Has Generative AI Already Peaked? - Computerphile
Summary
TLDRThe video script discusses the limitations of generative AI and the concept of CLIP embeddings, which are trained to match images with text descriptions. It challenges the notion that simply adding more data and bigger models will inevitably lead to general intelligence. The paper referenced suggests that the data required for zero-shot performance on new tasks is astronomically high, implying a plateau in AI capabilities without new strategies or representations. The script also touches on the imbalance in data representation and its impact on AI performance across various tasks.
Takeaways
- 🧠 The script discusses the concept of CLIP (Contrastive Language-Image Pre-training) embeddings, which are representations learned from pairing images with text to understand and generate content.
- 🔮 There's an ongoing debate about whether adding more data and bigger models will eventually lead to general intelligence in AI, with some tech companies promoting this idea for product sales.
- 👨🔬 The speaker, as a scientist, emphasizes the importance of experimental evidence over hypotheses about AI's future capabilities and challenges the idea of AI's inevitable upward trajectory.
- 📊 The paper mentioned in the script argues against the notion that more data and larger models will solve all AI challenges, suggesting that the amount of data needed for general zero-shot performance is unattainably large.
- 📈 The paper presents data suggesting that performance gains in AI tasks may plateau despite increasing data, implying a limit to how effective current AI models can become.
- 📚 The script highlights the importance of data representation, mentioning that over-represented concepts like 'cats' perform better in AI models than under-represented ones like 'specific tree species'.
- 🌐 The discussion touches on downstream tasks enabled by CLIP embeddings, such as classification and recommendation systems, which can be used in services like Netflix or Spotify.
- 📉 The paper's findings indicate a potential logarithmic relationship between data amount and performance, suggesting diminishing returns on investment in data and model size.
- 🚧 The speaker suggests that for difficult tasks with under-represented data, current AI strategies may not suffice and alternative approaches may be necessary.
- 🌳 The script uses the example of identifying specific tree species to illustrate the challenge of applying AI to complex, nuanced problems with limited data.
- 🔑 The paper and the speaker both point to the uneven distribution of data as a significant barrier to achieving high performance across all potential AI tasks.
Q & A
What is the main topic discussed in the video script?
-The main topic discussed is the concept of CLIP (Contrastive Language-Image Pre-training) embeddings and the debate around the idea that adding more data and bigger models will lead to general intelligence in AI.
What is the general argument made by some tech companies regarding AI and data?
-The argument is that by continuously adding more data and increasing model sizes, AI will eventually achieve a level of general intelligence capable of performing any task across all domains.
What does the speaker suggest about the idea of AI achieving general intelligence through data and model size alone?
-The speaker suggests skepticism, stating that the idea needs to be experimentally justified rather than hypothesized, and refers to a recent paper that argues against this notion.
What does the paper mentioned in the script argue against?
-The paper argues against the idea that simply adding more data and bigger models will eventually solve all AI challenges, stating that the amount of data needed for general zero-shot performance is astronomically vast and impractical.
What are the potential downstream tasks for CLIP embeddings mentioned in the script?
-The potential downstream tasks mentioned include classification, image recall, and recommender systems for services like Spotify or Netflix.
What does the script suggest about the effectiveness of current AI models on difficult problems?
-The script suggests that current AI models may not be effective for difficult problems without massive amounts of data to support them, especially when dealing with under-represented concepts.
What does the speaker mean by 'zero-shot classification' in the context of the script?
-Zero-shot classification refers to the ability of a model to classify an object or concept without having seen examples of it during training, by relying on the embedded space where text and images are matched.
What does the script imply about the distribution of classes and concepts in current AI datasets?
-The script implies that there is an uneven distribution, with some concepts like cats being over-represented, while others like specific tree species are under-represented in the datasets.
What is the potential implication of the findings in the paper for the future of AI development?
-The implication is that there may be a plateau in AI performance improvements, suggesting that more data and bigger models alone may not lead to significant advancements and that alternative strategies may be needed.
What is the speaker's stance on the current trajectory of AI performance improvements?
-The speaker is cautiously optimistic but leans towards a more pessimistic view, suggesting that the current approach may not yield the expected exponential improvements in AI performance.
What is the role of human feedback in training AI models as mentioned in the script?
-Human feedback is suggested as a potential method to improve the training of AI models, making them more accurate and effective, especially for under-represented concepts.
