Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield

Summary

This video script explores the evolution and impact of artificial intelligence (AI), starting from its inception during World War II with the first computer that aided the Normandy landings. It highlights Alan Turing's foundational work and questions like 'can machines think?'. The script delves into AI's exponential growth, its integration into everyday life through Google search and voice assistants, and the backbone of AI—machine learning. It explains how machine learning uses algorithms and data to improve tasks, from stock market predictions to language translation, exemplified by Google Translate's neural network. The script also covers image processing, neural networks inspired by the human brain, and their applications in real-world technologies like self-driving cars and AI in various industries. It concludes by pondering the transformative potential of AI, drawing parallels to past revolutions and speculating on future advancements that could reshape humanity's future.

Takeaways

• 💡 The first computer was invented during World War II, which played a crucial role in cracking German communications and facilitating the Normandy landings.
• 🧠 Alan Turing, known as the father of modern computing, wrote a seminal paper in 1950 titled 'Computing Machinery and Intelligence', which introduced the question 'Can machines think?'
• 📈 AI has been growing exponentially over the past decade and is increasingly integrated into everyday technologies such as Google search and voice assistants like Siri.
• 🌐 Machine learning, a key component of AI, involves using algorithms to find patterns in data and learning algorithms to improve their performance on tasks, from predicting stock markets to language translation.
• 🔍 Google Translate, a widely used AI application, relies on vast amounts of data to improve its translation accuracy and speed, similar to how humans learn through practice.
• 🌐 Google processes around 10 to 15 exabytes of data, which is equivalent to the storage capacity of 30 million personal computers, highlighting the scale of data needed to power AI technologies.
• 👀 Image processing in AI involves separating visual information into features like color, shape, and movement, allowing computers to understand and interpret images.
• 🔬 An application created in the script demonstrates how AI can be trained to identify a Coca-Cola logo using image processing techniques and computer vision.
• 🧠 Neural networks in AI mimic the structure and function of the human brain, using interconnected nodes to process information and learn from data.
• 🚗 AI technologies are being applied in various fields, including self-driving cars and autonomous systems, which rely on image processing and sensor data to navigate and make decisions.
• 🎲 AI has also made significant strides in games, with programs like AlphaGo using reinforcement learning and neural networks to defeat human champions in complex games like Go.

Q & A

What significant event during World War II is mentioned in the script related to the invention of the first computer?

The script mentions that during World War II, the first computer was invented, which was instrumental in cracking German communication codes, thereby ensuring a successful Normandy landing.

Who is credited as the father of the machine that cracked the German communication codes during World War II?

Alan Turing is credited as the father behind the machine that cracked the German communication codes during World War II.

What paper did Alan Turing write in 1950 that is mentioned in the script?

Alan Turing wrote the paper 'Computing Machinery and Intelligence' in 1950, which is mentioned in the script.

What is the central question posed by Alan Turing in his paper 'Computing Machinery and Intelligence'?

The central question posed by Alan Turing in his paper is 'Can machines think?'

How has AI been growing and impacting our lives according to the script?

According to the script, AI has been growing exponentially in the past decade and is already touching our lives in ways we might not notice, such as through Google search and voice assistants like Siri.

What is the backbone of artificial intelligence as mentioned in the script?

The backbone of artificial intelligence, as mentioned in the script, is machine learning.

What are the two major components of machine learning as described in the script?

The two major components of machine learning are using algorithms to find meaning in random and unordered data, and using learning algorithms to find relationships between that knowledge and improve the learning process.

What is the purpose of Google Translate's neural network as depicted in the script?

The purpose of Google Translate's neural network, as depicted in the script, is to improve the translation process by learning from more data and becoming faster and more accurate over time.

How much data does Google process according to the script?

According to the script, Google processes around 10 to 15 exabytes of data.

What is the comparison made in the script to illustrate the amount of data Google processes?

The script compares Google's 15 exabytes of data to 30 million personal computers, each with a storage capacity of 500 gigabytes.

What is the main focus of the application created in the script to identify the Coca-Cola logo?

The main focus of the application created in the script is to identify the Coca-Cola logo by processing the image and comparing its features, such as area parameters and skeleton, with what is stored in memory.

What is the fundamental concept behind neural networks as explained in the script?

The fundamental concept behind neural networks, as explained in the script, is to mimic the human brain's structure and function, with artificial neurons communicating and processing information to enable learning and intelligence.

What historical AI achievement is mentioned in the script regarding IBM's Deep Blue?

