Who Invented A.I.? - The Pioneers of Our Future
Summary
TLDRThis ColdFusion video explores the history and potential of artificial intelligence (AI), highlighting the pioneering work of figures like Frank Rosenblatt and Geoffrey Hinton. It discusses the evolution from simple perceptrons to complex deep neural networks and the transformative impact of AI on various fields. The script also touches on the challenges and ethical considerations of AI development, including the possibility of singularity, where AI surpasses human intelligence.
Takeaways
- 🌟 Artificial Intelligence (AI) has the potential to revolutionize our world in ways similar to how computers and the Internet did in the past.
- 🤖 The field of AI has been built on the contributions of pioneers who laid the groundwork for modern AI, starting with the concept of associationism introduced by Aristotle in 300 BC.
- 👨🔬 Frank Rosenblatt's development of the perceptron in 1957 marked the beginning of AI, a digital neural network designed to mimic brain neurons and classify images.
- 📰 High expectations for AI were set in the late 1950s, with the New York Times predicting the creation of an electronic computer with consciousness, but the technology at the time was limited.
- 🧠 Geoffrey Hinton's belief in the power of neural networks and his work on multi-layered neural networks, or deep learning, was pivotal in the AI revolution.
- 🔄 Hinton's research on backpropagation allowed computers to learn from their mistakes, a significant advancement in AI capability.
- 🚗 Innovations in AI, such as self-driving cars and handwriting recognition, have been built upon the foundations laid by pioneers like Hinton and Yan LeCun.
- 🌐 The growth of the Internet provided the data necessary for AI to advance, along with increased computing power predicted by Moore's Law.
- 🏆 Hinton's work culminated in the creation of AlexNet, which achieved unprecedented success in image recognition and sparked a resurgence in AI research.
- 📈 The success of neural networks in image recognition challenges has led to a rapid increase in accuracy, now surpassing human capabilities.
- 🔮 The concept of singularity, where AI surpasses human intelligence, is a topic of speculation and concern, with potential impacts on various fields including medicine and science.
Q & A
What is the potential impact of artificial intelligence (AI) on our world?
-AI has the potential to revolutionize our world, affecting how we do things and how we live, similar to the impact of computers and the Internet in the past.
Who is considered one of the pioneers in the field of AI and what was his contribution?
-Frank Rosenblatt is considered a pioneer in AI; he developed the perceptron, a digital neural network designed to mimic brain neurons, in 1957.
What was the limitation of Frank Rosenblatt's perceptron model?
-The perceptron model was limited because it only used a single layer of artificial neurons, which restricted its capabilities and learning abilities.
What is the significance of the year 1958 in the history of AI?
-In 1958, the New York Times reported on the potential of the perceptron, generating significant media hype and public interest in AI.
Who is Geoffrey Hinton and what is his major contribution to AI?
-Geoffrey Hinton is a prominent computer scientist who theorized that the human brain operates as a neural network and that artificial neural networks could be made to work effectively. He is known for developing multi-layered neural networks, which are now referred to as deep neural networks.
What is a deep neural network and why is it significant?
-A deep neural network is a multi-layered approach to neural networks that allows for greater capabilities and learning. It is significant because it overcame the limitations of single-layer networks and has become the foundation for modern AI advancements.
What is the Boltzmann machine and its role in AI history?
-The Boltzmann machine, introduced by Geoffrey Hinton in 1985, is an early form of deep neural networks. It is considered a fundamental building block that allowed artificial neurons to learn basic features from data.
What is backpropagation and how does it relate to AI learning?
-Backpropagation is the process by which computers learn from their mistakes, improving their performance at a given task. It is a key mechanism in AI learning, allowing neural networks to adjust and optimize their weights to make better predictions.
What challenges did AI face in the past and how were they overcome?
-AI faced challenges such as slow and inadequate computing power and a lack of data. These were overcome with the increase in processing power due to Moore's Law and the accumulation of data through the Internet.
What is the significance of the date September 30th, 2012, in AI history?
-September 30th, 2012, marks the day when Geoffrey Hinton's team created AlexNet, the first artificial deep neural network to achieve unprecedented success on the ImageNet image recognition benchmark, demonstrating the power of deep learning.
