The moment we stopped understanding AI [AlexNet]
Summary
TLDRThis video explores the inner workings of AI models like Chat GPT and AlexNet, revealing how simple compute blocks, when scaled massively with data, can perform complex tasks. It delves into the concept of embedding spaces, where high-dimensional data is organized, and how models like AlexNet learn to recognize patterns without explicit instructions. The video also highlights the power of deep learning and the challenges in understanding these models, ending with a discussion on the future of AI and its potential breakthroughs.
Takeaways
- ๐ง The script discusses the inner workings of AI models like AlexNet and Chat GPT, emphasizing the high-dimensional spaces they use to understand the world.
- ๐ AlexNet, introduced in 2012, was a breakthrough in AI, demonstrating the power of scaling up neural networks for computer vision tasks.
- ๐ AlexNet's success hinged on the use of convolutional blocks, which are a type of compute block that can detect patterns in images.
- ๐ค Chat GPT operates on a similar principle but for language, using 'transformers' to process and generate human-like text based on input matrices.
- ๐ The script highlights the importance of vast amounts of data for training AI models, which allows them to learn complex patterns and behaviors.
- ๐ The intelligence of models like Chat GPT is not inherent but emerges from the combination of simple operations on large datasets.
- ๐ AlexNet's first layer learns to detect edges and color blobs, which are foundational for recognizing more complex visual concepts.
- ๐ฎ Deep learning models map inputs to high-dimensional spaces where similar concepts are close together, forming a kind of 'activation atlas'.
- ๐ The script mentions 'feature visualization', a technique that generates images designed to maximize specific neural activations, revealing what the model has learned.
- ๐ฏ AlexNet's performance in the ImageNet competition marked a shift towards data-driven AI and away from expert-crafted algorithms.
- ๐ The scale of data and compute power is a defining characteristic of modern AI, with models like Chat GPT having over a trillion parameters.
Q & A
What was the significance of the 2012 AlexNet paper in the field of computer vision?
-The AlexNet paper was significant because it demonstrated the effectiveness of deep learning in computer vision. It shocked the community by showing that an old AI idea, when scaled up, could perform exceptionally well. It marked the beginning of a new era in AI, where deep neural networks became the dominant approach.
What is the basic function of a Transformer block in AI models like Chat GPT?
-A Transformer block in AI models like Chat GPT performs a set of fixed matrix operations on an input matrix of data and typically returns an output matrix of the same size. These blocks are fundamental to the model's ability to process and generate responses based on the input data.
How does Chat GPT formulate a response to a user's query?
-Chat GPT formulates a response by breaking down the query into words and word fragments, mapping each to a vector, and stacking these vectors into a matrix. This matrix is then processed through multiple Transformer blocks. The model predicts the next word or word fragment based on the final output matrix, which is appended to the original output and fed back into the model until a stop word fragment is reached.
What is the role of the final output matrix's last column in Chat GPT's response generation?
-The last column of Chat GPT's final output matrix is mapped from a vector back to text to generate the next word or word fragment in the response. This process is repeated with each new word fragment being added to the input matrix until a stop word fragment is returned.
How does the training of AlexNet differ from that of Chat GPT in terms of the task they are designed to perform?
-AlexNet is trained to predict a label given an image, whereas Chat GPT is trained to predict the next word fragment given some text. Both models learn from large datasets, but the nature of the task and the type of data they process are different.
What is the purpose of the convolutional blocks in the first layers of AlexNet?
-The convolutional blocks in the first layers of AlexNet are used to detect basic visual patterns like edges and color blobs in the input image. These blocks transform the image by sliding smaller tensors, or kernels, across the image and computing the dot product at each location, which serves as a similarity score.
How does the visualization of AlexNet's first layer kernels help us understand what the model has learned?
-The visualization of AlexNet's first layer kernels as RGB images provides insight into the basic visual patterns the model has learned to detect, such as edges and color blobs. This helps us understand how the model begins to interpret the input image at a fundamental level.
What is an 'activation atlas' and how does it help visualize the embedding spaces of deep neural networks?
-An activation atlas is a visualization technique that shows how deep neural networks organize the visual world or concepts in high-dimensional embedding spaces. It provides a way to see smooth visual transitions between related concepts and understand how the model represents different ideas in its internal space.
How do the synthetic images generated by feature visualization help in understanding a model's learned representations?
-Synthetic images generated by feature visualization are optimized to maximize a given activation. These images provide a visual representation of what a specific activation layer is looking for, offering another way to see the learned representations within the model.
