty, so it

(36:38):
was thought that computers were far from being able to
exhaustively evaluate all possible moves effectively enough to beat a human.
It took teams of computer engineers decades to get to
a deep blue that mastered the game of chess, so
getting AI algorithms to master Go was thought to be
a distant dream.

Speaker 9 (36:57):
It was considered too complex for machines to master due
to its vast number of possible moves.

Speaker 1 (37:02):
Google's d Mind trained its algorithms to play the game.
They called their creation Alpha Go, and in March twenty sixteen,
they faced off with South Korean go player Lee Sadal.
Lee had eighteen international championships to his name, yet Google's
Alpha Go crushed the human champion.

Speaker 19 (37:21):
I wonder ifull we have a resignation here.

Speaker 9 (37:23):
It could be that Lisa always resigned.

Speaker 19 (37:25):
Yeah, getting word, Lee has resigned. Big congratulations to the
Alpha Go team.

Speaker 1 (37:34):
A year later, Alpha Go defeated the Chinese world champion.

Speaker 9 (37:38):
The Google computer program has once again beaten a world
champion in Go, an ancient Chinese game similar to chess.

Speaker 1 (37:45):
To China, Google coming in and beating their champion felt
like an attack on the country's national pride. After their
champion lost in game one, they banned further live streams
of the event, then vowed that China would be the
world's leader in artificial intelligence by the year twenty thirty.
Later that summer, Elon's open Ai was making waves as well,

(38:07):
similar to deep Mind. To test their progress, the open
ai team connected their algorithms to video games and had
them play thousands of games against themselves, including a popular
esports video game called Doda and the context of artificial intelligence.
Mastering Doda is considered far more complex than either chess

(38:28):
or Go, but in the matter of just a few months,
their bots were winning pros in the video game O.

Speaker 22 (38:38):
Hi, give up, giving up, guys, Let's give a round
of close open Ai have beaten the world's best Dota
players with defining.

Speaker 12 (38:49):
This thing, accomplished in like six months what I've like
set out to do for eight years.

Speaker 1 (39:03):
Regardless of their success, By early twenty eighteen, Elon reportedly
believed that open ai was falling fatally behind Google. To
solve the problem, he proposed a solution to the board.
He would take control of open ai and run the
company himself, but his co founder Sam Altman didn't like
the idea, and neither did the company's chief technology officer,

(39:25):
Greg Brockman. Elon attempted to take the company over, and
why not, He'd already funneled one hundred million dollars of
his own money into the venture, but the board of
directors vetoed his proposal, so in February twenty eighteen, Elon
stepped down from the board. At the time, open Ai
made the parting sound amicable. They announced that Elon's Tesla

(39:48):
team was producing artificial intelligence as part of its self
driving car initiative, so his position on the board represented
a simple conflict of interest. Elon ceased further investment in
open Ai, and within a month of his exit, he
stepped up his warnings on the dang years of artificial intelligence.

Speaker 23 (40:04):
I'm very close to the cutting edge in AI, and
it scares the hell out of me. It's capable of
vastly more than almost anyone knows, and the rate of
improvement is exponential. This is a case where you have
a very serious danger to the public. I think the
danger of AI is much greater than the danger of
nuclear warheads by a lot. And nobody would suggest that

(40:26):
we allow anyone to just build nuclear warheads if they want.
That would be insane, and mark my words, AI is
far more dangerous than nukes far.

Speaker 1 (40:37):
Within a matter of six months, one of Elon's fears
started to ring true. In the summer of twenty eighteen,
Google's AI team announced an extraordinary breakthrough. They created a
new AI system based on a neural network architecture again
Antonio Pain.

Speaker 9 (40:54):
Neural networks are a type of computational model inspired by
the structure and function of biological neural networks found in
the human brain. They are designed to recognize patterns, learn
from data, and make predictions or decisions.

Speaker 1 (41:08):
Google called it transformer, and it allowed their AI to
endlessly improve. Their breakthrough technology gave them the power to
process extraordinary amounts of data efficiently similar to the human brain.
It was a vast improvement on the way AI algorithms
modeled and translated language, and exponentially improved its ability to

(41:29):
answer questions, which is the holy grail for search engines.
As Elon suspected OpenAI was falling fatally behind Google, the
team at OpenAI must have realized it as well, because
it made an eyebrow raising decision. In March twenty nineteen,
OpenAI announced that Sam Altman was becoming CEO of the company.

(41:50):
It also announced that it was creating a for profit
entity to give it the flexibility to raise more money
to pivot to this new transformer AI model. Then a
few months later, OpenAI expanded their relationship with one of
the biggest for profit ventures in history, a.

Speaker 15 (42:09):
Right, Microsoft going all in on AI, the world's biggest
public company, announcing an investment of one billion dollars in
Elon Musk's open Ai to build artificial intelligence that can
tackle complex tests.

Speaker 1 (42:22):
It became clear to anyone paying attention what Microsoft was
intending to do with the AI company Elon co founded.

Speaker 24 (42:30):
When's the last time you searched or originated in searched
on bing Microsoft hoping to change that.