The script mentions the historical AI achievement where IBM's Deep Blue became the first-ever program to defeat a world chess champion under tournament rules.

What game did Google's AlphaGo use to demonstrate AI's capabilities beyond brute force calculation?

Google's AlphaGo used the game of Go to demonstrate AI's capabilities beyond brute force calculation, as it requires more intuition and strategic thinking.

How did AlphaGo's approach differ from earlier AI programs, and what was the significance of this?

AlphaGo's approach differed by using reinforcement learning and neural networks, which resembles human decision-making processes. This was significant as it showcased AI's ability to learn and adapt, rather than just relying on raw computational power.

What potential future applications of AI are mentioned in the script?

The script mentions potential future applications of AI such as autonomously constructing a space station on Mars, self-driving cars, fair and safe trading environments through AI in stock markets, and using AI in hospitals to find mutations in human DNA databases and cures for diseases.

What is the final message conveyed in the script regarding AI's role in the future?

The final message conveyed in the script is that AI will not replace biological intelligence but will enhance our lives and future, providing us with unprecedented power and opportunities to shape our world.

Outlines

00:00

💡 The Dawn of AI and Machine Learning

This paragraph delves into the origins of artificial intelligence, tracing back to the invention of the first computer during World War II and Alan Turing's seminal paper on machine intelligence. It highlights the exponential growth of AI in the past decade, its omnipresence in everyday life through applications like Google search and Siri, and the potential of AI to revolutionize our understanding of the universe and human nature. The backbone of AI is machine learning, which is broken down into two components: using algorithms to find meaning in data and improving the learning process through relationships found within that data.

05:00

🌐 Exploring Machine Learning: Image Processing and Neural Networks

The second paragraph focuses on the intricacies of machine learning, particularly in the realms of image processing and neural networks. It begins with an analogy to human vision, explaining how the brain processes visual information, and then applies this concept to computer vision, using the example of creating an application to identify the Coca-Cola logo. The paragraph discusses the importance of learning algorithms in enabling computers to learn from data and improve their performance on tasks. It also provides a live demonstration of an application using open computer vision to track and identify the Coca-Cola logo in real-time.

10:02

🤖 The Evolution of AI: From Manual Calculation to Neural Networks

This paragraph explores the evolution of AI, contrasting the manual and time-consuming methods of the past with the modern capabilities of computers to learn and process information autonomously. It discusses the transition from scientists creating lookup tables to the development of programs that can perform complex tasks like image and speech recognition with ease. The demonstration using Google Cloud Platform showcases the power of combining image processing and neural networks to identify objects in real-time. The paragraph also touches on the applications of AI in various fields, such as self-driving cars and the自动识别 of fish species in the fishing industry.

15:02

🚀 Envisioning the Future of AI and Its Impact on Society

The final paragraph paints a vivid picture of the future with AI, discussing its potential to bring about significant societal changes akin to the Industrial Revolution and the digital revolution of the 1990s. It imagines a future where AI plays a crucial role in space exploration, autonomous transportation, financial markets, and healthcare. The paragraph emphasizes the empowerment and humility that come with creating machines capable of learning and thinking like humans. It concludes with a reflection on the collective journey of humanity and AI, and the responsibility we all share in shaping the future of artificial intelligence.

Keywords

💡World War Two

World War Two, often abbreviated as WWII, was a global war that lasted from 1939 to 1945. It involved many of the world's nations, including all of the great powers, organized into two opposing military alliances: the Allies and the Axis. In the video, WWII is mentioned as the period during which the first computer was invented, which played a crucial role in cracking German communications and aiding the successful Normandy landing.

💡Alan Turing

Alan Turing was an English mathematician, computer scientist, and cryptographer who is widely considered to be the father of theoretical computer science and artificial intelligence. He made significant contributions to the development of the first computers and the concept of machine intelligence. In the script, Turing is credited with writing the seminal paper 'Computing Machinery and Intelligence,' which introduced the question 'Can machines think?' and set the stage for discussions on AI.

💡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. The video discusses AI as one of the most significant scientific breakthroughs of the 21st century, with the potential to revolutionize various aspects of human life. Examples from the script include AI's application in Google search and Siri's natural language processing and speech recognition.

💡Machine Learning

Machine Learning is a subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can receive input data and use statistical analysis to predict an output. In the video, machine learning is described as the backbone of AI, with its two main components being the use of algorithms to find meaning in data and the improvement of learning processes.

