How OpenAI Strawberry Works ― "ONE TEXTBOOK TO RULE THEM ALL" ― My Educated Guess

Summary

The speaker discusses the capabilities of 'Strawberry', a rumored AI project from OpenAI, which may utilize synthetic data and latent space activation to solve complex problems. They share their expertise in synthetic data and their previous work on similar concepts, suggesting that 'Strawberry' could be generating comprehensive textbooks by recursively interrogating and activating AI models, grading the output for quality. The video speculates on how this might be applied to train future AI models like GPT 5 or 6, with a focus on knowledge synthesis and refinement.

Takeaways

• 🍓 Strawberry, a rumored AI project from OpenAI, is speculated to solve complex math problems and utilize synthetic data for training.
• 📈 The speaker has been working on synthetic data generation for over two years and is considered a world-leading expert in the field.
• 🧠 Latent Space Activation is likened to how human brains connect dots during conversation, applying this concept to large language models.
• 🔍 The concept of 'Surface Plate' involves a combination of three models: an expert, an interrogator, and a grader, to extract and refine knowledge.
• 📚 The idea of recursively generating a 'textbook of everything' by using AI models to extract and synthesize human knowledge is introduced.
• 🤖 The process involves an expert model providing base information, an interrogator model asking for deeper insights, and a grader model assessing the output.
• 📈 Synthetic data, when highly curated, is found to be more efficient for training AI models, which is a key aspect of the 'Strawberry' project.
• 🔑 The grading process is crucial for refining the synthesized data, ensuring that only the highest quality information is retained.
• 🔄 The iterative process of questioning and answering can lead to the creation of comprehensive textbooks on various topics by AI.
• 🤝 The potential of multiple fine-tuned models working in parallel to unpack every domain of human knowledge is highlighted.
• 🤔 The speaker expresses uncertainty about how the 'Strawberry' project handles complex math, suggesting it might involve generating LaTeX formulas and logical unpacking.

Q & A

What is 'Strawberry' as mentioned in the script?

Strawberry is a rumored feature or capability from OpenAI that is said to involve solving complex math problems and generating synthetic data for training models like Project Oion.

What does the speaker claim to be an expert in?

The speaker claims to be a world-leading expert in synthetic data, having worked on it for a long time and shared insights openly.

What is Latent Space Activation?

Latent Space Activation is a concept where a model's embedded knowledge is assembled and crystallized upon being asked a question it hasn't thought of before, similar to how human brains make new connections during conversation.

What is the speaker's previous work related to 'Strawberry'?

The speaker had been working on a startup 18 months ago that involved developing a concept called 'Surface Plate,' which combined three models: a generator, an interrogator, and a grader, similar to the rumored workings of 'Strawberry'.

How does the speaker describe the process of creating synthetic data?

The speaker describes the process as one where a model is iteratively drilled into topics to generate comprehensive information, which is then graded for quality and used to refine the data set.

What role does the 'interrogator' play in the process described?

The 'interrogator' is a model that extracts all the information from the 'expert' model, prompting it to crystallize its knowledge on a given topic.

What is the purpose of the 'grader' in the process?

The 'grader' evaluates the quality of the generated text based on a rubric, helping to ensure that only the highest quality information is retained in the data set.

How does the speaker relate the process to writing a textbook?

The speaker likens the process to recursively writing a textbook of everything humans know, with models working in parallel to unpack every domain of human knowledge.

What is the speaker's speculation about how 'Strawberry' might handle math?

The speaker is unsure about the specifics but suggests that 'Strawberry' might use a generator to write out LaTeX formulas and logically unpack them, similar to its approach with other types of knowledge.

What is the potential outcome of the process described by the speaker?

The potential outcome is the creation of a comprehensive, high-quality data set that encapsulates a vast amount of human knowledge, which could be used for fine-tuning models like GPT-5 or GPT-6.

What is the speaker's final thought on the process and its implications?

The speaker believes that if their understanding of the process is correct, it could represent a significant advancement in AI training and knowledge synthesis, potentially leading to the creation of a 'one data set to rule them all.'