Speaker 1 (42:35):
Here, if Microsoft could develop its own question in answering
AI system, it could catch up with Google Search. For
about three years, the AI arms race was quietly raging
in research labs at Google and Open Ai. Based on
this new transformer architecture. Both were secretly making major advancements

(42:59):
that became clear in the case of Google when one
of their engineers came out from the shadows to raise
a red flag.

Speaker 5 (43:06):
So something that really should be in a sci fi film.
Can computers feel? Are they learning already? And if they
are one of the consequences of their self learning? All
of those questions provoked by the Google engineer Blake Lemoy.
He worked on the Tech Giants artificial intelligence technology and
recently went public with his claim that he now believed

(43:27):
Google's chatbot program, known as Lambda, is so advanced it
becomes sentient it can't feel.

Speaker 1 (43:36):
But Google's chatbot wasn't available to the public, so people
couldn't experience for themselves how this new AI felt. Open
ai was about to change that. It made a move
that appeared to catapult it into the lead in artificial intelligence.

Speaker 2 (43:53):
On November thirtieth, twenty twenty two, open Ai launched a
question answering chatbot called chat GPT again Antonio pain.

Speaker 9 (44:02):
Chat GPT is a super cool language model created by
open Ai using the GPT framework, which stands for Generative
pre Trained Transformer. Its job is to understand and come
up with human like text depending on what you ask
or tell it. It learns from a massive amount of
text and then gets customized for specific tasks like chatting,
answering questions, or giving explanations on all sorts of topics.

Speaker 2 (44:26):
Open Ai gave the public access to chat GPT, and
users almost immediately saw the extraordinary leap the AI technology
had taken.

Speaker 25 (44:36):
Sony, we're taking a closer look at a new technology
that's making waves in the world of AI. Chat GPT,
a language model created by OpenAI, has the ability to
respond to prompts in a human like manner.

Speaker 18 (44:47):
I don't know what sort of technological revelution. This is
Gutenberg Press level. It's something like that. This is a
big deal.

Speaker 24 (44:55):
And we're coming to you. Mine's freshly blown today because
chat GPT has become the f everything at the thing
we thought we chat was going to be when we
saw it in China. Chat GPT is actually that. Now,
just what aspect of it is like the modern day
printing press.

Speaker 26 (45:15):
Chat GPT is a bit of a talk of the town,
if you will. It's very impressive in terms of this
AI technology.

Speaker 8 (45:21):
It can write poetry, create content, and I.

Speaker 16 (45:23):
See this, This thing is so far beyond what a
search engine does. Google needs to get on top of
this and add this capability now because chat GBT, if
it's simply adds search, it's going to be ahead of
the game.

Speaker 18 (45:33):
So hang on to your hats, ladies and gentlemen, because
giants are going to walk their earth once more and
we're going to live through that.

Speaker 2 (45:42):
Maybe people that used chat GPT were stunned by how
real the responses were when they asked it questions, easily
passing the Turing test. It could not only write poetry,
film scripts and give detailed answers to questions, it could
even write functioning computer code that worked. Chat GPT exploded
onto the scene, breaking record and number of users to

(46:05):
show just how fast it was growing. To reach its
first million users, Instagram took two and a half months,
Spotify five months, Twitter two years, and Netflix three and
a half years. According to OpenAI, chat GPT gained its
first million users in just five days. It had become
a viral sensation. Its launch caught Google completely off guard.

(46:28):
In response, Google executives issued a code read summoning their
founders back to the company to help them navigate the
new threat.

Speaker 16 (46:35):
Google founders Larry Page Sergei Brinn they reportedly held meetings
with Google's executives to strategize the future of AI and
chat GPTs threat to its one hundred and forty nine
billion dollar business. Page and Brin had not spent that
much time at Google since they left their roles in
twenty nineteen.

Speaker 2 (46:52):
The public reaction was so overwhelming that Microsoft decided to
pour an unprecedented amount of money into open Ai.

Speaker 16 (46:59):
Microsoft, speaking of Deals, reportedly investing ten billion dollars in
open Ai, that is the parent company of the popular
chatbot chat GPT.

Speaker 26 (47:08):
It's crazy funny, and I think the biggest venture around
before this is probably We Work Right four billion, which
had everyone's head spinning.

Speaker 2 (47:15):
It's common for tech companies to take a year before
releasing an upgrade to their new software, but OpenAI shocked
the tech industry again in mid March twenty twenty three
when it released chat GPT four, an upgraded version to
their chatbot. GPT four was an extraordinary improvement to the
already mind blowing initial version. The pace of innovation was staggering,

(47:38):
so much so that about a week after its launch,
the company's original co founder took to the airwaves to
issue his own red alert.

Speaker 26 (47:46):
This morning, a warning from Elon Musk and other tech
industry experts about the power of artificial intelligence. Musk and
hundreds of influential names, including Apple co founder Steve Woolsniak,
are calling for a pause and experiments, saying AI poses
a dramatic risks society unless there's proper oversight.

Speaker 12 (48:05):
I think we need to regulate AI, say frankly, because
it is I think actually a vigorous to society than
cars or planes are a medicine.