💡Google Translate

Google Translate is a free multilingual neural machine translation service developed by Google, to translate text from one language to another. It is used as an example in the script to illustrate how AI works in practice. Google Translate uses a vast amount of data to improve its translation accuracy, much like how humans improve their skills through practice.

💡Neural Networks

Neural Networks are a set of algorithms designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. An important concept in the video, neural networks are inspired by the human brain's structure and function, with interconnected nodes that process information. The script mentions neural networks in the context of Google Translate and other AI applications, such as image processing and self-driving cars.

💡Image Processing

Image Processing refers to the techniques of modifying images, typically with the aim of enhancing or analyzing them. In the video, image processing is a key component of AI, particularly in applications like Google's self-driving cars and the demonstration of identifying a Coca-Cola logo using an iPad application. The script explains how computers break down images into features like area, parameter, and skeleton to understand and classify them.

💡Reinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to achieve a goal in a complex, uncertain environment through trial and error. It is mentioned in the script in relation to Google's AlphaGo, a program that learned the game of Go and defeated a world champion using a combination of neural networks and reinforcement learning, showcasing a more intuitive and human-like approach to AI.

💡Self-Driving Cars

Self-driving cars, also known as autonomous vehicles, use a combination of sensors, cameras, and AI to navigate roads and transport passengers without the need for human intervention. The script discusses Google's self-driving car project as an example of how AI technologies like image processing and neural networks can be applied to create innovative and potentially life-changing solutions.

💡DNA Databases

DNA Databases are collections of DNA profiles that can be used for various purposes, including medical research and forensic investigations. In the video, the potential of AI in analyzing DNA databases to find mutations and develop cures for diseases is highlighted. This represents the transformative impact AI can have on healthcare and scientific research.

Highlights

World War II saw the invention of the first computer, instrumental in cracking German communications and aiding the Normandy landings.

Alan Turing's 1950 paper 'Computing Machinery and Intelligence' posed the question 'Can machines think?', setting the stage for AI development.

AI has grown exponentially in the past decade, integrating into everyday life through search engines and voice assistants.

Machine learning, a core component of AI, involves algorithms finding meaning in data and improving learning processes.

Google Translate uses machine learning to improve translation accuracy by processing vast amounts of data.

The speaker, born in China, relies on Google Translate for multilingual communication during global travels.

Google processes 10 to 15 exabytes of data, equivalent to 30 million personal computers, to power services like Translate.

AI's learning process is inspired by human learning, improving through practice and exposure to more data.

Image processing and neural networks are key areas of focus within AI, with applications in computer vision.

The human visual system processes information through three distinct systems for color, shape, and motion.

An application created to identify the Coca-Cola logo demonstrates the practical use of image processing with AI.

Neural networks in AI mimic the human brain's structure and function, with artificial neurons processing information.

Google's self-driving car project uses image processing and sensor data to navigate safely.

AI has been used in various industries, including fisheries, to improve efficiency and decision-making.

AlphaGo's victory over a Go champion showcased AI's ability to learn and make decisions through reinforcement learning.

AI is predicted to bring about significant changes akin to the Industrial Revolution and the advent of personal computing.

The future with AI envisions autonomous construction on Mars, self-driving cars, and advanced healthcare through DNA analysis.

AI is seen as an enhancement to human intelligence, not a replacement, with the potential to shape our future positively.

Transcripts

[Music]

[Applause]