Outlines

00:00

🤖 Synthetic Data and Latent Space Activation in AI

The speaker introduces the concept of 'Strawberry', a rumored AI project from OpenAI capable of solving complex math problems, and generating synthetic data for training AI models. They highlight their expertise in synthetic data, having worked on it over two years ago, and share insights on Latent Space Activation, a process where AI models, trained on vast datasets, can crystallize knowledge by connecting dots in real-time, akin to human thought processes. The speaker also discusses their past work on a startup that involved combining three AI models: a generator, an interrogator, and a grader, to create a comprehensive knowledge base through recursive questioning and grading of the generated content.

05:01

📚 Recursive Textbook Generation and Model Grading in AI

The speaker elaborates on their approach to generating synthetic data and activating latent spaces in AI models, likening it to recursively writing a textbook on all human knowledge. They describe a process involving an expert model generating base text, an interrogator model asking for detailed information, and a grader model evaluating the quality of the generated content. The aim is to iteratively refine and synthesize information, discarding lower-quality samples to maintain a high standard of knowledge. The speaker also speculates on how OpenAI might be training their next models, possibly using similar recursive and iterative techniques to expand the AI's understanding across all domains of human knowledge.

10:01

🔧 Fine-Tuning AI Models for Comprehensive Knowledge Synthesis

In the final paragraph, the speaker reflects on their experience fine-tuning AI models since the GPT-2 era and shares their approach to creating a 'one dataset to rule them all' using latent space activation. They suggest that by using multiple instances of advanced AI models working in parallel, one could iteratively unpack every domain of human knowledge. The speaker also expresses uncertainty about how math could be incorporated into this process but remains optimistic about the potential of their method. They conclude by offering to make another video if they figure out the solution to incorporating math into the AI training process.

Keywords

💡Strawberry

Strawberry refers to a rumored AI model from OpenAI that is said to be capable of solving complex math problems. It is a central topic in the video, as the speaker discusses how such a model might work and its potential implications. The term 'Strawberry' is used to represent the latest advancements in AI and machine learning, suggesting a significant leap from previous models.

💡Synthetic Data

Synthetic data is artificially generated data used to train machine learning models. In the video, the speaker mentions having worked on synthetic data generation over two years ago, using GPT3 to create data sets for fine-tuning. The concept is integral to the video's theme, as it highlights a method for enhancing AI models' capabilities without relying solely on real-world data.

💡Latent Space Activation

Latent space activation is a concept where AI models, trained on large datasets, are prompted to 'activate' or crystallize their embedded knowledge on a specific topic. The speaker uses this term to describe a process similar to how human brains make connections when faced with new questions, illustrating how AI can be coaxed into revealing deeper insights.

💡Recursive Search

Recursive search is an algorithmic method of exploring data by repeatedly breaking down a problem into smaller components. The video describes how a recursive search algorithm might be used in AI to delve deeper into topics, such as unpacking fundamental forces in physics, by iteratively asking for more detailed information.

💡Fine-tuning

Fine-tuning in the context of AI refers to the process of adapting a pre-trained model to a specific task by retraining it on a smaller, more focused dataset. The speaker mentions using GPT3 for fine-tuning to create synthetic datasets, emphasizing the importance of this technique in advancing AI capabilities.

💡Expert Model

An expert model, as discussed in the video, is a type of AI that has been trained to a high level of proficiency in a particular domain. The speaker's concept of 'surface plate' involves an expert model that serves as a knowledge base to be interrogated by other AI components.

💡Interrogator

In the video, the interrogator is a component of the AI system designed to question the expert model and extract detailed information. It plays a crucial role in the process of latent space activation, acting as a catalyst for the AI to reveal its knowledge on a given subject.

💡Discriminator

A discriminator in AI is typically part of a Generative Adversarial Network (GAN) and is used to evaluate the authenticity of generated data. In the context of the video, the discriminator serves as a grader for the AI-generated content, assessing the quality and providing feedback.

💡Surface Plate

Surface plate is a concept introduced by the speaker, describing a system combining three AI models: an expert, an interrogator, and a grader. This concept is central to the video's narrative, illustrating a method for deeply exploring and synthesizing knowledge within AI models.

💡Generative Model

A generative model in AI is capable of creating new data instances that resemble the original training data. The video discusses the use of a generative model to produce content, such as writing sections of a textbook, based on the information provided to it.

💡GPT

GPT stands for 'Generative Pre-trained Transformer' and refers to a series of AI models developed by OpenAI. The speaker mentions GPT2 and GPT4, indicating the progression of these models and their increasing capabilities, which is a key point in discussing the potential of AI like 'Strawberry'.

