Recent breakthroughs in AI: A brief overview | Aravind Srinivas and Lex Fridman

Summary

The transcript discusses the evolution of AI, focusing on the pivotal role of self-attention and the Transformer model in advancing natural language processing. It highlights how innovations like parallel computation and efficient hardware utilization have been crucial. The conversation also touches on the importance of unsupervised pre-training with large datasets and the refinement of models through post-training phases. The discussion suggests future breakthroughs may lie in decoupling reasoning from memorization and the potential of small, specialized models for efficient reasoning.

Takeaways

• 📈 The concept of self-attention was pivotal in the development of the Transformer model, leading to significant advancements in AI.
• 🧠 Attention mechanisms allowed for more efficient computation compared to RNNs, enabling models to learn higher-order dependencies.
• 💡 Masking in convolutional models was a key innovation that allowed for parallel training, vastly improving computational efficiency.
• 🚀 Transformers combined the strengths of attention mechanisms and parallel processing, becoming a cornerstone of modern AI architectures.
• 📚 Unsupervised pre-training with large datasets has been fundamental in training language models like GPT, leading to models with impressive natural language understanding.
• 🔍 The importance of data quality and quantity in training AI models cannot be overstated, with larger and higher-quality datasets leading to better model performance.
• 🔄 The iterative process of pre-training and post-training, including reinforcement learning and fine-tuning, is crucial for developing controllable and effective AI systems.
• 🔧 The post-training phase, including data formatting and tool usage, is essential for creating user-friendly AI products and services.
• 💡 The idea of training smaller models (SLMs) on specific reasoning-focused data sets is an emerging research direction that could revolutionize AI efficiency.
• 🌐 Open-source models provide a valuable foundation for experimentation and innovation in the post-training phase, potentially leading to more specialized and efficient AI systems.

Q & A

What was the significance of the paper 'Soft Attention' in the development of AI models?

The paper 'Soft Attention' was significant as it introduced the concept of attention mechanisms, which were first applied in the paper 'Align and Translate'. This concept of attention was pivotal in the development of models that could better handle dependencies in data, leading to improvements in machine translation systems.

How did the idea of using simple RNN models scale up and influence AI development?

The idea of scaling up simple RNN models was initially brute force, requiring significant computational resources. However, it demonstrated that by increasing model size and training data, performance could be improved, which was a precursor to the development of more efficient models like the Transformer.

What was the key innovation in the paper 'Pixel RNNs' that influenced subsequent AI models?

The key innovation in 'Pixel RNNs' was the realization that an entirely convolutional model could perform autoregressive modeling with masked convolutions. This allowed for parallel training instead of sequential backpropagation, significantly improving computational efficiency.

How did the Transformer model combine the best elements of previous models to create a breakthrough?

The Transformer model combined the power of attention mechanisms, which could handle higher-order dependencies, with the efficiency of fully convolutional models that allowed for parallel processing. This combination led to a significant leap in performance and efficiency in handling sequential data.

What was the importance of the insight that led to the development of the Transformer model?

The insight that led to the Transformer model was recognizing the value of parallel computation during training to efficiently utilize hardware. This was a significant departure from sequential processing in RNNs, allowing for faster training times and better scalability.

How did the concept of unsupervised learning contribute to the evolution of large language models (LLMs)?

Unsupervised learning allowed for the training of large language models on vast amounts of text data without the need for labeled examples. This approach enabled models to learn natural language and common sense, which was a significant step towards more human-like AI.

What was the impact of scaling up the size of language models on their capabilities?

Scaling up the size of language models, as seen with models like GPT-2 and GPT-3, allowed them to process more complex language tasks and generate more coherent and contextually relevant text. It also enabled them to handle longer dependencies in text.

How did the approach to data and tokenization evolve as language models became more sophisticated?

As language models became more sophisticated, the focus shifted to the quality and quantity of the data they were trained on. There was an increased emphasis on using larger datasets and ensuring the tokens used were of high quality, which contributed to the models' improved performance.

