Has Generative AI Already Peaked? - Computerphile

Summary

The 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

00:00

🧠 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.

05:00

📈 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.

10:01

🌳 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.

Keywords

💡Clip Embeddings

Clip embeddings refer to a method in AI where images and text are paired and processed to learn a shared representation. This technique is central to the video's theme, as it discusses the limitations of using generative AI for understanding and producing new content. The script mentions clip embeddings in the context of training AI to understand images by associating them with descriptive text.

💡Generative AI

Generative AI is a branch of artificial intelligence that focuses on creating new content, such as sentences, images, and more. The video discusses the potential and limitations of generative AI, particularly in relation to the idea that adding more data and bigger models will inevitably lead to more capable AI systems.

💡General Intelligence

General intelligence, in the context of AI, refers to the ability of a system to perform well across a wide range of tasks, not just a specific domain. The script challenges the notion that simply scaling up data and models will lead to AI with general intelligence, suggesting that this may not be feasible without an impractically large amount of data.

💡Zero-Shot Performance

Zero-shot performance is the ability of a machine learning model to perform a task without any training on that specific task. The video discusses the paper's argument that achieving general zero-shot performance on new tasks with current AI models would require an astronomical amount of data, which may not be feasible.

💡Vision Transformer

A Vision Transformer is a type of neural network architecture that is used for processing images. In the script, it is mentioned as part of the clip embeddings process, where it works alongside a text encoder to create a shared embedded space for images and text.

💡Text Encoder

A text encoder is a component of a machine learning system that converts text into a numerical representation that can be processed by other parts of the system. The script describes how a text encoder works in conjunction with a Vision Transformer to create meaningful embeddings for both images and text.

💡Recommended System

A recommended system, as mentioned in the script, is a type of algorithm used by services like Spotify or Netflix to suggest content to users based on their previous interactions. The video discusses how clip embeddings could theoretically be used to improve the effectiveness of such recommendation systems.

💡Downstream Tasks

Downstream tasks are the applications or specific problems that machine learning models are used to solve after pre-training. The script talks about how clip embeddings can be used for downstream tasks such as classification and recommendations, but also highlights the challenges in applying these tasks to difficult problems without sufficient data.

💡Concepts

In the context of the video, concepts refer to the categories or ideas that AI models are trained to recognize, such as 'cat' or 'tree species'. The script discusses how the prevalence of these concepts in datasets affects the performance of AI models on downstream tasks.

💡Data Distribution

Data distribution refers to the way data is spread across different categories within a dataset. The script points out that an uneven distribution, with some concepts like 'cats' being overrepresented and others like 'specific tree species' being underrepresented, can affect the performance of AI models on tasks involving less common concepts.

💡Hallucination

In the context of AI, 'hallucination' refers to the phenomenon where a model generates outputs that are incorrect or nonsensical, particularly when dealing with underrepresented data. The script uses this term to describe the limitations of current AI models when faced with tasks that are not well-represented in their training data.

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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