How OpenAI Strawberry Works ― "ONE TEXTBOOK TO RULE THEM ALL" ― My Educated Guess
Summary
TLDRThe 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
🤖 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.
📚 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.
🔧 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.
Mindmap
Keywords
💡Strawberry
💡Synthetic Data
💡Latent Space Activation
💡Recursive Search
💡Fine-tuning
💡Expert Model
💡Interrogator
💡Discriminator
💡Surface Plate
💡Generative Model
💡GPT
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
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