What was the key difference in 2012 that allowed AlexNet to achieve unprecedented success in the ImageNet competition?
-The key difference in 2012 was the scale of data and compute power available. The ImageNet dataset provided a large labeled dataset, and the use of Nvidia GPUs provided significant computational power, allowing AlexNet to learn from vast amounts of data with its deep neural network architecture.
How does the scale of parameters in AI models like AlexNet and Chat GPT contribute to their performance and complexity?
-The scale of parameters in AI models directly contributes to their performance by allowing them to learn more complex patterns and representations. However, it also increases the complexity and difficulty in understanding how these models work, as seen in the exponential growth from AlexNet's 60 million parameters to Chat GPT's over a trillion parameters.
Outlines
๐ง The Emergence of AI Intelligence Through Scale
This paragraph introduces the concept of AI models like AlexNet and chat GPT, which utilize high-dimensional spaces for data representation. AlexNet, introduced in 2012, demonstrated the power of scaling up AI ideas with a simple 8-page paper. It laid the groundwork for models like chat GPT, which uses 'transformers' to process input data through a series of matrix operations. The paragraph emphasizes the non-intuitive nature of these models, which lack obvious signs of intelligence but perform complex tasks through repeated matrix manipulations. It also touches on the training process of these models on vast datasets, which allows them to develop the ability to perform tasks like writing essays and solving math problems.
๐ Deep Dive into Neural Network Layers and Feature Visualization
This paragraph delves into the inner workings of neural networks, specifically AlexNet, and how they learn to recognize patterns. It explains the role of convolutional blocks and how they transform input images into activation maps that highlight areas of the image that match learned kernels. The paragraph discusses the progression from simple feature detection, such as edges and color blobs, to complex concepts like faces, which are learned without explicit instruction. It also introduces feature visualization techniques that generate synthetic images to maximize specific activations, providing insight into what the network has learned.
๐จ Activation Atlases: Visualizing Neural Network Embedding Spaces
The paragraph discusses the creation of activation atlases, which are visualizations that represent the high-dimensional embedding spaces of neural networks. These atlases show how models like AlexNet organize visual concepts, with similar concepts being close to each other in the embedding space. The paragraph describes how synthetic images are used to create a two-dimensional projection of these spaces, allowing for a visual walk through the model's understanding of concepts. It also touches on the semantic meaningfulness of the embedding space directions and how they can be manipulated to shift attributes like age or gender in images.
๐ The Evolution and Scale of AI: From AlexNet to chat GPT
This paragraph reflects on the historical context and evolution of AI, highlighting the significance of AlexNet's victory in the ImageNet competition and the shift in AI approaches that followed. It discusses the scalability of neural networks and the computational advancements that have allowed models like chat GPT to grow to over a trillion parameters. The paragraph also contemplates the future of AI, considering the possibility of new breakthroughs emerging from scaling up existing models or the resurgence of older AI methods.
๐ค The Complexity and Unpredictability of AI Development
The final paragraph addresses the complexity and unpredictability inherent in AI development. It acknowledges the difficulty in understanding the inner workings of models with vast numbers of parameters and the challenge of visualizing high-dimensional spaces. The paragraph also reflects on the historical underestimation of the potential of neural networks and the surprising resurgence of older AI techniques, such as those used in AlexNet. It concludes with a nod to the ongoing exploration and discovery in the field of AI, emphasizing the importance of continued research and development.
Mindmap
Keywords
๐กActivation Atlas
๐กAlexNet
๐กTransformers
๐กConvolutional Blocks
๐กEmbedding Space
๐กFeature Visualization
๐กNearest Neighbors
๐กLanguage Models
๐กBackpropagation
๐กNeural Networks
๐กHigh-Dimensional Representation
Highlights
Introduction of the Activation Atlas, a tool to visualize high-dimensional spaces used by AI models.
AlexNet's groundbreaking impact on computer vision in 2012 by demonstrating the effectiveness of scaled AI models.
The role of Ilya Sutskever in co-founding OpenAI and the development of models like Chat GPT.
The inner workings of Chat GPT, emphasizing the use of transformer blocks and matrix operations.
How Chat GPT formulates responses through a series of matrix transformations and vector mappings.
The surprising simplicity of GPT's response generation from the final output matrix's last column.
The importance of data in training models like AlexNet and Chat GPT for high performance.
AlexNet's training to predict image labels and its comparison to Chat GPT's text prediction.
Visualization of the first convolutional layer in AlexNet and its learned patterns.
