Kind: captions
Language: en
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machines that think like humans our
Dream to create machines in our own
image that are smart and intelligent
goes back to Antiquity oh can you bring
it to me is it possible that the dream
of artificial intelligence has become
reality they're able to do things that
we didn't they could
do go was thought to be a game where
machines would never win the number of
choices for every move is enormous and
now the possibilities seem
endless and this is going to be one of
the greatest boosts to productivity in
the history of our species that looks
like just a hint of some type of smoke
identifying problems before a human can
we taught the model to re recognize
developing lung
cancer and inventing new drugs I never
thought that we would be able to be
doing the things we're doing with AI but
along with the hope this is a dangerous
time comes deep concern one of the first
drops in the feared flood of AI created
disinformation we have lowered barriers
to entry to manipulate reality we're
going to live in a world where we don't
know what's real the risks are uncertain
and potentially enormous how powerful is
AI how does it work and how can we reap
its extraordinary benefits civil looked
here and anticipated that there would be
a problem without jeopardizing our
future AI
Revolution right now on
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tell me the backstory on inflection AI
Our Story begins with the making of this
story the story of inflection AI is an
exciting one
I was researching an interview subject
who is Mustafa sulan something I've done
a thousand times in my 40-year career
Mustafa San is a true Pioneer in the
field of artificial intelligence but
this time it was different I wasn't
typing out Search
terms what is machine learning I was
having a conversation with a computer
sounds like an exciting project miles it
felt like something big had changed
machine learning ml is a type of
artificial intelligence and as it
happened I was focused on one of the
innovators of this
revolution okay so if I do this Mustafa
sulan is co-founder of a startup called
inflection it makes an artificial
intelligence assistant called piie so
now you can speak I met them both in
London it's fundamentally different
isn't it yeah it's a conversational
Style all of us humans learn through
stories and through narrative and
through interactive dialogue and now the
machine can kind of come alive and talk
to you about whatever it is that's on
top of your mind tell me about the PBS
program Nova chatbots can offer up quick
answers write poems finish essays and
translate languages among many other
things Nova is a science documentary
series produced they aren't perfect but
they have put artificial intelligence in
our hands
and into the public
conscious and it seems where equal parts
Leery and
intrigued AI is a tool for helping us to
understand the world around us predict
what's likely to happen and then invent
solutions that help improve the world
around us my motivation was to try to
use AI tools to uh you know invent the
future the rise in artificial
intelligence AI technology is developing
lately it seems a dark future is already
here predictions the technology could
replace millions of jobs if you listen
to the news reporting the moment
civilization was transformed so how can
artificial intelligence help us and how
might it hurt us at the center of the
public hand ringing how should we put
guard rails around it we definitely need
more regulations in place artificial
intelligence is moving fast and changing
the world can we keep up nonhuman Minds
smarter than our own Mach the news
coverage may make it seem like
artificial intelligence is something new
at a moment of Revolution but human
beings have been thinking about this for
a very long time I have a very fine
brain our Dream to create machines in
our own image that are smart and
intelligent goes back to antiquity
uh it's it's something that has has
permeated the evolution of society and
of
science the modern origins of artificial
intelligence can be traced back to World
War II and the prodigious human brain of
Alan
Turing the legendary British
mathematician developed a machine
capable of deciphering coded messages
from the
Nazis after the war he was among the
first first to predict computers might
one day match the human brain there are
no surviving recordings of touring's
voice but in 1951 he gave a short
lecture on BBC Radio we asked an AI
generated voice to read a passage I
think it is probable for instance that
at the end of the century it will be
possible to program a machine to answer
questions in such a way that it will be
extremely difficult to guess whether the
answers are being given by a man or by
the
machine and so the touring test was born
could anyone build a machine that could
converse with a human in a way that is
indistinguishable from another
person in 1956 a group of pioneering
scientists spent the summer
brainstorming at Dartmouth
College and they told the world that
they have coined a new academic field of
study they called it artificial
intelligence for decades their
aspirations remained far ahead of the
