Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield
Summary
TLDRThis 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
💡 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.
🌐 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.
🤖 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.
🚀 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.
Mindmap
Keywords
💡World War Two
💡Alan Turing
💡Artificial Intelligence (AI)
💡Machine Learning
💡Google Translate
💡Neural Networks
💡Image Processing
💡Reinforcement Learning
💡Self-Driving Cars
💡DNA Databases
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
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