What is the role of reinforcement learning from human feedback (RLHF) in refining AI models?

Reinforcement learning from human feedback (RLHF) plays a crucial role in making AI models more controllable and well-behaved. It allows for fine-tuning the models to better align with human values and expectations, which is essential for creating usable and reliable AI products.

How does the concept of pre-training and post-training relate to the development of AI models?

Pre-training involves scaling up models on large amounts of compute to acquire general intelligence and common sense. Post-training, which includes RLHF and supervised fine-tuning, refines these models to perform specific tasks. Both stages are essential for creating AI models that are both generally intelligent and task-specifically effective.

What are the potential benefits of training smaller models on specific data sets that require reasoning?

Training smaller models on specific reasoning-focused data sets could lead to more efficient and potentially more effective models. It could reduce the computational resources required for training and allow for more rapid iteration and improvement, potentially leading to breakthroughs in AI reasoning capabilities.

Outlines

00:00

🤖 Evolution of Attention Mechanisms and Transformers

The speaker reflects on the surprising effectiveness of self-attention, which led to the development of the Transformer model and a surge in AI capabilities. They discuss the pivotal paper by Yoshua Bengio and others on soft attention, which was first applied in 'Align and Translate'. They also mention the RNN model by Iask that outperformed phrase-based machine translation systems without attention, but at a high computational cost. A graduate student's work on attention mechanisms significantly reduced the computational requirements. The speaker then discusses the importance of the masking technique in convolutional models, which allowed for parallel training and efficient use of GPU resources. They conclude by highlighting the Transformer's combination of attention and parallel processing as a significant breakthrough in AI, with minor improvements continuing since its introduction in 2017.

05:00

📚 Scaling Language Models and the Role of Data

The speaker delves into the history of large language models (LLMs), starting with the training of simple models on children's books, which showed promise. Google's BERT model improved upon this by training on Wikipedia and books, and OpenAI's GPT models further scaled up with more parameters and data. The speaker emphasizes the importance of data quality and quantity, as well as the right evaluations on reasoning benchmarks. They discuss the significance of reinforcement learning from human feedback (RLHF) in making systems controllable and well-behaved, and how post-training steps are crucial for creating usable products. The speaker also touches on the concept of pre-train/post-train and the importance of pre-training in providing a foundation of common sense for post-training to build upon.

10:00

🔍 Towards Efficient Reasoning with Small Language Models

The speaker ponders the efficiency of pre-training large models to acquire general common sense versus training smaller models on specific data sets that enhance reasoning abilities. They mention Microsoft's work on small language models (SLMs) trained on tokens important for reasoning, distilled from the knowledge of larger models like GPT-4. The speaker suggests that if a small model with good reasoning skills can be developed, it could disrupt the current model training paradigms by reducing the need for massive computational resources. They also propose the idea of using larger models to help filter data that is useful for reasoning, and advocate for open-source models as a base for experimenting with post-training phases to improve reasoning capabilities.

Keywords

💡Self-attention

Self-attention is a mechanism that allows models to weigh the importance of different parts of the input data when making predictions. It was a pivotal concept in the development of the Transformer model. In the script, self-attention is highlighted as a key innovation that led to the 'explosion of intelligence' in AI, enabling models to learn higher-order dependencies more effectively.

💡Transformer

The Transformer is a deep learning model introduced in the paper 'Attention Is All You Need'. It relies on self-attention mechanisms and has become foundational in natural language processing tasks. The script discusses how the Transformer combined the strengths of attention mechanisms and parallel computation to outperform previous models, marking a significant leap in AI capabilities.

💡Parallel computation

Parallel computation refers to the ability to perform multiple calculations simultaneously, which is crucial for efficient training of AI models. The script emphasizes the importance of parallel computation in the development of the Transformer, where it allows for training that is much faster than traditional sequential methods like backpropagation through time used in RNNs.