capabilities of
computers in 1978 Nova released its
first film on artificial intelligence we
have seen the first crude beginnings of
artificial intelligence and the
legendary science fiction writer Arthur
C Clark was as always pre it doesn't
really exist yet at any level because
our most complex computers are still
morons highspeed morons but still morons
nevertheless we have the possibility of
machines which can outpace their
creators and therefore become more
intelligent than
us at the time researchers were
developing expert systems purpose-built
to perform specific tasks and so the
thing that we need to do to make machine
understand um you know our world is to
put all our knowledge into a machine and
then provide it with some
rules classic AI reached a pivotal
moment in
1997 when an artificial intelligence
program devised by IBM called Deep Blue
defeated World chess champion and
grandmas Gary
caspero it searched about 200 million
positions a second navigating through a
tree of possibilities to determine the
best move the program analyzed the board
configuration could project forward
millions of moves to examine millions of
possibilities and then picked the best
path effective but brittle deep blue
wasn't strategizing as a human does from
the outset artificial intelligence
researchers imagined making machines
that think like
us the human brain with more than 80
billion neurons learns not by following
rules but but rather by taking in a
steady stream of data and looking for
patterns the way that learning actually
works in the human brain is by updating
the weights of the synaptic connections
that are underlying this neural network
manolis Kellis is a professor of
computer science at the Massachusetts
Institute of
Technology so we have trillions of
parameters in our brain that we can
adjust based on experience I'm getting a
reward I will up dat the strength of the
connections that led to this reward I'm
getting punished I will diminish the
strength of the connections that led to
the punishment so this is the original
neural network we did not invent it we
you know we inherited
it but could an artificial neural
network be made in our own image touring
imagined it but computers were nowhere
near powerful enough to do it until
recently it's only with the Advent of
extraordinary data sets that we have uh
since the early 2000s that we were able
to build up enough images enough
annotations enough text to be able to
finally train these uh sufficiently
powerful
models an artificial neural network is
in fact modeled on the human brain it
uses interconnected nodes or neurons
that communicate with each other each
node receives inputs from other nodes
and processes those inputs to produce
outputs which are then passed on to
still other nodes it learns by adjusting
the strength of the connections between
the nodes based on the data it is
exposed to this process of adjusting the
connections is called training and it
allows an artificial neural network to
recognize patterns and learn from its
experiences like humans
do a child how is it learning so fast it
is learning so fast because it's
constantly predicting the future and
then seeing what happens and updating
their weights in their neural network
based on what just happened now you can
take this self-supervised learning
Paradigm and apply to
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machines at first some of these
artificial neural networks were trained
on Vintage Atari video games like Space
Invaders and
breakout games reduce the complexity of
the real world to a very narrow set of
actions that can be taken before he
started inflection Mustafa sulan
co-founded a company called Deep Mind in
2010 it was acquired by Google four
years later when an AI plays a game we
show it frame by frame every pixel in
the moving image and so the AI learns to
associate pixels with actions that it
can take moving left or right or
pressing the file fire
button when it obliterates blocks or
shoots aliens the connections between
the nodes that enabled that success are
strengthened in other words it is
rewarded when it fails no
reward eventually all those reinforced
connections overrule the weaker ones the
program has learned how to
win this sort of repeated allocation of
reward for repetitive behavior is a
great way to train a dog it's a great
way to teach a kid it's a great way for
us as adults to adapt our behavior and
in fact it's actually a good way to
train machine learning algorithms to get
better in 2014 Deep Mind began work on
an artificial neural network called
Alpha go that could play the ancient and
deceptively complex board game of
Go Go was thought to be a game where
machines would never win the number of
choices for every move is
enormous but at Deep Mind they were
counting on the astounding growth of
compute
power and I think that's the key concept
to try to grasp is that we are massively
exponentially growing the amount of
computation used and in some sense that
computation is a proxy for how
intelligent the model is Alpha P go was
trained two ways first it was fed a
large data set of expert go games so
that it could learn how to play the game
this is known as supervised