Outlines
🧠 AI's Limitations in General Intelligence
The script discusses the concept of clip embeddings in generative AI, which is the process of learning to represent images and text in a shared space. It challenges the notion that simply adding more data and bigger models will inevitably lead to general intelligence. The speaker highlights a recent paper that argues the amount of data needed for zero-shot performance on new tasks is astronomically high and may not be feasible. The paper suggests that the effectiveness of models like CLIP (Contrastive Language-Image Pre-training) for downstream tasks diminishes as the complexity of the task increases, especially when dealing with underrepresented concepts. The speaker emphasizes the importance of experimental evidence over speculation about AI's capabilities.
📈 Data Abundance vs. Model Performance
This paragraph delves into the relationship between the volume of data and the performance of AI models in downstream tasks such as classification and recommendation systems. The speaker describes an experiment where the prevalence of concepts in datasets is measured and compared against the performance of these tasks. The graph illustrates that as the number of examples for a specific concept increases, the performance improvement plateaus, suggesting a limit to the effectiveness of adding more data. The speaker questions the optimistic view that more data will lead to an AI explosion and instead presents a more pessimistic or realistic outlook where performance gains are marginal and costly.
🌳 The Challenge of Underrepresented Data in AI
The speaker addresses the issue of underrepresented data in AI training sets, using the example of specific tree species being less common than general categories like cats or dogs. This leads to poorer performance when AI models are tasked with identifying more specific or obscure items. The script also touches on the potential inefficiency of relying solely on data collection to improve AI performance. It suggests that alternative methods may be necessary to achieve high performance on difficult tasks that are not well-represented in typical datasets. The speaker also speculates on the future of AI development, pondering whether we might be reaching a plateau in performance improvements.
Mindmap
Keywords
💡Clip Embeddings
💡Generative AI
💡General Intelligence
💡Zero-Shot Performance
💡Vision Transformer
💡Text Encoder
💡Recommended System
💡Downstream Tasks
💡Concepts
💡Data Distribution
💡Hallucination
Highlights
The concept of using generative AI for producing new sentences and images and understanding various forms of data.
The potential for AI to develop general intelligence through training on vast amounts of image-text pairs.
The skepticism about the inevitability of achieving general AI capabilities by simply scaling up data and models.
The importance of experimental evidence in scientific hypotheses rather than speculation about AI's future capabilities.
A recent paper challenging the idea that more data and bigger models will inevitably lead to general zero-shot performance.
The argument that the data requirements for general AI are so vast they may be unattainable.
The role of CLIP embeddings in finding a shared representation for images and text.
The application of CLIP embeddings in downstream tasks such as classification and recommender systems.
The paper's findings that massive amounts of data are needed for effective application of downstream tasks on difficult problems.
The limitations of current models in performing well on under-represented or more complex concepts.
The paper's methodology of defining core concepts and analyzing their prevalence and performance in data sets.
The graphical representation used in the paper to illustrate the relationship between data amount and task performance.
The differing perspectives on AI development: the optimistic 'AI explosion' vs. the paper's more cautious outlook.
The paper's evidence suggesting a plateau in performance improvement despite increasing data and model size.
The challenge of efficiently training AI on a diverse and balanced set of concepts.
The implications for AI development, suggesting the need for new strategies beyond scaling up data and models.
The potential for future advancements in AI training methods and data quality to improve performance.
The call for continued observation and experimentation to truly understand the capabilities and limits of AI.