💡Unsupervised learning

Unsupervised learning is a type of machine learning where models learn from data without explicit guidance or labels. The script mentions unsupervised learning as a key area of focus, with models like GPT trained on vast amounts of text to learn natural language patterns and common sense, which is a significant departure from supervised learning approaches.

💡Pre-training

Pre-training involves training a model on a large dataset to learn general patterns before fine-tuning it for a specific task. The script discusses pre-training as a critical phase where models like GPT acquire a broad understanding of language, which is essential before they can be effectively fine-tuned for more specialized tasks.

💡Fine-tuning

Fine-tuning is the process of adjusting a pre-trained model for a specific task using a smaller, more focused dataset. The script highlights fine-tuning as part of the post-training phase, where models are made more controllable and better suited for practical applications, building on the general knowledge acquired during pre-training.

💡Scaling

Scaling in the context of AI refers to increasing the size of models and the amount of data they are trained on to improve performance. The script describes how scaling, particularly in the number of parameters and the volume of data, has been instrumental in the evolution of models like GPT, leading to substantial improvements in their capabilities.

💡Commonsense reasoning

Commonsense reasoning is the ability to make logical conclusions based on general knowledge. The script discusses the importance of developing AI models that can reason with common sense, which is a significant challenge. It suggests that larger models trained on more data are better at reasoning, although the script also ponders more efficient ways to achieve this.

💡Mixture of Experts

A Mixture of Experts is a machine learning approach where different experts (models) contribute to the final prediction. The script briefly mentions attempts to improve model efficiency by using a mixture of experts, suggesting that distributing the workload across specialized modules can be more effective than a single large model.

💡Data quality

Data quality is a measure of how fit data is for use. In the script, data quality is discussed as a critical factor in training effective AI models. It suggests that not only the quantity of data but also its relevance and accuracy are crucial for models to learn effectively, especially for tasks requiring reasoning and understanding.

💡Flywheel effect

The flywheel effect refers to the momentum that builds as a process or system continues to improve. In the script, the flywheel effect is used to describe how continuous improvement in AI models, through more data and better training techniques, leads to increasingly capable systems that can learn and adapt more rapidly.

Highlights

The introduction of self-attention led to the development of the Transformer model.

Attention mechanisms were first applied in the 'Align and Translate' paper.

Simple RNN models were scaled up to beat phrase-based machine translation systems.

Attention was identified as a key idea that could beat the performance of brute-force RNN models with less compute.

Pixel RNNs showed that convolutional models could do autoregressive modeling with masked convolutions.

The Transformer combined the strengths of attention and convolutional models for efficient parallel processing.

The core Transformer architecture has remained largely unchanged since 2017.

Masking allows for parallel computation during training, which is more efficient than sequential backpropagation.

Self-attention in Transformers does not have parameters but performs a lot of computations.

Unsupervised pre-training with large language models has been crucial for learning common sense.

GPT models demonstrated that training on a massive scale could lead to models with impressive capabilities.

The importance of data quality and quantity in training large language models.

The evolution of models from GPT-1 to GPT-3 showed the impact of scaling up parameters and data.

The role of reinforcement learning from human feedback (RLHF) in making AI systems controllable and well-behaved.

The significance of post-training processes in creating products that users can interact with.

The potential of retrieval-augmented generative models for more efficient AI training.

The idea of training smaller models on specific data sets for better reasoning skills.

The possibility of decoupling reasoning from memorization of facts for more efficient AI learning.

The importance of open-source models for experimentation and innovation in AI.