The transformation of images through convolutional blocks and the creation of activation maps.
How AlexNet's deeper layers respond to higher-level concepts without explicit instructions.
Feature visualization technique to understand what specific activation layers are detecting.
The concept of high-dimensional embedding spaces in AI models like AlexNet.
The nearest neighbor experiment showing similar concepts in high-dimensional space.
The significance of the perceptron and backpropagation in training deep neural networks.
The scale of data and compute power as the key to the success of AI models like AlexNet and Chat GPT.
The comparison between the computational cost of older AI approaches and the efficiency of modern models.
The unpredictability of AI breakthroughs and the potential for future advancements.
The role of activation atlases in visualizing and understanding the organization of concepts in AI models.
Sponsorship mention of KiwiCo and its focus on educational products for children.
Transcripts
this is an activation Atlas it gives us
a glimpse into the high-dimensional
embedding spaces modern AI models use to
organize and make sense of the world the
first model to really see the world like
this alexnet was published in 2012 in an
8-page paper that shocked the computer
vision Community by showing that an old
AI idea would work unbelievably well
when scaled the paper second author ilas
HK would go on co-found open AI where he
and the open AI team would massively
scale up this idea again to create chat
GPT this video is sponsored by kiwico
more on them later if you look under the
hood of chat GPT you won't find any
obvious signs of intelligence instead
you'll find layer after layer of compute
blocks called transformers this is what
the T and GPT stands for each
Transformer performs a set of fixed
Matrix operations on an input Matrix of
data and typically returns an output
Matrix of the same size to figure out
what it's going to say next chat GPT
breaks apart what you ask get into words
and word fragments Maps each of these to
a vector and stacks all of these vectors
together into a matrix this Matrix is
then passed into the first Transformer
block which returns a new Matrix of the
same size this operation is then
repeated again and again 96 times in
chat GPT 3.5 and reportedly 120 times in
chat GPT 4 now here's the Absurd part
with a few caveats the next word or word
fragment that chat GPT says back to you
is is literally just the last column of
its final output Matrix mapped from a
vector back to text to formulate a full
response this new word or word fragment
is appended to the end of the original
output and this new slightly longer text
is fed back into the input of chat GPT
this process is repeated again and again
with one new column added to the input
Matrix each time until the model's
output returns a special stop word
fragment and that is it one Matrix
multiply after another GPT slowly morphs
the input you give it into the output it
returns where is the
intelligence how is it that these 100 or
so blocks of dumb compute are able to
write essays translate language
summarized books solve math problems
explain complex Concepts or even at the
next line of this script the answer lies
in the vast amounts of data these models
are trained on okay pretty good but not
quite what I wanted to say next the
alexnet paper is significant because it
marks the first time we really see
layers of compute blocks like this
learning to do unbelievable things an AI
Tipping Point towards high performance
in scale and away from explainability
while chat GPT is trained to predict the
next word fragment given some text Alex
net is trained to predict a label given
an image the input image to alexnet is
represented as a three-dimensional
Matrix or tensor of RGB intensity values
and the output is a single Vector of
length 1,000 where each entry
corresponds to Alex Net's predicted
probability that the input put image
belongs to one of the a thousand classes
in the imag net data set things like
tabby cats German Shepherds hot dogs
toasters and aircraft
carriers just like chat GPT today
alexnet was somehow magically able to
map the inputs we give it into the
outputs we wanted using layer after
layer of compute block after training on
a large data set one nice thing about
Vision models however is that it's
easier to poke around under the hood and
get some idea of what the model has
learned one of the first under the hood
insights that kvky suit and Hinton show
in the Alex net paper is that the model
has learned some very interesting visual
patterns in its first layer the first
five layers of alexnet are all
convolutional blocks first developed in
the late 1980s to classify handwritten
digits and can be understood as a
special case of the Transformer blocks
in chat GPT and other large language
models in convolutional blocks the input
image tensor is transformed by sliding a
much smaller tensor called a kernel of
learned weight values across the image
and at each location Computing the dot
product between the image and kernel
here it's helpful to think of the dot
product as a similarity score the more
similar a given patch of the image and
kernel are the higher the resulting dot
product will be Alex net uses 96
individual kernels in its first layer
each of Dimension 11 by 11 by3 so
conveniently we can visualize them as
little RGB images these images give us a
nice idea of how the first layer of
alexnet sees the image the upper kernels
in this figure show where Alex and has
clearly learned to detect edges or rapid
changes from light to dark at various
angles images with similar patterns will
generate High Dot products with these
kernels below we see where Alexon has
learned to detect Blobs of various