Highlights

The speaker discusses the capabilities of 'Strawberry', a rumored AI model from OpenAI, including solving complex math problems and generating synthetic data.

The speaker claims to have been working on synthetic data generation over two years ago, using GPT3 for fine-tuning.

The concept of 'Laten space activation' is introduced, drawing parallels to human brain function and knowledge assembly.

The speaker shares their expertise in synthetic data, having been a world leader in the field.

A high-level explanation of how 'Strawberry' might work, based on the speaker's previous startup experience.

The idea of 'Surface Plate', a concept combining three models: a generator, an interrogator, and a grader.

The process of recursively searching through data to create 'latent space activation'.

The efficiency of highly curated synthetic data in training AI models, as discovered in the speaker's research.

The iterative process of generating comprehensive knowledge through recursive questioning and answering.

The role of the 'interrogator' model in extracting deep knowledge from the 'expert' model.

The use of a 'grader' model to assess the quality of generated information, based on a rubric.

The importance of grading and refining generated samples to maintain high-quality synthetic data sets.

The potential for multiple fine-tuned models working in parallel to generate comprehensive textbooks.

The speaker's hypothesis on how OpenAI might be training GPT 5 or GPT 6 using these methods.

The challenge of understanding how 'Strawberry' could handle complex math problems.

The speaker's intention to possibly create another video if they figure out the math aspect of 'Strawberry'.

A summary of the speaker's approach to using latent space activation for fine-tuning AI models.