Transcripts

how surprising was it to you because you

were in the middle of it how effective

attention was how how self attention

self attention the thing that led to the

Transformer and everything else like

this explosion of intelligence that came

from this yeah idea maybe you can kind

of try to describe which ideas are

important here was it just as simple as

self

attention so uh I think I think first of

all I attention like like yosua Benjo

wrote this paper with Dimitri Bano

called Soft attention which was first

applied in this paper called align and

translate iask wrote the first paper

that said you can just train a simple

RNN model uh scale it up and it'll beat

all the phas based machine translation

systems uh but that was Brute Force

there's no attention in it and spent a

lot of Google compute like I think

probably like 400 million parameter

models something even back in those days

and then this grad student Bano uh in

beno's lab identifies

attention and beats his numbers with

Veil as compute mhm so clearly a great

idea and then people at Deep mine

figured that like this paper called

pixel

rnn's um figured that uh you don't even

need RNN even though the title is called

pixel RNN uh I guess it's the actual

architecture that became popular was

wave net and and they figured out that a

completely convolutional model can do

aut regressive modeling as long as you

do mask convolutions the masking was the

key idea so you can train in parallel

instead of back propagating through time

you can back propagate through every

input token in parallel so that way you

can utilize the GPU computer a lot more

efficiently because you're just doing

mat

Ms uh and so they just said threw away

the RNN that was

powerful um and so then Google brain

like wasani ATL that the Transformer

paper identified that okay let's let's

take the good elements of both let's

take attention it's more powerful than

cons it learns more higher order

dependencies because it applies more

multiplicative compute and uh let's take

the inside in wet that you can just have

a all convolutional model that fully

parallel Matrix multiplies and combine

the two together and they built a

Transformer and that is

the I would say it's almost like the

last answer that like nothing has

changed since 2017 except maybe a few

changes on what the nonlinearities are

and like how the square root descaling

should be done like some of that has

changed but and then people have tried

mixture of experts having more

parameters per in uh for the same flop

and things like that but the core

Transformer architecture has not changed

isn't it crazy to you that masking as as

simple as something like that works so

damn well yeah it's a very clever

Insight that look you want to learn

causal dependencies but you don't want

to waste your Hardware your compute and

keep doing the back propagation

sequentially you want to do as much

parallel computer as possible during

training that way whatever job was

earlier running in eight days would run

like in a single day I think that was

the most important insight and like

whether it's cons or attention I guess

attention and and Transformers make even

better use of Hardware than cons uh

because they apply more uh compute per

flop because in a Transformer the self

attention operator doesn't even have

parameters the qk transpose softmax

times V has no parameter but it's doing

a lot of flops and that's powerful it

learns Multi Auto dependencies I think

the inside then opening I took from that

is hey like Ilia s was been saying like

unsupervised learning is important right

like they wrote this paper called

sentiment neuron and then Alec Ratford

and him worked on this paper called gpt1

it's not it wasn't even called gpt1 was

just called GPT little did they know

that it would go on to be this big but

just said hey like let's revisit the

idea that you can just train a giant

language model and it will learn common

natural language common sense that was

not scalable earlier because you were

scaling up rnns but now you got this new

Transformer model that's 100x more

efficient at getting to the same

performance which means if you run the

same job you would get something that's

way better if you apply the same amount

of compute and so they just train

transformer like uh all the book

like story books children's story books

and that that got like really good and

then Google took that inside and did BT

except they did bidirectional but they

trained on Wikipedia and books and that

got a lot better and then open I

followed up and said okay great so it

looks like the secret sauce that we were

missing was data and throwing more

parameters so we'll get gpt2 which is

like a billion parameter model and like

trained on like a lot of links from

Reddit and then that became amazing like

you know produce all these stories about

a unicorn and things like that if you

remember yeah yeah um and then like the

gpt3 happened which is like you just

scale up even more data you take common

crawl and instead of 1 billion go all

the way to 175 billion but that was done

through analysis called the scaling loss

which is for a bigger model you need to

keep scaling the amount of tokens and

you train on 300 billion tokens now it

feels small these models are being

trained on like tens of trillions of

