Trinity of Artificial Intelligence | Anima Anandkumar | TEDxIndianaUniversity
Summary
TLDRIn this talk, the presenter explores the concept of artificial intelligence, focusing on the Trinity of AI: data, learning algorithms, and computer infrastructure. They discuss the importance of each component in creating task-oriented intelligence and highlight the rapid advancements in image classification due to the ImageNet dataset and deep learning models. The presenter also touches on the future of AI, including multi-dimensional data processing with tensors and the integration of AI with robotics to create instinctive, deliberative, and multi-agent robots.
Takeaways
- đ€ The speaker discusses the Trinity of AI, which includes data, learning algorithms, and computer infrastructure as the three main ingredients for AI success.
- đ§ Intelligence is defined as the ability to acquire and apply knowledge and skills, contrasting the pre-programmed actions of robots with the improvisational skills of animals like dogs.
- đ The importance of large datasets, such as ImageNet, is highlighted for training AI systems to recognize and categorize images effectively.
- đŸ The shift from CPU to GPU computing is identified as a key factor in the advancement of AI, with GPUs providing the necessary parallel processing power.
- đ Deep learning models with many layers are crucial for processing complex data like images, allowing AI to learn from examples and improve its performance.
- đ The speaker points out the rapid progress in image classification, noting how AI systems have reached and even surpassed human-level performance on certain datasets.
- đź Future research directions include multi-dimensional data processing using tensors, which can handle data with more than two dimensions, like videos or text documents.
- đ€ The integration of AI (the mind) with robotics (the body) is presented as a challenge and opportunity, aiming to create more instinctive, deliberative, and interactive robots.
- đž An example of AI in robotics is given, where drones learn to land more effectively through data-driven learning rather than pre-programmed algorithms.
- đȘïž The potential for AI to enhance drone performance in adverse conditions, such as different wind patterns, is explored through simulation and data collection.
Q & A
What is the Trinity of AI as discussed in the transcript?
-The Trinity of AI consists of three main ingredients: data, learning algorithms, and computer infrastructure. Data provides the examples or information needed for learning, learning algorithms process the data to extract knowledge, and computer infrastructure provides the necessary computational power to perform these tasks.
Why is data considered the most important ingredient in the Trinity of AI?
-Data is considered the most important ingredient because it provides the examples or observations that an AI system needs to learn from. Without sufficient data, an AI system cannot acquire the knowledge and skills required to perform tasks effectively.
How does the image classification task exemplify the success of AI?
-The image classification task exemplifies the success of AI by demonstrating how AI systems can learn from a large dataset of images, such as ImageNet, to recognize and categorize new images with high accuracy. This progress was made possible by combining data, deep learning models, and powerful computer infrastructure like GPUs.
What is the significance of the ImageNet dataset in AI development?
-The ImageNet dataset is significant because it provided a large and diverse collection of categorized images, which allowed AI systems to learn and improve their ability to recognize and classify images. This dataset was crucial in advancing computer vision and deep learning algorithms.
How do deep learning models contribute to AI's ability to perform tasks?
-Deep learning models contribute to AI's ability to perform tasks by processing input data through multiple layers, allowing the system to learn complex patterns and features. These models can extract relevant information from vast amounts of data, enabling AI systems to perform specific tasks, such as image recognition, with high accuracy.
What is the role of GPU computing in AI?
-GPU computing plays a crucial role in AI by providing the parallel processing capabilities needed to handle the massive computational demands of deep learning algorithms. GPUs enable AI systems to perform billions of operations per image in a highly efficient manner, which is essential for training and deploying large-scale AI models.
Why did the speaker mention the Boston Dynamics Atlas robot and a dog attempting a backflip?
-The speaker mentioned the Boston Dynamics Atlas robot and a dog attempting a backflip to illustrate the difference between pre-programmed actions and true intelligence. While the robot executed a perfect backflip, it did so based on a pre-programmed sequence, whereas the dog, despite failing, demonstrated the ability to learn and adapt, which is a sign of intelligence.
What is the challenge with AI systems when it comes to generalizing beyond their training data?
-The challenge with AI systems when it comes to generalizing beyond their training data is that they may perform well on specific datasets or tasks but struggle to adapt to new or unseen data. This limitation highlights the need for AI systems to develop more robust learning capabilities that allow them to generalize and apply their knowledge to a wider range of situations.
How does the concept of tensors relate to multi-dimensional data processing in AI?
-Tensors are mathematical objects that enable processing in any number of dimensions, generalizing the concepts of vectors and matrices. In AI, tensors are used to handle and analyze multi-dimensional data, such as images, videos, and text, by capturing and processing information across different dimensions, which can lead to more effective learning and understanding of complex data structures.
What is the potential future of integrating AI 'mind' with robotics 'body'?
