Who Invented A.I.? - The Pioneers of Our Future

Summary

This 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

00:00

🧠 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.

05:01

🤖 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.

10:04

🚀 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.

15:06

🌐 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.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is portrayed as a revolutionary technology with the potential to transform various aspects of life and society, much like the advent of computers and the Internet did in the past. The script discusses AI's evolution, its applications in different fields, and its future implications.

💡Neural Networks

Neural networks are a subset of AI that are modeled loosely after the human brain. They consist of nodes, or artificial neurons, and weights that represent the strength of connections between these neurons. The script explains how neural networks, especially deep neural networks, have been pivotal in advancing AI capabilities, enabling machines to perform tasks such as image recognition and solving complex problems.

💡Perceptron

The perceptron, introduced by psychologist Frank Rosenblatt in 1957, represents the first digital neural network designed to mimic brain neurons. It was an early attempt at creating a machine that could learn to classify images. The video script discusses the perceptron's limitations due to its single layer of artificial neurons and the subsequent hype and disillusionment it generated in the AI community.

💡Geoffrey Hinton

Geoffrey Hinton is a key figure in the field of AI, known for his belief in and contributions to neural networks. The script highlights his persistence in the face of skepticism and his development of multi-layered neural networks, which significantly advanced the capabilities of AI. Hinton's work on deep learning and backpropagation is foundational to modern AI applications.

💡Backpropagation

Backpropagation is a learning algorithm used in neural networks, allowing the system to learn from its mistakes and improve its performance over time. In the script, it is mentioned as a critical innovation that enabled neural networks to become more sophisticated and effective, with applications ranging from self-driving cars to medical diagnostics.

💡Deep Neural Networks

Deep neural networks are a type of neural network with multiple layers, allowing for more complex pattern recognition and learning. The video script explains how Hinton's work on deep neural networks overcame the limitations of the single-layer perceptron, leading to significant breakthroughs in AI and enabling machines to perform tasks previously thought to be the domain of human intelligence.

💡Singularity

The concept of singularity in the video refers to the hypothetical point in the future when AI surpasses human intelligence. The script raises questions about the implications of this event, suggesting that it could lead to rapid advancements in various fields and potentially reshape human society in ways that are currently unpredictable.

💡ImageNet

ImageNet is a large visual database used in machine learning research. The script discusses a pivotal moment in AI history when Hinton's team used a deep neural network called AlexNet to achieve unprecedented success on the ImageNet image recognition test, demonstrating the power of deep learning and sparking renewed interest in the field.

💡Moore's Law

Moore's Law, mentioned in the script, is the observation that the number of transistors on a microchip doubles approximately every two years, leading to exponential growth in computing power. This increase in processing power has been a critical factor in the advancement of AI, enabling more complex neural networks and algorithms to be developed and run efficiently.

💡Self-Driving Cars

Self-driving cars, as discussed in the script, are an example of AI's practical application. They utilize neural networks for tasks such as object recognition, decision-making, and navigation. The script highlights the potential of AI to revolutionize transportation by reducing accidents and improving traffic flow.

💡AI Pioneers

The term 'AI pioneers' in the script refers to the individuals who have made significant contributions to the field of AI, such as Frank Rosenblatt, Geoffrey Hinton, and Yan LeCun. Their work has laid the foundation for modern AI technologies and continues to influence the direction of AI research and development.

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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you