learning then the software played
against itself many millions of times
so-called reinforcement learning this
gradually improved its skills and
strategies in March 2016 alphago faced
Lee sadal one of the world's top ranking
players in a five-game match in Soul
South Korea Alpha go not only won but
also made a move so novel The Go
cognoscenti thought it was a huge
blunder that's a very surprising move
there's no question to me that these AI
models are creative they're incredibly
creative it turns out the move was a
stroke of Brilliance and this emergent
Creative Behavior was a hint of what was
to come generative
AI meanwhile a company called open AI
was creating a generative AI model that
would become chat GPT it allows users to
engage in a dialogue with a machine that
seems uncannily human it was first
released in 2018 but it was a subsequent
version that became a global sensation
in late
2022 this promises to be the viral
Sensation that could completely reset
how we do things cranking out entire
essays in a matter of seconds not only
did it wow the public it also caught
artificial intelligence innovators off
guard it surprised me a lot that they're
able to do things that we didn't think
they could do simply
by learning to imitate how humans
respond and I thought this kind of
abilities would take many more years or
decades chat GPT is a large language
Model llms start by consuming massive
amounts of text books articles and
websites which are publicly available on
the Internet by recognizing patterns in
billions of words they can make guesses
at the next word in a sentence that's
how chat GPT generates unique answers to
your questions if I ask for a high coup
about the blue sky it writes something
that seems completely
original if you're good at predicting
this next word it means you're
understanding something about the
sentence what the style of the sentence
is what the feeling of the sentence is
and you can't tell whether this was
human or a machine that's basically the
definition of the touring test so how is
this changing our world well it might
change my world as an arm amputee ready
for my casting call right yes let's do
it all right that's Brian Monroe of the
hangar Clinic he's been my prosthetist
since an injury took my arm above the
elbow 10 years ago so what we're going
to do today is take a mold of your arm
kind of as like a cast for a broken
B up until now I have used a body
powerered
prosthetic harness and a cable allow me
to move it by shrugging my
shoulders the technology is more than a
century old
but artificial intelligence coupled with
small electric motors is finally pushing
Prosthetics into the 21st
century which brings me to Chicago and
the offices of a small company called
co-a I met the CEO Blair lock a Pioneer
in the push to apply artificial
intelligence to artificial
limbs so what do we have here what are
we going to do this allows us to very
easily test how your control would be
using a pretty simple cuff this has
electrodes in it and we'll let the power
of the electronics that are doing the
machine learning see what you're capable
of all right let's give it a try like
most amputees I feel my missing hand
almost as if it was still there a
phantom everything will't touch you is
that okay yeah not too tight no all good
it's almost entirely immobile stuck in
molasses let's make a fist not too hard
but I am able to to imagine moving it
ever so slightly and I'm going to have
you squeeze into that a little bit
harder very good and I see the pattern
on the screen change a little bit and
when I do I generate an array of faint
electrical signals in my stump that's
your muscle information it feels it
feels like I'm overcoming something
that's really stuck I don't know is that
enough signal should be oh okay we don't
need a lot of signal we're going for
information in the signal not how loud
it is and this is where artificial
intelligence comes in
using a virtual prosthetic depicted on a
screen I trained a machine learning
algorithm to become fluent in the
language of my nerves and
muscles we see eight different signals
on the screen all eight of those sensor
sites are going to feed in together and
let the algorithm sort out the data what
you are experiencing is your ability to
teach the system what is hand Clos to
you and that's different than what it
would be to me I told the software what
motion I desired open close or rotate
then imagined moving my Phantom limb
accordingly this generates an array of
electromyographic or EMG signals in my
remaining muscles I was training the AI
to connect the pattern of these
electrical signals with a specific
movement the system adapts and as you
add more data and use it over time it
becomes more robust and it learns to
improve upon use is it me that's
learning or or the algorithm that's
learning are we learning together you're
learning together okay so how does the
co-a pattern recognition system
work it's called a basian classification
model as I train the software it labels
my various EMG patterns into
corresponding classes of movement hand
open hand closed wrist rotation for
example
as I use the arm it compares the
electrical signals I'm transmitting to