Transcripts
so we looked at clip embeddings right
and we've talked a lot about using
generative AI to produce new sentences
to produce new images and so on and so
to understand images all these kind of
different things and the idea was that
if we look at enough pairs of images and
text we will learn to distill what it is
in an image into that kind of language
so the idea is you have an image you
have some texts and you can find a
representation where they're both the
same the argument has gone that it's
only a matter of time before we have so
many images that we train on and so and
such a big Network and all this kind of
business that we get this kind of
general intelligence or we get some kind
of extremely effective AI that works
across all domains right that's the
implication right the argument is and
you see a lot in the sort of tech sector
from the from some of these sort of um
big tech companies who to be fair want
to sell products right that if you just
keep adding more and more data or bigger
and bigger models or a combination of
both ultimately you will move Beyond
just recognizing cats and you'll be able
to do anything right that's the idea you
show enough cats and dogs and eventually
the elephant just is
implied as someone who works in science
we don't hypothesize about what happens
we experimentally justify it right so I
would say if you're going to if you're
going to say to me that the only upward
trajectory is is going you know the only
trajectory is up it's going to be
amazing I would say go on and prove it
and do it right and then we'll see we'll
sit here for a couple of years and we'll
see what happens but in the meantime
let's look at this paper right which
came out just recently this
paper is saying that that is not true
right this paper is saying that the
amount of data you will need to get that
kind of General zero shot performance
that is to say performance on new tasks
that you've never
seen is going to be astronomically vast
to the point where we cannot do it right
that's the idea so it basically is
arguing against the idea that we can
just add more data and more models and
we we'll solve it right now this is only
one p
and of course you know your mileage may
vary if you have a bigger GPU than these
people and so on but I think that this
is actual numbers right which is what I
like because I want to see tables of
data that show a trend actually
happening or not happening I think
that's much more interesting than
someone's blog post that says I think
this is going what's going to happen so
let's talk about what this paper does
and why it's interesting we have clip
embeddings right so we have an image we
have a big Vision Transformer and we
have a big text encoder which is another
Transformer bit like the sort of you
would see in a large language model
right which takes text strings my text
string today and we have some shared
embedded space and that embedded space
is just a numerical fingerprint for the
meaning in these two items and they're
trained remember across many many images
such that when you put the same image
and the text that describes that image
in you get something in the middle that
matches and the idea then is you can use
that for other tasks like you can use
that for classification you can use it
for image recall if you use a streaming
service like Spotify or Netflix right
they have this thing called a recom
recommended system a recommended system
is where you've watched this program
this program this program what should
you watch next right and you you might
have noticed that your mileage may vary
on how effective that is but actually I
think they're pretty impressive what
they have to do but you could use this
for a recommender system because you
could say basically what programs have I
got that embed into the same space of
all the things I just watched and and
recommend them that way right so there
are Downstream tasks like classification
and recommendations that we could use
based on a system like this what this
paper is showing is that you cannot
apply these effectively these Downstream
tasks for difficult problems without
massive amounts of data to back it up
right and so and the idea that you can
apply you know this kind of
classification on hard things so not
just cats and dogs but specific cats and
specific dogs or subspecies of tree
right or difficult problems where the
the answer is more difficult than just
the broad category that there isn't
enough data on those things to train
these models and way I've got one of
those apps that tells you what specific
species a tree is so is it not just
similar to that no because they're just
doing classification right or some other
problem they're not using this kind of
generative giant AI right the argument
has been why do that silly little
problem where you can do a general
problem and solve all your problems
right and the response is because it
didn't work right that's that's that's
that's why we're doing it um so there
are pros and cons for both right I'm not
going to say that no generative AI is
useful or no or these these models are
incredibly effective for what they do
but I'm perhaps suggesting that it may
not be reasonable to expect them to do
very difficult medical diagnosis because
you haven't got the data set to back
that up right so how does this paper do
this well what they do is they def they
Define these Core Concepts right so some
of the concepts are going to be simple
ones like a cat or a person some of them
are going to be slightly more difficult
like a specific species of cat or a
specific disease in an image or
something like this and they they come
up about
4,000 different concepts right and these
are simple text Concepts right these are
not complicated philosophical ideas
right I don't know how well it embeds
those and and what they do is they look
at the prevalence of these Concepts in
these data sets and then they sh they
they test how well the downstream task
of let's say one zero shot
classification or recall recommended
systems works on all of these different
concepts and they plot that against the
amount of data that they had for that
specific concept right so let's draw a
graph and that will help me make it more
clear right so let's imagine we have a
graph here like this and this is the
number of
examples in our training set of a
specific concept right so let's say a
cat a dog something more difficult and
this is the performance on the actual
task of let's say recommend a system or
recall of an object or the ability to
actually classify as a cat right
remember we talked about how you could
use this for zero shck classification by