colors these kernels are all initialized
as random numbers and the patterns we're
looking at are completely learned from
data sliding each of our 96 kernels over
the input image and Computing the dot
product at each location produces a new
set of 96 matrices sometimes called
activation Maps conveniently we can view
these as images as well the activation
Maps show us which parts of an image if
any match a given kernel well if I hold
up something visually similar to a given
kernel we see high activation in that
part of the activation
map notice that it goes away when I
rotate the pattern by 90ยฐ the image and
kernel are no longer aligned you can
also see various activation Maps picking
up edges and other lowl features in our
image of course finding edges and color
blobs in images is still hugely removed
from recognizing complex Concepts like
German Shepherds or aircraft carriers
what's astounding about deep neural
networks like alexnet and chat GPT is
that from here all we do is repeat the
same operation again just with a
different set of learned weights for
Alex net this means that these 96
activation maps are stacked together
into a tensor that become the input to
the exact same type of convolutional
compute block the second overall layer
in the model we can make our activations
easier to see by removing the values
close to zero unfortunately in our
second layer we can't learn much by
simply visualizing the weight values and
the kernels themselves the first issue
is that we just can't see enough colors
the depth of the kernel has to match the
depth of the incoming data in the first
layer of alexnet the depth of the
incoming data is just three because the
model takes in color images with red
green and blue color channels however
since the first layer computes 9 6
separate activation Maps the computation
in the second layer of alexnet is like
processing images with 96 separate color
channels the second factor that makes
what's happening in the second layer of
alexnet more difficult to visualize is
that the dot products are really taking
weighted combinations of the
computations in the first layer we need
some way to visualize how the layers are
working together a simple way to see
what's going on is to try to find parts
of various images that strongly activate
the outputs of the second layer for
example this activation map appears to
be putting together Edge detectors to
form basic Corners remarkably as we move
deeper into alexnet strong activations
correspond to higher and higher level
concepts by the time we reach the fifth
layer we have activation maps that
respond very strongly to faces and other
highlevel Concepts and what's incredible
here is that no one explicitly told Alex
net what a face is all alexnet had to
learn from were the images and labels in
the imag net data set which does not not
contain a person or a face class Alex
net was able to learn completely on its
own both that faces are important and
how to recognize them to better
understand what a given Colonel and Alex
net has learned we can also look at the
examples in the training data set that
give the highest activation values for
that kernel for our face kernel not
surprisingly we find examples that
contain people finally there's this
really interesting technique called
feature visualization where we can
generate synthetic images that are
optimized to maximize a given activation
these synthetic images give us another
way to see what a specific activation
layer is looking
for by the time we reach the final layer
of alexnet our image has been processed
into a vector of length
4,096 the final layer performs one last
Matrix computation on this Vector to
create a final output Vector of length
1,000 with one entry for each of the
classes in the imag net data set chfi
suit and Hinton noticed that the second
to last layer Vector demonstrated some
very interesting properties
one way to think about this Vector is as
a point in 4,096 dimensional space each
image we pass into the model is
effectively mapped to a point in this
space all we have to do is just stop one
layer early and grab this Vector just as
we can measure the distance between two
points in 2D space we can also measure
the distance between points or images in
this high-dimensional space hinton's
team ran a simple experiment where they
took a test image in the imag net data
set computed its corresponding vector
and then search for the other images in
the imag net data set that were closest
or the nearest neighbors to the test
image in this High dimensional space
remarkably the nearest neighbor images
showed highly similar Concepts to the
test images in figure four from the Alex
net paper we see an example where an
elephant test image yields nearest
neighbors that are all
elephants what's interesting here too is
that the pixel values themselves between
these images are very different Alex net
really has learned high-dimensional
representations of data where similar
concepts are physically close this
high-dimensional space is often called a
latent or embedding space in the Years
following the alexnet paper it was shown
that not only distance but
directionality in some of these
embedding spaces is Meaningful the demos
you see where faces are age or gender
shifted often work by first mapping an
image to a vector in an embedding space
and then literally moving this point in
the age or gender Direction in that
embedding space and then mapping the
modified Vector back to an image
before we get into activation atlases
which give us an amazing way to
visualize these embedding spaces please
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now back to alexnet there's some really
amazing work that combines the synthetic
images that maximize a given set of