thank you thank you

thank you so during World War two the

first computer was invented they cracked

the German communication cold and

ensured a successful Normandy landing

the father behind this unprecedented

machine Alan Turing wrote the paper

Computing Machinery and intelligence in

1950 and the paper opens with the words

I propose to consider the question can

machines think well today inspired by

his thoughtful question we'll try to

answer the following how can we create

an intelligent computer and what will

the future look like with intelligent

machines

well in fact AI has been growing

exponentially in the past decade it has

already been touching our lives in ways

that you might not notice for example

every time you go on Google search some

kind of AI is being used to show you the

best results every time you ask Siri a

question natural language processing and

speech recognition is being used so

artificial intelligence will probably be

one of the biggest scientific

breakthroughs in a 21st century it will

give us the power to probe the universe

and our humanity with a different

approach

AI has the potential of forever changing

our humanity the backbone of artificial

intelligence is machine learning and I

think the term is pretty

self-explanatory we want to make

machines learn based on its knowledge

and make decisions

machine learning can be understood in

two major components one is to use

algorithms to find meaning in random and

unordered data and the second part is to

use learning algorithms to find

relationship between that knowledge and

improve that learning process so the

overall goal for machine learning is

actually quite simple is to improve the

machines performance on certain tasks

and that has can be predicting the stock

market to complicated ones such as

translating articles between languages

and the screenshot that you see right

now is actually a depiction of Google

Translate neural network whoo speaking

of translation anyone here speaks the

second or third language right that's

awesome well I was born in China and I

speak Chinese and also speak English

plus a couple of programming languages

here on account that so when my family

and I travel around the world we often

need something called Google Translate

and by examining Google Translate

artificial intelligence we can actually

gain a greater understanding of how most

AI works well first of all have you ever

wondered how much data is a Google have

well it turns out Google hauls right

around 10 to 15 exabytes of data well

what does that even mean

let me put that into perspective for you

if one personal computer is 500

gigabytes then Google's 15 exabyte would

be 30 million personal computers and

data turns out to be one of the fuels

that powers Google Translate of magical

technology so on the surface Google

Translate hasn't changed since 2007 when

it first launched but will you my notice

is that the translator is getting faster

and more accurate so it turns out the

learning process for Google Translate is

inspired by our own we as humans get

better at doing things by practicing

just like what our math teachers and our

musical teachers always tell us it turns

out Google Translate can get better at

translating by reading more articles so

how do computer learn

can actually come up with this flowchart

that will give us a summarize will give

us a good picture of how artificial

intelligence actually works so it turns

out we have to use some training input

and put that into a learning algorithm

which will give us some knowledge and

that knowledge will be on a computer

knows about that specific subject and

you and me where the user right the user

will give the computer some input and

hopefully some alpha will come out so in

our case Google Google's 15 X byte of

data will be the training input and

something you won't translate is going

to be the user input and the output is

going to be something in a different

language so the most important part of

this whole entire process is actually

the learning algorithm this is what

powers computers to learn and be

intelligent so today we're going to

focus on two parts one is image

processing and the second part is neural

networks so let's begin by talking about

image processing we can talk about

computer vision without talking about

human vision right and visual signal

from our retina is relayed through our

brain to our primary visual cortex in

the back of our brain which is right

here and virtual information is

separated and processed in three

different processing systems one system

mainly processing information about

color second one about shape the third

one about movement location and

organization so with all of that in mind

today we'll try to create an application

that will be able to identify a

coca-cola logo so first of all we have

to understand that most pictures that we

see on a computer screen

I mean of pixels tiny tiny things that

represent color which is also why Steve

Jobs names his company Pixar since every

person that world is made of pixels

which is great so the computer is trying

to understand this image it will first

separate them into different futures

objects that we can easily see in the

still image and then each of these

features will provide the computer some

information about that image and today

we'll mainly focus on

area parameter and skeleton and some

details about these features so now the

computer has those things in memory so

when the user gives the computer some

input it will be able to process an

import and compare that with what is in

memory and then give you some output

whether the image match with the

template or not so here's that

technology in action so I've created an

application on this iPad that will be

able to identify coca-cola logo and this

application is actually powered by open

computer vision and thanks to a great

framework so today we'll learn a

coca-cola logo so let's click on that

great we just learned this image

wonderful and as you can see the image

on top has little green rectangles and

squares around it and those are regions

that the computers are processing and in

the image below is one of the biggest

features in that image and in a table as

you can see there are details that

computer is remembered so let's dismiss

that and click start tracking oh look at

that that's pretty sensitive it

successfully tailed that the paper right

in front of me has a coca-cola logo on

it great and also this is life so you

know I'm not thinking anything by the

way so wonderful thank you

so now let's recap we can summarize

everything we did with this simple

flowchart we had some input data and we

use some algorithm to find some meaning

in that data and in the future we'll use

new networks to improve this whole

entire process and hopefully learn more

and more images and the pixel in our

case or the input data and the meaning