the existing library of classifications
I taught it it relies on statistical
probability to choose the best
match and this is just one way machine
learning is quietly revolutionizing
medicine computer scientist Regina Barz
first started working on artificial
intelligence in the 1990s just as rule
based AI like deep blue was giving way
to neural networks she used the
techniques to decipher dead languages
you might call it a small language model
something that is fun and intellectually
very challenging but it's not like it's
going to change our life and then her
life changed in an instant we see a spot
there in 2014 she was diagnosed with
breast
cancer when you go through the treatment
there are a lot of people who are s
suffering I was interested in what I can
do about it and clearly it was not
continuing deciphering dead languages
and it was quite a journey not
surprisingly she began that Journey with
mammograms it's a little bit more
prominent she and constant Leeman a
radiologist at Massachusetts General
Hospital realized the Achilles heel in
the diagnostic system is the human
eye so the question that we ask is what
is the likelihood of the patients to
develop cancer within the next 5 years
we with our human eyes cannot really
make these assertions because this P so
subtle now is that different from the
surrounding tissue it's a perfect use
case for pattern recognition using what
is known as a convolutional neural
network here's an example of how CNN's
get smart they comb through a picture
with many virtual magnifying glasses
each one is looking for a specific kind
of puzzle piece like an edge a shape or
a texture then it makes simplified
versions repeating the process on larger
and larger sections eventually the
puzzle can be assembled and it's time to
make a guess is it a cat a dog a
tree sometimes the guess is right but
sometimes it's wrong and here's the
learning part with a process called back
propagation labeled images are sent back
to correct the previous
operation so the next time it plays the
guessing game it will be even better to
validate the model Regina and her team
gathered up more than 128,000 mammograms
collected at seven sites in four
countries more than 3,800 of them led to
a cancer diagnosis within 5
years you just give to it the image and
then the 5 years uh of outcomes and it
can Lear the likelihood of getting uh
cancer
diagnosis the software called marai was
a success in fact it is between 75 and
84% accurate in predicting future cancer
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diagnosis then a friend of reginas
developed lung cancer in lung cancer
it's actually sort of mindboggling how
much has changed her friend saw
oncologist Leisa sequist
she and Regina wondered if artificial
intelligence could be applied to CAT
scans of patients lungs we taught the
model to recognize the patterns of a
developing lung cancer by using
thousands of CAT scans from patients who
were participating in a clinical trial
from the new study correct oh
interesting we had a lot of information
about them we had demographic
information we had health information
and we had outcomes information they
called all the model cble in the
retrospective study right so
retrospective data radiologist Florian
fintan showed me what it can do this is
earlier and this is later there's
nothing that I can perceive pick up or
describe there's no what we call a
precursor lesion on this CT scan Cil
looked here and anticipated that there
would be a problem based on the Baseline
scan what is it seeing that's a million
dollar question and and maybe not the
million question does it really
matter does it when they compared the
predictions to actual outcomes from
previous cases cibil fared well it
correctly forecast cancer between 80 and
95% of the time depending on the
population it studied the technique is
still in the trial phase but once it is
deployed it could provide a potent tool
for
prevention the hope is that if you can
predict very early on that the patient
is in the wrong way you can do clinical
trials you can develop the drugs that
are doing the prevention rather than
treatment of very Advanced disease that
we are doing
today which takes us back to Deep Mind
and alphao the fun and games were just
the beginning a means to an end we have
always set out at Deep Mind to um use
our Technologies to make make the world
a better place in 2021 the company
released Alpha fold it is pattern
recognition software designed to make it
easier for researchers to understand
proteins long chains of amino acids
involved in nearly every function in our
bodies how a protein folds into a
specific three-dimensional shape
determines how it interacts with other
molecules there's this correlation
between what the protein does and how
it's structured so if we can predict how
the protein folds then say something
about their function if we know how a
disease's protein is shaped or folded we
can sometimes create a drug to disable
it but the shape of millions of proteins
remained a mystery Deep Mind trained
Alpha fold on thousands of known protein
structures it leveraged this knowledge
to predict 200 million protein
structures nearly all the proteins known
to