-The potential future of integrating AI 'mind' with robotics 'body' involves creating robots that are instinctive, deliberative, multi-agent, and behavioral. This integration aims to develop robots that can react and control their reactions in a fine-grained manner, make plans and consider consequences, coordinate and work with others, and interact with humans by understanding emotions and acting as partners.
Outlines
đ€ Introduction to Artificial Intelligence
The speaker begins by introducing the topic of artificial intelligence (AI) and the concept of the 'Trinity of AI'. They discuss the importance of understanding the three main components that make AI work and how they contribute to the future of AI. The speaker contrasts the public's perception of AI, with both its potential and concerns, and poses the question of what constitutes 'intelligence'. They use two videos to illustrate the difference between programmed actions and true intelligence, highlighting that while a robot may perform a complex task like a backflip, it lacks the ability to learn and adapt, which is a characteristic of intelligent beings like dogs. The speaker emphasizes that AI, as it stands, is more about task-oriented intelligence rather than general intelligence, which would mimic human cognitive abilities.
đ The Trinity of AI: Data, Algorithms, and Infrastructure
The speaker delves into the 'Trinity of AI', which consists of data, learning algorithms, and computer infrastructure. They explain that for AI to learn and perform tasks, it requires a vast amount of data or examples. The speaker uses the example of image classification and the ImageNet dataset, which has been pivotal in revolutionizing computer vision by providing a large collection of categorized images. They then discuss the role of deep learning models in processing this data, highlighting the importance of layers of processing to extract knowledge from examples. Lastly, the speaker touches on the necessity of powerful computer infrastructure, like GPUs, to handle the massive computational requirements of deep learning, using NVIDIA's contributions as an example. The speaker concludes by showing how these three elements combined have led to significant progress in AI, particularly in image recognition tasks.
đ Progress in AI: Image Classification and Beyond
The speaker continues by discussing the progress made in AI, particularly in the field of image classification, which has been a significant success story for AI technology. They mention how AI systems have surpassed human performance on the ImageNet dataset, but clarify that this does not mean AI is more intelligent than humans, as it is limited to specific tasks and datasets. The speaker then shifts the focus to the future of AI, discussing the importance of learning in multiple dimensions. They introduce the concept of tensors, which are mathematical objects that can process data across various dimensions, and explain how they can be used to improve AI algorithms. The speaker shares their research on using tensors to categorize text documents, highlighting the Amazon Comprehend tool as an example of this technology in action. They emphasize the potential for multidimensional processing to be applied to a wide range of applications.
đ Integrating AI with Robotics: The Future of Autonomous Systems
In the final paragraph, the speaker looks towards the future of AI, focusing on the integration of AI with robotics, or the 'body' of AI. They discuss the potential for drones to learn from data and improve their performance, such as learning to land more effectively. The speaker also mentions ongoing research at Caltech on teaching drones to fly in adverse conditions, like different wind conditions, by using AI algorithms. The speaker envisions a future where robots are instinctive, deliberative, multi-agent, and behavioral, able to interact with humans and understand emotions, suggesting a bright future for AI and robotics working in tandem.
Mindmap
Keywords
đĄArtificial Intelligence (AI)
đĄTrinity of AI
đĄData
đĄDeep Learning
đĄGeneral Intelligence
đĄImage Classification
đĄGPU Computing
đĄTensors
đĄAutonomous Systems
đĄMulti-agent Systems
Highlights
The Trinity of AI consists of data, learning algorithms, and computer infrastructure.
Intelligence is defined as the ability to acquire and apply knowledge and skills.
Current robots, like the Boston Dynamics Atlas, lack intelligence as they are pre-programmed.
Image classification is a significant success of AI, demonstrating the Trinity of AI in action.
The ImageNet dataset has been pivotal in revolutionizing computer vision with over 14 million categorized images.
Deep learning models with many layers of processing are essential for image recognition.
GPU computing, led by NVIDIA, enables the vast amounts of parallel processing required for deep learning.
AI systems have reached human-level capabilities in image recognition on the ImageNet dataset.
AI's ability to generalize beyond specific datasets remains a significant challenge.
Research is exploring multi-dimensional data processing using mathematical objects known as tensors.
Amazon Comprehend uses tensor-based algorithms to categorize text documents without predefined topics.
The future of AI involves integrating 'mind' (intelligence) and 'body' (robotics) for more autonomous systems.
Caltech's Center for Autonomous Systems and Technology works on merging AI with robotics.
Drones can learn from data to land more efficiently, showcasing the potential of AI in robotics.
Future robots should be instinctive, deliberative, multi-agent, and behavioral to interact effectively with humans.
The talk concludes with a vision of a future where AI and robotics are seamlessly integrated.