What is the singularity in the context of AI and what are its implications?
-The singularity refers to the point when AI surpasses human intelligence. Its implications are vast and uncertain, with the potential for AI to self-improve, innovate, and progress fields without human direction.
What are some current applications of AI in our daily lives?
-AI is currently used in various applications such as self-driving cars, smart traffic systems, personalized content recommendations on platforms like Netflix and YouTube, and optimizing services in ride-sharing apps like Uber.
What awards have Geoffrey Hinton and Yann LeCun received for their contributions to AI?
-Geoffrey Hinton and Yann LeCun, along with others, have won the Turing Award, which is considered the Nobel Prize of computing, for their foundational contributions to deep learning and AI.
Outlines
🧠 The Dawn of AI and Its Pioneers
This paragraph introduces the video's theme of artificial intelligence (AI), its potential to revolutionize our world, and the historical context of its development. It discusses the early challenges and misconceptions surrounding AI, and emphasizes the importance of wisdom in managing AI's impact. The script also highlights the significant contributions of pioneers in the field, setting the stage for a deeper exploration of AI's history and its foundational figures.
🤖 The Evolution of Neural Networks and AI Revival
This section delves into the early history of AI, starting with psychologist Frank Rosenblatt's invention of the perceptron in 1957, an early form of a neural network. It discusses the initial hype and subsequent disillusionment with AI due to the limitations of single-layer neural networks. The paragraph then shifts to the revival of AI through the work of Geoffrey Hinton, who advocated for the potential of multi-layered neural networks and introduced concepts like the Boltzmann machine and backpropagation, laying the groundwork for deep learning.
🚀 Breakthroughs and the Surge of AI Applications
This paragraph marks the turning point in AI's history with the development of deep neural networks, leading to significant advancements in image recognition and other AI applications. It describes the breakthrough moment when Hinton's team achieved unprecedented success with AlexNet on the ImageNet competition, sparking a surge of interest and innovation in the field. The script also touches on the wide-ranging applications of AI today, from self-driving cars to personalized content recommendations, showcasing the transformative impact of AI on various industries.
🌐 The Future of AI and the Singularity
The final paragraph contemplates the future of AI, particularly the concept of singularity, where AI surpasses human intelligence. It discusses the rapid progress in AI capabilities, exemplified by AlphaGo Zero's mastery of the game Go. The script raises questions about the ethical and societal implications of AI, including its potential symbiotic relationship with humans. It concludes with a reflection on the legacy of AI pioneers like Geoffrey Hinton and the ongoing impact of their work, as well as a teaser for the next video in the series.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Neural Networks
💡Perceptron
💡Geoffrey Hinton
💡Backpropagation
💡Deep Neural Networks
💡Singularity
💡ImageNet
💡Moore's Law
💡Self-Driving Cars
💡AI Pioneers
Highlights
AI has the potential to revolutionize our world, similar to the impact of computers and the Internet.
Neural networks have been used to improve video game production and solve complex physics problems.
The development of AI is built on the contributions of pioneers who first conceptualized computers that could think for themselves.
Frank Rosenblatt's perceptron in 1957 was an early attempt at a digital neural network mimicking brain neurons.
The perceptron faced limitations due to its single-layer design and the computational power available at the time.
Geoffrey Hinton's research revived interest in neural networks, proposing they could be more powerful with multi-layered approaches.
Hinton's work on deep neural networks and the Boltzmann machine laid the foundation for modern AI advancements.
Yann LeCun contributed to AI by developing a program for recognizing handwritten digits and furthering backpropagation theory.
Backpropagation allows computers to learn from mistakes, improving their performance over time.
AI development was initially hindered by limited computational power and lack of data.
The rise of the Internet and increased computational power revived AI research and applications.
Hinton's AlexNet achieved a breakthrough in image recognition, outperforming previous attempts and sparking widespread interest in neural networks.
AI accuracy in image recognition has since surpassed human capabilities, reaching 97% accuracy.
AI is now integrated into various aspects of daily life, including self-driving cars, recommendation algorithms, and smart traffic systems.