science you take some high quality known
data and you use that to uh you know
make a prediction about how a similar
piece of information is likely to unfold
over some time series and and the
structure of proteins is you know in
that sense no different to making a
prediction in the game of Go or in Atari
or in a mamography scan or indeed in a
large language model these thin sticks
here they represent the amino acids that
make up a protein theoretical chemist
Patrina comia works for a company called
in silico medicine it uses Alpha fold
and its own deep learning models to make
accurate predictions about protein
structures what we're doing in drug
design is we're designing a molecule
that is analogous to the Natural
molecule that binds to the protein but
instead it will lock it if this molecule
is involved in a disease where it's
hyper active if the molecule fits well
it can inhibit the disease causing
proteins so you're filtering it down
like you're choosing an Airbnb or
something to you
know whatever exactly right that's a
very good analogy it's sort of like
Airbnb so you are putting in your
criteria and then Airbnb will filter out
all the different properties based on
your criteria so you can be very very
restrictive or you can be very very free
in terms of guiding the generative
algorithms and telling them what types
of molecules you want them to to
generate it will take 48 to 72 hours of
computing time to identify the best
candidates ranked in order how long
would it have taken you to figure that
out as a computational chemist I would
have thought of some of these but not
all of them okay while there are no
shortcuts for human trials nor should we
hope for that this could greatly speed
up the drug development
pipeline there will not be the need to
invest so heavily in preclinical
Discovery um and so drugs can therefore
be cheaper um and you can go after those
diseases that are otherwise neglected
because you don't have to invest so
heavily in order for you to come up with
a drug a viable
drug but medicine isn't the only place
where AI is breaking New
Frontiers it's conducting financial
analysis helps with fraud
detection it's now being deployed to
discover novel m materials and could
help us build clean energy
technology and it is even helping to
save lives as the climate crisis boils
over in St helina California dispatchers
at the calfire Sonoma Lake NAPA Command
Center caught a break in
2023 wildfires blackened nearly 700
Acres of their
territory we were at 400,000 AC in
2020 something like that would generate
a response from us if that was Chief
Mike maruchi has been fighting fires for
more than 30
years once we started having these
devastating fires we needed more Intel
the need for intelligence is is just
overwhelming in today's fire
service over the past 20 years
California has installed a network of
more than a thousand remotely operated
pan tilt Zoom surveillance cameras on
mountaintops vegetation fire Highway 29
at Doon Road all those cameras generate
pedabytes of video calire partnered with
Scientists at UC San Diego to train a
neural network to spot the early signs
of trouble it's called alert California
so here's one that just popped up here's
an anomaly calfire staff chief of fire
and intelligence Philip cigue showed me
how it works while it was in action
detecting nent fires micro fires that
looks like just a little hint of some
type of smoke that was based on this
dispatchers can orchestrate a fast
response AI is given this the ability to
uh detect and to see where those fires
are starting transport 1447 responding
VI MDC for all they know they have
nipped some mega fires in the bud the
success are the fires that you don't
hear about in the news
artificial intelligence can't put out
wildfires just yet human firefighters
still need to do that
job but researchers are pushing hard to
combine neural networks with mobility
and
dexterity this is where people get
nervous will they take our jobs or could
they turn against
us but at MIT they're exploring ideas to
make robots good human
Partners we are interested in making
machines that help people with physical
and cognitive tasks so this is really
great it has the stiffness that we want
it Daniela Rose is director of mit's
computer science and artificial
intelligence lab oh can you bring it to
me seale there have different like kind
of like muscles or
actuators we can do so much more when we
get people and Machines working together
we can get better reach we can get lift
Precision strength Vision all of these
are physical superpowers we can get
through
machines so they're focusing on making
it safe for humans to work in close
proximity to
machines they're using some of the
technology that's inside my prosthetic
arm electrodes that can read The Faint
EMG signals generated as our nerves
command our muscles to
move they have the capability to
interact with the human to understand
the human to step in and help the human
as needed I am at your disposal with 187
other languages along with their various
dialects and sub tongues but making
robots as useful as they are in the
movies is a big
challenge most neural networks run on
powerful
supercomputers thousands of processors