tokens and like trillions of parameters

but like this is literally the evolution

it's not like then the focus went more

into like part pieces outside the

architecture on like data what data

you're training on what are the tokens

how DD they are uh and then the shinilla

Insight that it's not just about making

the model bigger but you want to also

make the data set bigger you want to

make sure the tokens are also big enough

in quantity and high quality and do the

right evals on like lot of reasoning

benchmarks so I think that that ended up

being the Breakthrough right like this

it's not like attention alone was

important attention parall computation

Transformer uh scaling it up to do

unsupervised pre-training write data and

then constant improvements well let's

take it to the end because you just gave

an epic history of llms in the

breakthroughs of the

past 10 years plus uh so you mentioned

gpt3 so 35 how important to you uh is r

lhf that aspect of it it's really

important it's even though you you call

it as a cherry on the cake this this

cake has a lot of cherries by the way

it's not easy to make these systems

controllable and well behaved without

the rhf step by way there's this

terminology for this uh it's not very

used in papers but like people talk

about it as pre-train post-train mhm and

RF and supervised fine tuning are all in

posttraining phasee and the pre-training

phase is the raw scaling on compute and

without good post-training you're not

going to have a good

product but at the same time without

good pre-training there's not enough

common sense to like actually have you

know have the post training have any

effect like you can only teach

a generally intelligent person lot of

skills

and uh that's where the pre-training is

important that's why like you make the

model bigger the same RF on the bigger

model ends up like gbd4 ends up making

chat gbt much better than 3.5 but that

data like oh for this coding query make

sure the answer is formatted with these

uh markdown and like syntax highlighting

uh tool use it knows when to use what

tools you can decompose the query into

pieces these are all like stuff you do

in the post training phase and that's

what allows you to like build products

that users can interact with collect

more data create a flywheel go and look

at all the cases where it's failing uh

collect more human annotation on that I

think that's where like a lot more

breakthroughs will be made on the Post

train side yeah Post train plus plus so

like not just the training part of Post

train but like a bunch of other details

around that also yeah and and the rag

architecture the retrieval augmented

architecture uh I think there's an

interesting thought experiment here that

um we been spending a lot of computer in

the

pre-training uh to acquire General

common sense but that's seems brute

force and inefficient what you want is a

system that can learn like an open book

exam if you've written exams in like

like in undergrad or grad school where

people allowed you to like come with

your notes to the exam versus no notes

allowed I think not the same set of

people end up scoring number one on

both you're saying like pre-train is no

notes allowed kind of it it memorizes

everything like right you can you can

ask the question why do you need to

memorize every single fact to be good to

be good at reasoning but somehow that

seems like the more more comput and data

you throw at these models they get

better at reasoning but is there a way

to decouple reasoning from facts and

there are some interesting research

directions here like like Microsoft has

been working on this F models

uh where they're training small language

mods they call it slms but they're only

training it on tokens that are important

for reasoning and they're distilling the

intelligence from gp4 on it to see how

far you can get if you just take the

tokens of gp4 on data sets that require

you to reason and you train the model

only on that you don't need to train on

all of like regular internet Pages just

train it on like like basic Common Sense

stuff but it's hard to know what tokens

are needed for that it's hard to know if

there's an exhaustive set for that but

if we do manage to somehow get to a

right data set mix that gives good

reasoning skills for a small model then

that's like a breakthrough that disrupts

the whole uh Foundation model players

because you no longer

need uh that giant of cluster for

training and if this small model which

has good level of Common Sense can be

applied iteratively it bootstraps its

own

reasoning and doesn't necessarily come

up with one output answer but things for

a while bootstraps come things for a

while I think that can be like truly

transformational man there's a lot of

questions there is there is it possible

to form that slm you can use an llm to

help with the filtering which pieces of

data are likely to be useful for

reasoning

absolutely and these are the kind of

architectures we should Explore More uh

where um small model and this is also

why I believe open source is important

because at least it gives you like a

good base model to start with uh and and

try different experiments in the post

training phase uh to see if you can just

specifically shape these models for

being good reasoners