Transcripts

hello everybody I am awake and I don't

want to be because I couldn't shut my

brain off I think I know how strawberry

works I'm not even kidding so if you're

not familiar strawberry has been all the

rage qar strawberry the leaks from open

AI now the rumor has it right now and of

course just rumor we'll see if it gets

walked back but they're talking about

how strawberry can solve complex math

problems but then there's also talk

about synthetic data and that synthetic

data will be used to train project

oion now something stuck out to me when

they're talking about having solved uh

the generation of synthetic data so next

I want to point out I was talking about

synthetic data over two years ago so I

was using gpt3 to synthesize data to

create synthetic data sets for

fine-tuning the reason that I'm sharing

this it's not a flex I mean it is a

little bit of a flex but I'm pointing

out that like I have been working on

this for a while um and I've also been

sharing this you see this is the open AI

form I've been sharing it openly for a

while as

well so that's one thing is just keep in

mind synthetic data it's something that

I'm actually a world leading expert in

um and then I want to talk about Laten

space Activation so lat and space

activation um basically what we realized

is that

if uh all right here's here's another

way to think about it you train these

models on gigantic data sets and there's

a lot of embedded knowledge even if it's

not fully crystallized and so what I

mean by crystalized is it knows things

but it has to assemble them and this is

very similar to how human brains work is

that um have you ever had a conversation

where someone is asking you about

something that you know a lot about but

they ask you a question that you haven't

really thought of before and then at

that time of instantiation you kind of

connect all the dots and then you say

then you spit something out you're like

wow I knew more about that than I

thought that I did that's what Laten

space activation is that's the

equivalent of what latent space

activation is for uh large language

models and I was working on this 10

months ago um and I made videos about it

as well so that data is out there so

next what I want to show you is how I

think strawberry works at a very high

level just using a generic example on

Claude And the reason that I think that

I know how it works is because I was

working on this in my startup that I

ultimately didn't go anywhere 18 months

ago so I'm getting really excited now at

the startup that I was working on I was

developing a concept that I called

surface plate surface plate would have

been a combination of three models so

you'd have the generator or the the

expert then you'd have the interrogator

um which is basically asking the the

expert model what do you know about this

and then you'd have a greater which

would be a third model that would grade

the quality of output you can do all

this with chatbots now so in this first

one imagine that instead of me asking

this question this is a this is a

chatbot that is fine-tuned or just

instructed to interrogate the expert

model and extract all of the information

out of it to to create that latent space

activation saying I know you know about

this at a very deep level now tell me

everything you know about it to

crystallize it now you might remember

the uh the paper that Philip covered

over on AI explained textbooks are all

you need what we found is that synthetic

data when it is highly curated is

actually much more efficient at training

these models so that's all the theory

behind it so in this first example I

said tell me everything you know about

physics at a high level um we will

iteratively drill into topics y y y and

so then uh Claude happily generates the

top 10 qu uh categories of physics and

then I say excellent unpack fundamental

forces and particles cuz that was the

first one so by recursively searching

through the data remember what qar does

is it's a apparently a recursive search

algorithm now what I'm not quite sure on

is is how this would cover math so I'm

probably missing something but at least

I know how to generate synthetic data

and if you have enough tokens you can

recursively generate or or or synthesize

or you know do latent space activation

for all of human knowled in these models

and you can distill it and then also

make new connections so fundamental

forces and particles it started with

just uh three bullet points and then in

the next chat and

remember you like this does not take

much intelligence to interrogated again

so you could very easily fine-tune

another model to just uh be the

interrogator and it says certainly so

then it unpacks more about fundamental

forces um and so then uh let's just I'll

show you again it's like okay gravity is

the first one so I'm recursively going

through just basically a binary search

tree um tell me everything you know

about gravity and away we go so again it

does not take that much intelligence to

interrogate and activate and and create

those lat and space activations and then

what it's doing is it's synthesizing

information and so then what you do is

then you take it to the third model and

so in this case um said you're a grader

of data I will give you a bit of text

and you will grade it with a rubric what

we found a long time ago is that and

lots of people independently discovered

this by the way is that language models

are really good at discriminating so you

have a generator and a discriminator but

in this case we have an expert an

interrogator and a discriminator they're

really good at grading each other with

rubrics um particularly if you tell it

how to grade it and on what criteria now

I didn't give it uh a full rubric so a

full rubric would be grade one is the

this grade two is this grade three is

this um but I gave it the first sample

and I said you know there for uh this is

copied from the other uh conversation

and it gave it a five out of five um

which I would I would say it's close I

would say it's not particularly

comprehensive um and so the reason that

you do this is because as you're

generating samples you want to grade all

of them and you just basically discard

the bottom % of the samples so that

you're constantly ref ing your your your

your synthesized data set to be the

highest quality information so um next

sample whoops

sorry fat fingered

that all right there we

go okay so that's the next

sample and we're going to watch it grade

it um and again it graded at five out of

five I could see some room for

improvement

um but when you have basically you're

you're basically recursively writing a

textbook of everything that humans know

that is that is what I think strawberry

does is by by using these models that

have already been trained on all text

Data across the entire world they know

everything that humans know whether or

not they know it and whether or not it

has been trained in them well so by

recursively generating a textbook and

you have the expert that is saying okay

I'm writing the the base text and then

you have have the interrogator that's

saying okay tell me what you know about

that and then you have a third model

that is saying okay cool that was well

written or maybe not and you could do

this with I could imagine there might be

several more fine-tune models uh you

might have one that is reformatting it

so actually let's do this as an

experiment um so I will create another

chat and I say okay uh your purpose is

to write um uh

textbooks uh textbook sections based on

the data I give you um write

comprehensively um at the highest

intellectual level uh

oops uh domain expert um expound upon

the um Base information I give you with

all the uh knowledge you possess on the

topic so then there might a fourth model

which is just this is just the

drafter and so let's see what it

does um certainly I'd be happy to blah

blah blah blah blah and then uh actually

it's clawed so it's generating an

artifact so here we have it it's

actually generating the chapter for

us this is how I would if if I had the

money and the time and the uh and the

and the compute time if you asked me

said hey Dave use latent space

activation and create me a fine-tuning

data set that it's going to be like one

data set to rule them all um this is how

I would do it is I would uh use these

models to iteratively basically generate

one textbook to rule them all um is how

I would approach this and you see how

how far this is going and how fast

imagine that you have a whole bunch of

instances of Claud or gp4 or whatever

working in parallel to iter iteratively

unpack literally every domain of human

knowledge that is what I think open AI

is doing and that if I'm correct that is

how they are training GPT 5 or GPT 6

we're not sure um now and again like I

said the one part that's missing is I'm

not sure how they would have solved math

um unless they did the same thing where

they basically asked a generator saying

hey write out this latex formula and and

figure out how to how to logically you

know unpack this or something along

those lines I might make another video

if I figure that out

but yeah I hope you got a lot out of

this um I could be wrong but as someone

who's been fine-tuning models since

gpt2 this is how I would approach it so

cheers