The concept of singularity, where AI surpasses human intelligence, raises questions about the future relationship between humans and AI.
Geoffrey Hinton and other AI pioneers have won prestigious awards for their contributions to the field.
AI is considered one of humanity's greatest tools, with the potential for significant positive impact.
Transcripts
problems posed by the computer are
really no different than the problems we
have with other products of technology
it's gonna take a great deal of wisdom
on our part to manage them but if we do
we're going to make a much better world
hi welcome to another ColdFusion video
artificial intelligence or AI has the
potential to revolutionize our world the
way we do things and how we live and you
can say that it's already starting to do
that AI will be one of those big tools
that propels us into a new future like
computers and the Internet did decades
ago recently we've seen many examples of
neural nets in particular from speeding
up video game production and making
graphics more realistic to solving
age-old physics problems like the
three-body orbit problem so that's all
well and interesting but we have to
recognize that today in the field of AI
we're building off the shoulders of
giants
so the question must be asked who were
those original Giants
how did AI come to be who were the
people that first dreamed their
computers could think for themselves who
are the pioneers of AI TV
[Music]
what really worries me today is what's
going to happen to us if machines can
think could ask me that question just a
few years ago I'd have said it was very
far-fetched and today I just have to
admit I don't really know I suspect if
you come back and four or five years
I'll say sure they really do think as
soon as computers came into existence
scientists began finatus izing about how
they could revolutionize our world even
in the 1960s they theorized that one day
computers would be able to think for
themselves
there are many pioneers that laid the
foundation of AI even as far back as
Aristotle introducing associationism in
300 BC and this would start our attempt
to understand the human brain but in
this episode we're going to focus more
on the more recent notable contributions
the so called
fathers of AI the first attempt and the
beginning of AI all starts with
psychologist Frank Rosenblatt in 1957 in
that time he developed what was called
perceptron a perceptron was a digital
neural network that was designed to
mimic a few brain neurons Frank's first
task for the network was to classify
images into two categories he scanned in
images of men and woman and he
hypothesized that over time the network
would learn the differences between men
and women or at least see the patterns
that made men look like men and women
like women just a year later the media
caught onto the idea and the hype was
strong in 1958 the New York Times
reported that the perceptron was to be
quote the embryo of an electronic
computer that will be able to walk talk
C riot reproduce itself and be conscious
of its existence
end quote unfortunately for Frank
despite the higher his neural network
system didn't work very well at all this
was because he only used a single layer
of artificial neurons making it
extremely limited in what it could do
and even worse there wasn't much that
could be done about it at the time
computers of that day could only handle
this simple setup
these problems were never solved and by
1969 the computer science community had
abandoned the idea and with that AI was
dead everyone may have given up on the
idea but decades later a keen computer
scientist by the name of Geoffrey Hinton
thought that everyone else was just
plain wrong he theorized that the human
brain was indeed a neural network and
the human brain evidently made for an
incredibly powerful system to him this
was as much proof as he needed
artificial neural networks had to work
somehow maybe they just needed some
tweaking Hinton saw the genius in the
idea that everyone else missed it seems
to me there's no other way the brain
could work it has to work by learning
the strengths of connections and if you
want to make a device do something
intelligent you've got two options you
can program it or it can learn right and
we certainly want programmed so we have
to learn so this had to be the right way
to go so you have relatively simple
processing elements that are very
loosely models of neurons they have
connections coming in each connection
has a weight on it just for
clarification a node is an artificial
neuron and a weight represents the
strength of connections between neurons
that weight can be changed to do
learning and what a neuron does is take
the activities on the connections times
the weights adds them all up and then
decides whether to send an output and if
it gets a big enough sum it sends an
output if the sum is negative it doesn't
send anything and all you have to do is
just wire up a gazillion of those just
figure out how to change the weights and
it'll do anything it's just a question
of how you change the weights Geoffrey
Hinton is the superstar in the AI world
having authored 200 peer review
publications Hinton was instrumental in
the fundamental research that brought
about the AI revolution
after studying psychology Hinton moved