occupying entire
buildings
we have brains that require massive
computation uh which you cannot include
on a self-contained body we address the
size challenge by making liquid networks
liquid networks so looks like an
autonomous vehicle like I've seen before
but it is a little different right very
different this is an autonomous vehicle
that can drive in brand new environments
that has never seen before for the first
time most self-driving cars today rely
to some extent on detailed databases
that help them recognize their immediate
environment those robot cars get lost in
unfamiliar
Terrain in this case you're not relying
on a huge expansive neural network
you're running on 19 neurons right
correct computer scientist Alexander
amini took me on a ride in an autonomous
vehicle with a liquid neural network
brain we become very accustomed to
relying on big giant data centers and
Cloud compute but in an autonomous
vehicle you cannot make such assumptions
right you need to be able to operate
even if you lose internet connectivity
and you cannot talk to the cloud anymore
you your entire neural network the brain
of the car needs to live on the car and
that imposes a lot of interesting
constraints to build a brain smart
enough and small enough to do this job
they took some inspiration from nature a
lowly worm called C leans its brain
contains all of 300 neurons but it's a
very different kind of
neuron it can capture more complex
behaviors in every single piece of that
puzzle and also the wiring how a neuron
talks to another neuron is completely
different than what we see in today's
neural
networks autonomous cars that tap into
today's neural networks require huge
amounts of compute power in the
cloud but this car is using just 19
liquid
neurons a worm at the wheel sort of
today's AI models are really pushing the
boundaries of the scale of compute that
we have they're also pushing the
boundaries of the data sets that we have
and that's not sustainable because
ultimately we need to deploy AI onto the
device itself right onto the cars onto
the surgical robots all of these Edge
devices that actually make the
decisions the AI worm May in fact
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turn the portability of artificial
intelligence was on my mind when it came
time to pick up my new myo El electric
arm equipped with co-a AI pattern
recognition all right let's just check
this real quick a few weeks after my
trip to Chicago I met Brian Monroe at
his home office outside Washington DC
you happy the way it came out yeah would
you tell me
[Music]
otherwise as usual he did a great job
making a tight
socket how's the socket feel does it
feel like it's sliding down
or it's really important in this case
because the electrodes designed to read
the signals from my
muscles have to stay in place snugly in
order to generate accurate reliable
commands to the actuators in my new
hand wait is that you that's
me he also provided me with a humanlike
bionic
hand but getting it to work just right
took some time that's open and that's
closing it's backwards yeah I'll try if
it's revers I'll I can swap the electri
there go that's got it is it the right
drug out I don't okay it's a long way
from the movies
and I'm no Luke
Skywalker but my new arm and I are now
together and I'm heartened to know that
I have the freedom and Independence to
teach and tweak it on my own that's kind
of cool yeah hopefully we will listen to
each other that's pretty awesome but we
might want to listen with a skeptical
ear you see I would never say these
things at least not in a public address
but someone else would
someone like Jordan
Peele this is a dangerous time it's even
more dangerous now than it was in 2018
when comedian Jordan Peele combined his
Pitch Perfect Obama impression with AI
software to make this convincing fake
video or whether we become some kind of
distopia fakes are about as old as
photography itself
musolini Hitler and Stalin all ordered
that pictures be doctored or redacted
erasing those who fell out of favor
consolidating power manipulating their
followers through images they've always
been manipulated throughout history but
there was literally you can count on one
hand the number of people in the world
who could do this but now you need
almost no skill and we said give us an
image of a middle-aged woman news caster
sitting at her desk reading the news H
fared is a professor of computer science
at UC Berkeley and this is your daily
dose of future flash he and his team are
trying to navigate the house of mirrors
that is the world of AI enabled deep
fake imagery not perfect she's not
blinking but it's pretty good for Qui
and by the way he did this in a day and
a half it's the classic automation story
we have lowered barriers to entry to
manipulate reality and when you do that
more and more people will do it some
good people people will do it lots of
bad people do it there will be some
interesting use cases and there'll be a
lot of Nefarious use cases okay so um
glasses off how's the framing everything
okay about a week before I got on a
plane to see him on he asked me to meet
him on Zoom so he could get a good
recording of my voice and mannerisms and