Transcripts
[Music]
today I'm gonna talk about artificial
intelligence more specifically the
Trinity of AI what are the three main
ingredients that make AI happen and
what's the fabulous future we are headed
it when it comes to AI and I'll combine
both the academic perspective I have
from Caltech as well as the industry
perspective from Nvidia to see how there
are so many exciting opportunities for
industry academic collaborations to come
together and realize the dream of AI but
before we get there what is a I write
I'm sure everybody here in the audience
has seen AI in the news has seen both
you know amazing progress that AI has
done but also some dystopian future for
AI that people are worried about but
given that it's so much in the news the
first question we should be asking is
what is intelligence before we get to
artificial intelligence what do we even
mean by intelligence right so for this I
want to show you two videos and I want
you to think which one of the two is
more intelligent so let's see that so
the first one you see is the Boston
Dynamics Atlas robot right that's a
pretty cool backflip now what about the
next one sir game it's our best friend
it's also trying to do a backflip
so in the first case you see the robot
perfectly executing the backflip in the
second case you see it failing pretty
clumsily so what do you think is more
intelligent the robot or the dog
yeah it's the dog that's intelligent and
the robot actually has zero intelligence
the way we have it today
so why is that because how do we define
intelligence the intelligence is the
ability to acquire and apply knowledge
and skills right it was the robot doing
that no right at least the way it is
currently it was completely
pre-programmed even though the bakflip
looks so impressive it was all planned
beforehand and the program was built
into the robot on the other hand the dog
is improvising it's always observing its
environment maybe one day it'll also
learn to do the backflip better and so
the dog is intelligent while our current
robots are mostly not and so there you
see a very big gap of where we are when
it comes to building intelligence into
our robots into our systems and indeed
when we think about general intelligence
the gap is even wider so general
intelligence refers to have a human
level cognitive ability and artificial
systems are so far from it so when I
refer to AI or artificial intelligence I
usually mean some task oriented
intelligence it's the ability to execute
a specific task very well right to learn
from examples and to execute a given
task and that's what I'll show you how
we are making progress on and still the
vast challenges that lie ahead
so given that we got the definitions out
of the way let's now see what is the
Trinity of AI so for AI to be successful
I
see three important ingredients the very
first one and perhaps the most important
one is the data right so when I say data
I mean
examples you know like how can a system
learn it needs information observations
or data and so once it has it there are
learning algorithms which can process
the data and extract knowledge from
those examples but the third important
ingredient is the computer
infrastructure all this processing needs
a substrate how do we do this vast
amounts of processing so we need all
these three to come together for an AI
task to be successful let's now see an
example application and see how the
progress came about by putting this
Trinity together so the image
classification task has been perhaps the
most visible success of AI today as you
see here in the picture there is the
pool there are plans so there's a lot of
things in this image and as human beings
we find it's so natural to absorb and
reason about the entire image right but
it's really hard for a machine to do
that because it has to see lots of
variations of a swimming pool before it
realizes oh this is a swimming pool
right so it needs a lot of examples to
learn what a swimming pool looks like
and that's where we have the first
ingredient in our Trinity that is the
data how do we get lots of examples of
images for different categories so the
image net data set has been now has
revolutionized how we've done computer
vision and that's because for the first
time we had such a large collection of
categorised images this is more than 14
million images and more than thousand
categories and if you even look at one
example category here you see all the
variations of how natural images come
about for instance when you say fish
you know you just see so many different
variations of how a fish can occur in an
image and so the learning algorithm
needs to have access to all these
different variations for it to recognize
what a fish looks like in a new example
so that was the first ingredient the
data the second ingredient are the
algorithms and the models how do we take
these images and extract the information
so that an AI system can automatically
categorize images in a seamless way and
that's where we have the deep learning
models so the term deep refers to the
fact that there are many layers of
processing so you transform your input
image across these layers and in the
output you want an answer
you want to save with how much
confidence do you think there is a dog
in this image and if you have a good
algorithm and you've trained your system
well hopefully you will have a high
confidence that this particular image
has a dog in it right and so you need a
model that's highly flexible that can
learn from millions of examples and
extract the relevant information to
realize what looks like a dog
so those were the two ingredients the
third one is the computer infrastructure
so if you take these current deep
learning systems it is just not scalable
to run it on a normal computer so the
computers of yesteryear so the CPUs had
a wave of progress but now the curve of
their growth is plateauing and so the
new form of computing is known as the
GPU computing it stands for graphics
processing unit because you know it goes
back to how graphics was processed in a
parallel manner but the same form of
technology is also highly relevant for
processing a
because processing in these deep
learning layers requires more than
billion operations for each image but
they can be done in a highly parallel
manner and hence NVIDIA GPUs have been
at the forefront of enabling computation
at scale and have been a crucial
ingredient to realize this dream of
large-scale AI so now you see how all