where things like area parameter

skeleton those you know details the

computer focused on and hopefully in the

future we'll be able to classify any

image we want remember in the very

beginning we talked about there are two

parts of learning algorithms right the

second part is near networks so let's

talk about that a little bit our brain

is made of gazillions and good zillions

of neurons and those tiny things

communicate with each other process

information and that's how we become

intelligent it took thousands and

thousands of years of evolution and is

such an amazing price

so Times's thought what happened if we

actually turn that and put that into a

computer so first of all Russ will

understand difference and similarities

between artificial neuron and a

biological one so on your left this is a

biological neuron and it has cell bodies

axons and terminal axons and dendrites

and stuff like that and those parts will

take in information and process them and

give you some output

similarly on our right as you can see we

have a bunch of axis and from our

algebra class you might know that X our

input in our case and f of X is a

mathematical calculation and why it's an

output so this picture will represent

the basically the relationship between

neurons since we have so many of them

right and this by altering the

relationship between our neurons which

are called synapses we will be able to

learn and gain a better understanding of

things and synapses are represented as

lines on our right so this is an

animated version of what scientists

believe our neurons would look like so

back in the old days you know in the

1970s and before most of us were born

when scientists wanted to do something

like image recognition or speech

recognition what they had to do is

that'll sit around a table and you know

they'd have to put papers and pens and

start doing math they had to create

lookup tables and this was a pain

because they took so much manpower and

it took a long time so scientists

thought what happened if we give the

computers its own power to learn that

would be magical because lookup tables

would never exist if we can just make

computers learn on his own instead we'll

have computers all knowledge about a sub

Civic subject and this is what this

diagram represents the computers own

knowledge about something and this is

really empowering because scientists no

longer have to create lookup tables for

days and years what they have to do is

just write a simple program train the

computer and then they can do things

like image recognition

and speech recognition in a matter of

seconds so with help from Google cloud

platform we're going to do another

demonstration showing the power of

combining image processing as well as

neural networks so once again this is

all life and we have a great audience

here tonight and we're going to take a

picture take a picture of my phone let's

say and to see what computer things oh

it's a mobile phone it's a products at

gadget that's wonderful so what if we

take a picture of the audience it's a

performance there's audience and say hi

to the camera

great thank you so all of the things

that we just talked about are intangible

just like art music and language and all

of that but technology like that plays

such an important role in our daily

lives for example in Google's

self-driving car project they use image

processing to be able to identify the

difference between a police vehicle and

a normal passenger car and this is

another picture from Google self-driving

car project they combine image

processing and also laser and ultrasonic

sensors to be able to form

three-dimensional models of the cars

surrounding so the car can navigate

safely without lag and this might

surprise you back in the 90s

scientists actually implemented these

technology on fisherman's boats a well

trained computer can can identify the

difference between a tuna and a cod so

next time when dining how is serving you

fish you might appreciate the technical

journey the low fish took to be on your

plate so what's next let's try to answer

this question what will the future look

like with AI well actually jump back in

history and talk about one of the

biggest breakthroughs that we had with

AI and many of you might recall this

historic event between Garrick House

Burrell and the IBM computer

blew the IBM computer became the

first-ever program to defeat a chest a

world chess champion under tournament

rules in a classic game it was a very

significant victory it was a milestone

however later analysis actually played

down the intellectual value of chance as

a game that can be simply defeated by

brute force which means that if you had

enough calculation and enough computing

power chess can be defeated which means

that calculation does not equal to

intelligence and this is a very

important understanding however Google

took a different approach they created

alphago a program like to learn a game

of go as it goes I mean no pun intended

there um go is a program of far less

rules but requires far more intuition

you cannot just calculate what the

possibilities of go so google's alphago

was able to defeat the south korean go

champion lee sedol in a 2016 game and

this was a breakthrough another

breakthrough because the program used

reinforcement learning as well as neural

networks which resembles our own

decision-making process so AI will not

only change our lives in small ways like

we talked about evolve it will likely to

bring us tremendous change change like

we saw 200 years ago with the Industrial

Revolution when humans first harnessed

the power of CO and steam engines change

like we saw in the 1990s when millions

and millions of computers reached homes

across the globe AI will give us

unprecedented amount of power as well as

the opportunity to change imagine

imagine 10 years from now when we're

autonomously constructing a space

station on Mars your car is driving you

to work well you are talking to a friend

on the phone who works in Wall Street

and he doesn't have to worry about stock

tears anymore because AI will ensure a

fair

and safe trading environment also in

hospitals across the globe scientists

are using AI to find mutations in human

DNA databases and also cures for

diseases and these are just some of the

possibilities and the sky is no longer

the limit the power and the freedom that

we have of artificial intelligence is

empowering but also humbling we as

humans are capable of creating machines

that can learn and think just like us in

the long run

AI will not replace biological

intelligence yet it will enhance our

lives it would enhance our future and I

believe that most AI researchers out

there will agree with me on that so

after all you and I and all of us are on

this journey together all of us have the

chance to witness and also decide how

artificial intelligence will shape our

future thank you

you