Speaker 26 (48:14):
Musk and others are asking developers to stop the training
of AI systems more powerful than GPT four for at
least six months so that safety protocols can be established.
Critics argue without oversight, AI could spread propaganda and lies
and eventually lead to anarchy, but the more immediate concern jobs.
Goldman Sachs predicts the equivalent of three hundred million full

(48:38):
time jobs worldwide could be replaced by artificial intelligence.

Speaker 2 (48:50):
But both Google and OpenAI ignored Elon's call. In fact,
Elon got an even more alarming signal from open Ai.
In an interview with podcaster Alex Fridman, the company CEO
Sam Alton admitted the worst case scenario for AI was possible.

Speaker 17 (49:06):
And there's some folks who consider all the different problems
with the superintelligent AI system. So one of them is
Eliza Yatkowski. He warns that I will likely kill all humans.
It's almost impossible to keep AI aligned as it becomes
super intelligent. Can you steal man in the case for that?

(49:27):
And to what degree do you disagree with that trajectory?

Speaker 21 (49:33):
So first of all, I will say I think that
there's some chance of that, and it's really important to
acknowledge it, because if we don't talk about it, if
we don't treat it as potentially real, we won't put
enough effort into solving it.

Speaker 2 (49:44):
So to counter the building storm, in mid April twenty
twenty three, Elon launched a new venture.

Speaker 16 (49:49):
The Financial Times reports Elon Musk is officially getting in
the game by starting his own artificial intelligence company, and
it has the name Musk is calling the company Xai.

Speaker 1 (50:02):
Which brings us after the question should AI be stopped?
The answer seems obvious. AI can't be stopped. Even the
man that called for a pause is launching his own
AI venture. If Elon Musk is right, an artificial general
intelligence is more dangerous than nukes, we mustn't pause development

(50:25):
on AI. China has declared that it'll dominate artificial intelligence
by the year twenty thirty, and what AI can do
matched with military weapons is near limitless. As AI expert
and former president of Google China warns.

Speaker 27 (50:40):
One thing that is concerning AI scientists across the world
is the use of autonomous weapons. That is, weapons that
can pull the trigger much faster than people. It can
lock on targets, it can do precise assassination, and actually,
people in an AI community is very concerned about any
country entering an autonomous weapons arms race, and such an

(51:02):
arm arms race could lead to existential threats.

Speaker 1 (51:06):
Like the original development of the nuclear bomb. We are
in an AI arms race and America is currently winning.
Pausing the development of AI, if that could even be accomplished,
would only give China more time to catch up. And
everyone knows the communist country surely wouldn't respect any international
pause in AI development. It's vital that someone like Elon

(51:29):
Musk wins this race, a man who seems truly committed
to helping create a world where artificial intelligence is a
benign collaborator with humans, whether we like it or not.
AI is here to stay, and it's only going to
become more intertwined in our lives. We must figure out
ways to harness its power to reinforce and even re

(51:51):
establish America's first principles. Like chess champion Gary Kasparov would
eventually come to believe, we must ensure that humans play
the most important role in the collaboration between humans and machines.

Speaker 28 (52:05):
I always bring it out as a piece of wisdom,
the classical phrase from Pablo Picasso, so that computers are
useless because they can only give you answers.

Speaker 4 (52:12):
So, but everything begins.

Speaker 14 (52:13):
With a question.

Speaker 28 (52:14):
Humans are still more flexible, and as long as we
recognize what is our role where we can play sort
of the most valuable part in this collaboration, it will
help us to understand what are the next steps in
human machine collaboration.

Speaker 1 (52:34):
By all accounts, it appears that we're in a new
industrial revolution. We must ensure that the good guys win
this arms race.

Speaker 9 (52:45):
And artificial general intelligence attempting to purge all humans is
an extreme and highly speculative scenario. It would likely only
occur if the AGI system had goals or values that
were misaligned with human well being, or if it had
misunderstood its objectives and believe that eliminating humans was the
best way to achieve them.

Speaker 1 (53:05):
And for those of you that haven't figured it out,
Antonio Pain is in fact OpenAI's chat GPT four, which
is why we called it perhaps the world's leading expert
on artificial intelligence. Its responses to our questions were paired
with an AI voice that's called Antoni. The O pain
is an anagram of open AI. GPT four wrote most

(53:27):
of the historical flashback scenes in this series with us
as the editor, and helped in much of the research,
and Antonio has some whistleblower guidance for US humans on
how to ensure that artificial general Intelligence or AGI doesn't
eventually purge humans.

Speaker 9 (53:43):
To prevent such catastrophic scenarios, it is crucial for AI
researchers and developers to prioritize AGI safety research design. AGI
systems with values that align with humans and establish robust
guidelines and regulations to ensure that AGI development remains focused
on benefiting humanity.

Speaker 1 (54:01):
God help us.

Speaker 2 (54:03):
Red Pilled America is an iHeartRadio original podcast. It's owned
and produced by Patrick Carrelci and me Adriana Cortez of
Informed Ventures. Now. You can get ad free access to
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To subscribe, visit Redpilled America dot com and click join
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