these three ingredients came together so
we had the data we have the deep
learning model with learning algorithms
and then we had the GPU computing and
when they came together it created the
steep learning revolution we were able
to make progress in such a short length
of time so what you see is the errors of
that an AI system makes in recognizing
the image what are the categories in an
image over this image net dataset and
you see within a span of few years we
could get to human level capability on
this data set and so to have this
capability on such a large dataset in
such a short amount of time was what was
surprising to so many scientists and so
that shows that there is a lot of
promise that a I can still realize in
many other domains I do want to point
out one thing though if you see in 2015
on the image net data set the AI system
is doing better than a human being
so does this mean the AI system got more
intelligent than a human no that's not
the case right because it was it's only
doing better on only this data set and
that's always been a challenge with AI
if you have to now take it beyond to
other data sets or to other tasks so we
need the ability to generalize beyond
what it's seen in a current data and
that still remains in this case as well
so that is great we were able to make
fast progress but where do we go next so
my research looks at how to do learning
in many dimensions so what are
dimensions mean for data so if you think
of an image you have the width and the
height of an image and then you've the
colored image you have the number of
channels now if you took take a video
you also have the time dimension so when
you collect rich forms of data you have
all many dimensions coming together for
instance you may also have texts that is
connected to the video you may have
other forms of data that are all
processed together so how do we merge
all this very efficiently and process
them at scale so the idea is we don't
want to remove or throw away the
dimensions in our data if you do that we
can potentially lose information to help
you visualize this you know you have the
three-dimensional cube here and if you
just look at its projection on the wall
you do not see the complete picture
right and it's the same with data it's
if you're not processing it effectively
and not incorporating the information
from all different dimensions you may
potentially not well learn very well
what my research does is to use
mathematical objects known as tensors to
enable such multi-dimensional processing
so tensors or mathematical objects that
do processing in any dimension and
generalize the concept of vectors and
matrices that you probably see in an
undergraduate class so these may seem
like complicated mathematical objects
but you can realize them into real
algorithms for many applications so one
application I've looked at is how to
automatically categorize text documents
so if you have many millions of
documents
can you quickly say what are the topics
discussed in various documents right and
you feel don't even know what the
underlying topics are you don't have
examples of them when you're learning
it's even a harder task but we have a
tool that I built with my team at Amazon
called Amazon comprehend that precisely
does this and what enables this
algorithm was the concept of tensors so
intuitively what it's looking at are the
relationships of words in a document so
if there are words that are commonly
occurring together that's referred to a
topic you should be able to extract that
information and to do that at scale over
millions of documents requires more
thinking but that's precisely what this
algorithms are able to do they can take
this Corcoran's tensor of Triplets of
words and decompose them to obtain
topics that are present in different
documents and hence you can translate
mathematical concepts into actual
algorithms and then deploy them at scale
to make an impact on various
applications so I see a lot of
possibilities in future to take this
multi-dimensional processing into many
different applications so what's another
step for the future so I want to think
about both the mind and the body so far
I only talked about AI as the mind right
how do we create intelligence how do we
train a an AI system but there's also
the body which is the robotics so I
showed you in the beginning the Atlas
robot that currently is not intelligent
if you want to change that we have to
bring the two together and at Caltech we
have the Center for autonomous systems
and technology that aims to do that so
I'll show you one example we just
started working on which was to ask can
we
have the drones learn to land better so
instead of a pre-programmed algorithm
can the drone learn from lots of data
you know lots of sensors that it's
outfitted with and Bill learning to land
in a better way so let's see what was
the outcome so on the left you see the
draw and when there is no learning it's
a program controller and in the right
there is the learning and if you see on
the right the drone landed whereas it's
was hovering around in a pre-programmed
controller so this means you know there
is a lot of potential that learning can
provide to make drone flights more
efficient and ultimately autonomous
another project we are currently
thinking of is how can we have drones
I'll fly in adverse conditions and here
we can simulate wind conditions of all
different forms in the drone testing
laboratory at Caltech and then you
collect that data to design AI
algorithms that can be more effective in
countering such wind conditions if you
want to bring the mind at the body
together you want to build all different
forms of intelligence and meld them
together with the body so we want the
robots of your future to be instinctive
right so humans have instinct inbuilt in
us which is the ability to react and
control our reactions in a fine-grained
manner how do we do that in a robot
we're also deliberative we make plants
we look at consequences
how do we put do that in a robot it's
also multi-agent we are a society we
coordinate and work together with others
how do we get the robots to do the same
and ultimately the last one perhaps the
most difficult one is behavioral how can
we have robots that can interact
with us that can understand human
emotions ultimately be a partner with us
and that's perhaps you know gonna be a
bright new future for us thanks so much
you
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