I assume you're recording miles and he
turned the table on me a little bit
asking me a lot of questions to get a
good sampling how are you feeling about
the role of AI
as it enters into our world on a daily
basis I think it's very important first
of all to calibrate the concern level
let's take it away from the Terminator
scenario the Terminator scenario come
with me if you want to live you know a
malevolent neural network hellbent on
Exterminating Humanity you're really
real in the film series the cyborg
assassin is memorably played by Arnold
Schwarzenegger Hy thought it would be
fun to use AI to turn Arnold into me
okay a week later I showed up at
Berkeley School of information
ironically located in the oldest
building on campus so you had me do this
strange thing on Zoom here I am what did
you do with me yeah well it's going to
teach you to let me record your Zoom
call isn't it uh I did this with some
trepidation let I was excited to see
what tricks were up his sleeve I
uploaded 90 seconds of audio and I
clicked a box saying miles has given me
permission to use his voice which I
don't actually think you did um and I
waited about maybe 20 seconds and it
said okay what what would you like for
miles to say and I started typing and I
generated an audio of you saying
whatever I wanted you to say we are
synthesizing at much much lower
resolution this is you could have
knocked me over with a feather when I
watched this Terminators were science
fiction back then but if you follow the
recent AI media coverage you might think
that Terminators are just around the
corner the reality is that the eyes and
the mouth need some work but it sure
does sound like me is and consider what
happened in May of
2023 someone posted this AI generated
image of what appeared to be a terrorist
bombing at the Pentagon today we may
have witnessed one of the first drops in
the feared flood of AI created
disinformation it was shared on Twitter
via what seemed to be a verified account
from Bloomberg News it only took seconds
to spread fast the Dow now down about
200 points 2 minutes later the stock
market dropped a half a trillion dollars
from a single fake image anybody could
have made that image whether it was
intentionally manipulating the market or
unintentionally in some ways it doesn't
really matter so what are the
technological innovations that make this
tool widely
available one technique is called the
generative adversarial Network or Gan
two algorithms in a dizzying student
teacher back and forth let's say it's
learning how to generate a cat and it
starts by just splatting down a bunch of
pixels onto a canvas and it sends it
over to a discriminator and the
discriminator has access to millions and
millions of images of the category that
you want and it says Nope that doesn't
look like all these other things so it
goes back to to the generator says try
again modifies some pixels sends it back
to the discriminator and they do this in
what's called an adversarial Loop and
eventually after many thousands of
volleys the generator finally serves up
a cat and the discriminator says do more
like that today we have a whole new way
of doing these things we're called
diffusion based what diffusion does is
it has vacuumed up billions of images
with captions that are
descriptive it starts by making those
labeled images visually noisy on
purpose and then it corrupts it more and
it goes backwards and corrupts it more
and goes backwards and corrupts it more
and goes backwards and it does that 6
billion
times eventually it corrupts it so it's
unrecognizable from the original
image now that it knows how to turn an
image into nothing it can reverse the
process turning seemingly nothing into a
beautiful
image what it's learned is how to take a
completely
indescription on a text prompt you're
basically reverse engineering an image
down to the pixel yeah exactly yeah and
it's and by the way if you would ask me
will this work I would have said no
there's no way this system works it just
it just doesn't seem like it should work
and that's sort of the magic of when you
get this much data and very powerful
algorithms and very powerful Computing
to be able to Crunch these massive data
sets I mean we're not going to contain
it that's done I sat down with hone and
two of his grad students Justin Norman
and Sarah
barington we looked at some of the AI
trickery they have seen and made
somebody else wrote some base code and
they got grew on to and grow on to and
grow onto and eventually in a world
where anything can be manipulated with
such ease and seeming authenticity how
are we to know what's real anymore how
you look at the world how you interact
with people in it and where you look for
your threats that change generative AI
is now part of a larger ecosystem that
is built on mistrust we're going to live
in a world where we don't know what's
real there is distrust of governments
there is distrust of media there's
distrust of academics and now throw on
top of that video evidence so-called
video evidence I think this is the very
definition of throwing jet fuel onto a
dumpster fire and I and it's already
happening and I imagine we will see more
of it come with me if you want to live