activations with a two-dimensional
projection or flattening out of the
embedding space to make these incredible
visualizations called activation atlases
Neighbors on the activation Atlas are
generally close in the embedding space
and show similar Concepts the model has
learned we're getting a peak into how
deep neural networks organize the visual
world looking at the synthetic images
that most activate neighborhoods of
neurons we can visually walk through the
embedding space of the model seeing it
Mak smooth visual transitions from
Concepts like zebras to Tigers to
leopards to rabbits moving to the middle
layers of the model we can see less
fully formed but still meaningful
Concepts moving along this path
amazingly correlates with the number and
size of pieces of fruit in an image the
same princip applies in large language
models words and word fragments are
mapped to vectors in an embedding space
where words with similar meanings are
close to each other and the directions
in the embedding space are sometimes
semantically meaningful there's some
incredible very recent work from the
team at anthropic that shows how sets of
activations can be mapped to Concepts in
language these results can help us
better understand how llms work and can
be used to modify Model Behavior after
clamping a set of activations that
correspond to the concept Golden Gate
Bridge to a high value the llm the team
was experimenting with began to identify
itself as the Golden Gate Bridge Alex
net won the imag net large scale visual
recognition challenge by a wide margin
in 2012 the third year the challenge was
run in Prior years the winning teams
used approaches that under the hood look
much more like what you might expect to
find in an intelligent system the 2011
winner used a complex set of very
different algorithms starting starting
with an algorithm called cift which is
composed of specialized image analysis
techniques developed by experts over
many years of research Alex net in
contrast is an implementation of a much
older AI idea an artificial neural
network where the behavior of the
algorithm is almost entirely learned
from data the dot product operation
between the data and a set of Weights
was originally proposed by molic and
pits in the 1940s as a dramatically
oversimplified model of the neurons in
our brain in the second half of each
Transformer Block in chat GPT and at the
end of alexnet you'll find a multi-layer
perceptron the perceptron is a learning
algorithm and physical machine from the
1950s that uses molic and pits neurons
and can learn to perform basic shaped
recognition tasks back in the 1980s a
younger Jeff Hinton and his
collaborators at Carnegie melon showed
how to train multiple layers of these
perceptrons using a multivariate
calculus technique called back
propagation these models a couple layers
deep and remarkably pretty good at
driving cars in the 1990s Yan laon now
Chief AI scientist at meta was able to
train five layer deep models to
recognize handwritten digits despite the
intermittent successes of artificial
neural networks over the years this
approach was hardly the accepted way to
do AI right up until the publication of
alexnet if this was obviously the way to
build intelligence systems we would have
done it decades earlier as Ian
Goodfellow writes in his excellent deep
learning book at this point deep
networks were generally believed to be
very difficult to train we now know that
algorithms that have existed since the
1980s work quite well but this was not
apparent CC 2006 the issue is perhaps
simply that these algorithms were too
computationally costly to allow much
experimentation with the hardware
available at the time the key difference
in 2012 was simply scale of data and
scale of compute the imag net data set
was the largest labeled data set of its
kind kind to date with over 1.3 million
images and thanks to Nvidia gpus in 2012
hinton's team had access to roughly
10,000 times more compute power than Yan
laon had 15 years before laon's layet 5
model had around 60,000 learnable
parameters Alex net increased this a
thousandfold to around 60 million
parameters today chat GPT has well over
a trillion parameters making it over
10,000 times larger than alexnet this
mindboggling scale is the Hallmark of
this third wave of AI we find ourselves
in today driving both their performance
and the fundamental difficulty in
understanding how these models are able
to do what they do it's amazing that we
can figure out that Alex net learns
representations of faces and that large
language models learn representations of
Concepts like the Golden Gate Bridge but
there are many many more Concepts these
models learn that we don't even have
words for Activation atlases are
beautiful and fascinating but very
low-dimensional projections of very high
dimensional spaces where our spatial
reasoning abilities often fall apart
it's notoriously difficult to predict
where AI will go next almost no one
expected the neural networks of the 80s
and 90s scaled up by three or four
orders of magnitude to yield alexnet and
it was almost impossible to predict that
a generalization of the compute blocks
in alexnet scaled up by forers of
magnitude would yield chat GPT maybe the
next AI breakthrough is just another
three to four orders of magnitude of
scale away or maybe some mostly
forgotten approach to AI will resurface
as Alex net did in 2012 we'll have to
wait and
see are you mad that I called the blocks
of compute
[Music]
dumb not at
all describing the compute blocks as
dumb highlights the impressive nature of
how simple operations can combine to
produce intelligent
Behavior it's a great way to emphasize
the power of the underlying algorithms
and training data
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