just seeing if it embeds to the same
place as a picture of a cat the text a
picture of a cat that kind of process so
this is performance right the best case
scenario if you want to have an all
powerful AI that can solve all the
world's problems is that this line goes
very steeply upwards right this is the
exciting case it goes like like this
right that's the exciting case this is
the kind of AI explosion argument that
basically says we're on the Custer
something that's about to happen
whatever that may be where the scale is
going to be such that this can just do
anything right okay then there the
perhaps slightly more reasonable should
we say pragmatic interpretation which is
like just call it balanced right which
is but there a sort of linear movement
right so the idea is that we have to add
a lot of examples but we are going to
get a decent performance Boost from it
right so we just keep adding examples
we'll keep getting better and that's
going to be great and remember that if
we ended up up here we have something
that could take any image and tell you
exactly what's in it under any
circumstance right that's that's kind of
what we're aiming for and similarly for
large language models this would be
something that could write with
Incredible accuracy on lots of different
topics or for image generation it would
be something that could take your prompt
and generate a photorealistic image of
that with almost no coercion at all
that's kind of the goal this paper has
done a lot of experiments on a lot of
these Concepts across a lot of models
across a lot of Downstream tasks and
let's call this the evidence what you're
going to call it pessimistic now it is
pessimistic also right it's logarithmic
so it basically goes like this right
flattens out it flattens out now this is
just one paper right it doesn't
necessarily mean that it will always
flatten out but the argument is I think
that and it's not an argument they
necessarily make in in the paper but you
know the paper's very reasonable I'm
being a bit more Cavalier with my
wording the suggestion is that you can
keep adding more examples you can keep
making your models bigger but we are
soon about to hit a plateau where we
don't get any better and it's costing
you millions and millions of dollars to
train this at what point do you go well
that's probably about as good as we're
going to get with technology right and
then the argument goes we need something
else we need something in the
Transformer or some other way of
representing data or some other machine
learning strategy or some other strategy
that's better than this in the long term
if we want to have this line G up here
or this line gar up here that's that's
kind of the argument and so this is
essentially
evidence I would argue against the kind
of
explosion you know possibility of but
just you just add a bit more data and we
were on the cusp of something we might
come back here in a couple of years you
know if you're still allow me on
computer file after this absolute
embarrassment of of these claims that I
made um and we say okay actually the
performan has improve improved massively
right or we might say we've doubled the
number of data sets to 10 billion images
and we've got 1% more right on the on on
the classification to which is good but
is it worth it I don't know this is a
really interesting paper because it's
very very fough right if there's a lot
of evidence there's a lot of Curves and
they all look exactly the same it
doesn't doesn't matter what method you
use it doesn't matter what data set you
train on it doesn't matter what your
Downstream task is the vast majority of
them show this kind of problem and the
other problem is that we don't have a a
nice even distribution of classes and
Concepts within our data set so for
example cats you can imagine are over um
emphasized or over represented over
represented yeah over represented in the
data set by an order of magnitude right
whereas specific planes or specific
trees are incredibly under represented
because you just have tree right so I
mean trees are probably going to be less
represented than cats anyway but then
specific species of tree very very
underrepresented which is why when you
ask one of these models what kind of cat
is this or what kind of tree is this it
performs worse than when you ask it what
animal is this because it's a much
easier problem and you see the same
thing in image generation if you ask it
to draw a picture of something really
obvious like a castle where that comes
up a lot in the training set it can draw
you a Fant fantastic castle in the style
of Monet and it can do all this other
stuff but if you ask it to draw some
obscure artifact from a video game
that's barely even made it into the
training set suddenly it's starting to
draw something a little bit less quality
and the same with large language models
this paper isn't about large language
models but the same process you can see
actually already happening if you talk
to something like chap GPT when you ask
it about a really important topic from
physics or something like this it will
usually give you a pretty good
explanation of that thing because that
in the training set but the question is
what happens when you ask it about
something more difficult right when you
ask it to write that code which is
actually quite difficult to write and it
starts to make things up it starts to
hallucinate and it starts to be less
accurate and that is essentially the
performance degrading because it's under
represented in the training set the
argument I think is at least it's the
argument that I'm starting to come
around to thinking if you want
performance on hard tasks tasks that are
under represented on just general
internet text and searches we have to
find some other way of doing it than
just is collecting more and more data
right particularly because it's
incredibly inefficient to do this right
on the other hand we they you know these
companies will they've got a lot more
gpus than me right they're going to
train on on bigger and bigger corpuses
better quality data they're going to use
human feedback to better train their
language models and things so they may
find ways to improve this you know up
this way a little bit as we go forward
but it's going to be really interesting
see what happens because you know will
it Plateau out will we see trap GPT 7
or8 or 9 be roughly the same as chat
dpt4 or will we see another
state-of-the-art performance boost every
time I'm kind of trending this way but
you know it'll be excited to see if it
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