into computer science and pursued his
lifelong quest of muddling the brain
originally from prison in the UK he
moved to the University of Toronto in
Toronto he would go on to develop
multi-layered neural networks
he and his team quickly realized that
the problem with Frank Rosenblatt
single-layer approach was that more
layers were needed in the network to
allow for much greater capabilities and
the computers of the day were now
powerful enough to handle it this
multi-layer approach solved the problem
that Frank Rosenblatt had the neural
networks were much more capable today we
call this multi-layered approach a deep
neural network in 1985 Hinton
co-authored a paper which introduced the
Boltzmann machine Boltzmann machines are
the fundamental building blocks of early
deep neural networks you can think of
them like the Ford Model T of neural
networks without getting into the
details the concept is to have groups or
layers of neurons communicate in such a
way where each artificial neuron learns
a very basic feature from any data for
example each neuron can represent a
pixel in an image that the network is
trying to learn long story short the
result is a program that can make
accurate guesses and predictions about
data as never seen before
soon others began innovations based off
deep neural networks a self-driving car
was built in the late 80s on neural
networks and later in the 90s a man by
the name of Yan Li kun would build a
program which recognized handwritten
digits this program would go on to be
used widely
but Yan Li kun would also go on to be an
AI pioneer in his own right Li kun would
study under Geoffrey Hinton and would
lead the research that made Henson's
theory of backpropagation our reality
back propagation in simple terms is the
process of computers learning from their
mistakes and hence becoming better at a
given task much the same way humans
learned from trial and error however the
idea of AI being used for much more was
short-lived the field was stifled by two
problems one slow and inadequate
competing power and two a lack of data a
burst of investor confidence was
eventually met with disappointment and
the research money began drying up
Geoffrey would become ridiculed and
forced to the sidelines of the computer
science community he was seen as a fool
for his long-standing faith in a failed
idea undeterred by the opinion of his
colleagues Hinton pursued his dream with
an unfazed obsession in 2006 and the
world had finally caught up to him
computer processing speed had grown
significantly since the 90s Moore's law
observed by Intel's co-founder Gordon
Moore stated that the number of
transistors per square inch doubles
about every two years
this meant that computers were growing
and processing power exponentially
that's the first problem solved
meanwhile thanks to the advent of the
Internet some 15 years earlier a wealth
of data had been acquired and this
solved the second problem but but you
know I think about this and what about
this internet thing do you know anything
about that sure what what the hell is
that exactly
well it's it's become a place where
people are publishing information so
everybody can have their own homepage
companies are there the latest
information it's wild what's going on
you can send electronic mail to people
it is the big new thing the ingredients
of a Iowa now they're the computers were
powerful enough and there was enough
data to play with by 2012 the ridicule
Geoffrey Hinton was now 64 years of age
continuing the work wasn't an easy task
Hinton was forced to permanently stand
due to a back injury that would cause a
disc to slip out whenever he sat down
the birth of the modern AI movement can
be traced back to a single date
September 30th 2012 on this day Jeffrey
and his team created the first
artificial deep neural network to be
used on a widely known benchmark image
recognition test called imagenet
Hinton's program was called Alec's net
and when it was unleashed on this state
it had performance like no one had ever
seen Alec's net destroyed the
competition scoring an over seventy five
success rate forty one percent better
than the best previous attempt this one
event showed the world that artificial
neural networks were indeed something
special this sent an earthquake through
the science community a wave of neural
net innovations began and soon the world
took notice after this point everyone
began using neural networks in the image
benchmark challenge and the accuracy of
identifying objects rose from Hinton 75%
to 97% in just seven years for context
97% accuracy is surpassing the human
ability to recognize objects computers
recognizing objects better than humans
has never happened in history soon the
floodgates of research and the general
interest in neural nets would change the
world by the late 2010 image recognition
was commonplace even recognizing disease
and medical imaging images were just the
beginning
soon neural net AI was
video speech science and even games
today we see AI everywhere
Tesla among many companies has created a
sophisticated self-driving AI which is
already sharing the road with humans it
is predicted their self-driving cars
will reduce accidents by up to 90% while
smart traffic lights would reduce travel
time by 26% Netflix and YouTube even
uses AI to learn what shows you watch