but it also can be kind of fun as Han
promised here's my face on the
terminator's
body long before AI might take an
existential turn against humanity we
will need to reckon with the likes go
now of the miles inator this time he's
back who will no doubt be back trust
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me trust but always
verify so what kind of AI magic is
readily available online it's pretty
simple to make it look like you're
fluent in another
language it was pretty easy to do I just
had to upload a video and
wait and suddenly I looked pretty darn
smart sure it's fun
but I think you can see where it leads
to mischief and possibly even
Mayhem yosua Benjo is an artificial
intelligence
Pioneer he says he didn't spend much
time thinking about science fiction
dystopia as he was creating the
technology but as His Brilliant ideas
became reality reality set in and the
more I read the more I thought about it
um the more concerned I got we are not
honest with ourselves we going to fool
ourselves we're going to
lose avoiding that outcome is now his
main priority he assigned several public
warnings issued by AI thought leaders
including this Stark single sentence
statement in May of
2023 mitigating the risk of Extinction
from AI should be a global priority
alongside other societal scale risks
such as pandemic and nuclear
war as we approach more and more capable
AI systems that might even become
stronger than humans in many areas they
become more and more dangerous can't we
just pull the plug on the thing oh
that's the safest thing to do pull the
plug before it gets so powerful that it
prevents us from pulling the plug open
the pod bay doors hell I'm sorry Dave
I'm afraid I can't do
that it may be some time before
computers are able to act like movie
Super Villains
goodbye but there are near-term dangers
already
emerging besides deep fakes and
misinformation AI can also supercharge
bias and hate
content replace human jobs this is why
we're striking
everybody and make it easier for
terrorists to create
bioweapons and AIC systems are so
complex that they are difficult to
comprehend all but impossible to audit
nobody really understands how those
systems reach their decisions so we have
to be much more thoughtful about how we
test and evaluate them before releasing
them their concerned whether machine
will be able to begin to think for
itself the US and Europe have begun
charting a strategy to try to ensure
safe secure and trustworthy artificial
intelligence
in a way that will be safe for our
communities but how to do that in the
midst of a frenetic race to dominate a
technology with a predicted economic
impact of $13 trillion by 2030 there is
such a strong commercial incentive to
develop this and win the competition
against the other companies not to
mention the other
countries that it's hard to stop that
train but that
what governments should be doing the
titans of social media didn't want to
come to Capitol Hill historically the
tech industry has bridled against
regulation you have an army of lawyers
and lobbyists that have fought us on
there's no question that guard rails
will slow things down but the risks are
uncertain and potentially enormous so it
makes sense for us to start having the
conversation right
now for me the conversation about AI die
is
personal okay no network detected okay
um oh here we go okay and now I'm going
to open open open open open open open
open open I use the co-a app to train
the AI inside my new
prosthetic says all of my training data
is good it's four or five stars and now
let's try to
close all right seems to be doing what
it was
told was my new arm listening maybe I
decided to make things
simpler I took off the hand and attached
a myo El electric hook all right
function over
form Not A Conversation Piece
necessarily at a cocktail party like
this thing is this looks more like Luke
Skywalker I suppose but this thing has a
tremendous amount of function to it
although right now it wants to stay open
and that problem persisted find the tri
plade when I tried using it to set up my
basement studio for a live
broadcast come on close come on I was
quickly
frustrated really annoying not you st
the hook continuously opened on its
own damn it so I completely reset and
retrain the
arm and and reset here we go that they
bad but the software was
artificially
unhappy electrodes are not making good
skin contact maybe that is my problem
ultimately my problem really is I
haven't given this enough time UTS tell
me it can take many months to really
learn how to use an arm like this one
the choke point isn't artificial
intelligence
that a stor now but rather what is the
best way to communicate my intentions to
it a reboot there I guess all right
close open close it turns out machine
learning isn't smart enough to give me a
replacement arm like Luke Skywalker
got nor is it capable of creating the
Terminator right now it seems many hopes
and fears years for artificial
intelligence are rooted in science
fiction but we are walking down a road
to the unknown the door is opening to a
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revolution
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