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

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GUEST BIO:
Arvind Srinivas is CEO of Perplexity, a company that aims to revolutionize how we humans find answers to questions on the Internet.

PODCAST INFO:

SOCIAL:

[LexClips]

Guest bio: Arvind Srinivas is CEO of Perplexity, a company that aims to revolutionize how we humans find answers to questions on the Internet.

[Hlbkomer]

A short summary by Claude AI:

I'll summarize the key points discussed in this video about the development of language models and attention mechanisms:

1. Evolution of attention mechanisms:
- Soft attention was introduced by Yoshua Bengio and Dimitri Bahdanau.
- Attention mechanisms proved more efficient than brute force RNN approaches.
- DeepMind developed pixel RNNs and WaveNet, showing that convolutional models could perform autoregressive modeling with masked convolutions.
- Google Brain combined attention and convolutional insights to create the Transformer architecture in 2017.

2. Key innovations in the Transformer:
- Parallel computation instead of sequential backpropagation.
- Self-attention operator for learning higher-order dependencies.
- More efficient use of compute resources.

3. Development of large language models:
- GPT-1: Focused on unsupervised learning and common sense acquisition.
- BERT: Google's bidirectional model trained on Wikipedia and books.
- GPT-2: Larger model (1 billion parameters) trained on diverse internet text.
- GPT-3: Scaled up to 175 billion parameters, trained on 300 billion tokens.

4. Importance of scaling:
- Increasing model size, dataset size, and token quantity.
- Focus on data quality and evaluation on reasoning benchmarks.

5. Post-training techniques:
- Reinforcement Learning from Human Feedback (RLHF) for controllability and behavior.
- Supervised fine-tuning for specific tasks and product development.

6. Future directions:
- Exploring more efficient training methods, like Microsoft's SLMs (small language models).
- Decoupling reasoning from factual knowledge.
- Potential for open-source models to facilitate experimentation.

7. Challenges and opportunities:
- Finding the right balance between pre-training and post-training.
- Developing models that can reason effectively with less reliance on memorization.
- Potential for bootstrapping reasoning capabilities in smaller models.

The discussion highlights the rapid progress in language model development and the ongoing challenges in creating more efficient and capable AI systems.

[vallab]

A Srinivas explained the progress of AI into generative models in the past in such a simple way that a common man (like me) could understand the essence of it. Thank you.

[harolddavies]

Lex, your podcasts are very inspiring to this old inorganic chem guy who spent his career in the Martial Arts, thank you!

[miraculixxs]

In a nutshell: Language models were introduced some ~15 years ago, i.e. models that can generate text. While they generated text, these were not very good or useful. Several smart people tried different approaches (RNN, WaveNet, etc. finally Attention/Transformers), and ultimately found a model that works really good, but on a small data base. Google, OpenAI, and some others, were in somewhat like a research competition of getting better and better models, using more and more data. Then OpenAI was bold enough to use all the data they could get their hands on. And that gave us ChatGPT.

[WALLACE]

He will interview everyone except the guy who invented transformers

[sygad]

I didn't understand a single thing in this, enjoyed it regardless

[thehubrisoftheunivris]

Now I have to read a whole bunch of ai and computer jargon so I understand any of this.

[nintishia]

Clear summary of how the LLMs came about, including only the absolute essentials. I like it. What I like more and agree with, though, is the trend that he describes at the end.

[willcowan]

Can we beg Aravind to write a book on ML and his thoughts on direction. He has such clarity and would be (is) a great teacher.

[mraarone]

But when will we get feed forward training?

[TooManyPartsToCount]

From 9.00 mins in Aravind outlines what is perhaps the most important 'next phase' for the current ML/LLM trajectory. Thanks for the clip Lex

[Dadspoke]

Kendrick….drop a diss track on this foo

[wyattross]

This video was the cherry on the cake to my day

[VideoToWords]

✨ Summary:

- Attention mechanisms, such as self-attention, led to breakthroughs like Transformers, significantly improving model performance.

- Key ideas include leveraging soft attention and convolutional models for autoregressive tasks.

- Combining attention with convolutional models allowed for efficient parallel computation, optimizing GPU usage.

- Transformers marked a pivotal moment, enhancing compute efficiency and learning higher-order dependencies without parameters in self-attention.

- Scaling transformers with large datasets, as seen in GPT models, improved language understanding and generation.

- Breakthroughs also came from unsupervised pre-training and leveraging extensive datasets like Common Crawl.

- Post-training phases, including reinforcement learning from human feedback (RLHF), are crucial for making models controllable and well-behaved.

- Future advancements might focus on retrieval-augmented generation (RAG) and developing smaller, reasoning-focused models.

- Open source models can facilitate experimentation and innovation in improving reasoning capabilities and efficiency in AI systems.

[Rmko]

3:42 I assume he meant to say more compute per param

[richardnunziata]

learning directional graphs over the embedding space may help in reasoning. Also content updating

[HybridHalfie]

It’s interesting how antiquated recurrent neural networks, supervised learning, support vector machines, convolutional neural networks, have become so antiquated in so little time since transformers came out. Machine learning is such an ever changing area. I would be curious to learn more about how transformers improve upon thee models regarding back propagation

[EdFormer]

Excellent overview of the history of deep autoregressive models, not AI in general.

[sweetride]

"How to train an LLM to be woke yet still appear to be reasonable" is what they want. Not likely going to happen.