and recommend new ones
uber uses machine learning AI to
determine surge pricing your rides
estimated time of arrival and how to
optimize the services to avoid detours
there's also a new interesting
hide-and-seek AI as shown here by the
YouTube channel two minute papers in
this scenario two AI teams battle
against each other one outsmarting the
other as each round of the game
persisted after a given time one of the
teams figured out how to break the
game's physics engine in order to win
this was something that the researchers
never anticipated is the potent
demonstration of a eyes problem-solving
abilities the popular app tik-tok is
completely AI driven leading towards
popularity as we've covered in the
previous video so now a is everywhere
it's in our daily lives even if we're
not aware of it of course there's many
examples of AI being used but perhaps
the most interesting uses will come
after we reach singularity
[Music]
singularity is the concept of a is
surpassing human intelligence after this
point what happens is a bit of an
open-ended question by default computers
would be able to reinvent better
versions of themselves they could
progress fields such as medicine and
science without human direction
alphago zero is a graphic illustration
of the possible rate of this progress in
2016
experts thought that it would take an AI
around 12 years to beat a human at the
ancient game of God a game with
virtually infinite possibilities and a
game that relies on human intuition to
master but the experts were very wrong
the 12-year prediction in reality was
actually zero an AI did in fact beat the
grandmaster of go in that very same 2016
year the next version of the AI alphago
zero learned to play the game from
scratch and beat the previous version a
hundred games to zero in just three days
alphago zero was so good that it was
able to be applied to other things that
it wasn't trained for like lowering the
power usage on Google's data centers the
new breeds of AI could even begin to
invent new tools that humans would never
be able to fathom dr. Richard Sutton of
the University of Alberta says that
singularity is widely estimated to
happen around 2040 by 2030 we should
have the hardware capability to achieve
this allowing for another decade for
people like Sutton to make the code that
achieves singularity it's a rather
unnerving thought that in about a decade
we may have computers that are smarter
than us how many years away do you think
we are from a neural network being able
to do anything that a brain can do so I
don't think it'll happen in the next
five years beyond that it's all a kind
of fog so I'd be very cautious about
making a prediction is there anything
about this that makes you nervous in the
very long run yes I mean obviously
having other super intelligent beings or
more intelligent than us is something to
be nervous about it's not gonna happen
for a long time but it is something to
be nervous about in the long run what
aspect of it makes you nervous well will
they be nice to us also the movies
always portray it
as an individual intelligence I think it
may be that it goes in a different
direction where we sort of developed
jointly with these things so the things
aren't fully autonomous they're
developed to help us they're like
personal assistance and we'll develop
with them and it'll be more of a
symbiosis than a rivalry so seen the
future so where are the pioneers now
currently Geoffrey Hinton divides his
time between his roles as a professor at
the University of Toronto and vice
president at Google Lee Khan is vice
president at Facebook
both of these pioneers had won the 2018
cheering Award for their contribution to
AI named after the father of computer
science Alan Turing who created a
machine to decipher German codes
virtually ending World War two the Alan
Turing prize is considered the Nobel
Prize of computing
artificial intelligence has rapidly
grown in the span of less than two
decades from the fringes of science to
the centerpiece of the world without the
work of these pioneers who refuse to
give up our future may be very different
perhaps we don't fully understand the
potential of AI but nonetheless it
should be obvious that their work has
created a significant point in human
history much like the invention of fire
the wheel electricity computers and the
Internet artificial intelligence will be
one of humanity's greatest tools duties
back conditioned Geoffrey Hinton hasn't
sat down for the last 12 years at 71 we
hope Hinton will keep standing for many
more years to come while AI is helping
many people today we can only hope that
will continually be used for good in the
future so thanks for watching that so
look at the people who created AI and
the history of artificial intelligence
itself next week in the next episode
we'll be taking a look at the very
strange story of the first man to visit
space if you want to know more about the
inventors who influenced our world you
can check out my book new thinking which
is what this video is based off if you
want to see more things on science
technology business and history feel
free to subscribe so you don't miss out
on any new videos ok so before I leave
you here are some interesting examples
of AI outputting data visually cheers
guys
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