Bonus: Future of LLMs? | By Transformer Co-inventor

Okay, now that we briefly know how LLMs work and what transformer architecture is, let's address the elephant in the room. Currently, LLMs are becoming more powerful as they're trained on increasingly vast amounts of data. But what happens when the data available on the internet reaches its limit, becoming saturated and overrun by noise generated by AI itself? How will models continue to evolve then?

To tackle this question, we have Łukasz Kaiser at the Pathway Bay Area Meetup. Łukasz, a supporter of Pathway and a pivotal figure in the development of transformative AI technologies. He is the co-creator of TensorFlow, Transformer Architecture, ChatGPT, GPT-4o, and more.

Insights from Łukasz Kaiser

In his presentation titled "Deep Learning Past and Future: What Comes After GPT?", Łukasz explores the future trajectory of LLMs in an era of potential data scarcity. He discusses how the landscape of deep learning has evolved and what strategic shifts are necessary to sustain further advancements.

The Core of the Discussion

Łukasz highlights a critical shift from the traditional paradigm of 'more data, better results' to a new model of efficiency. He proposes that the next generation of LLMs will need to achieve greater sophistication not by simply consuming more data, but by using smarter, high-quality data sets. This includes leveraging techniques that enhance a model's ability to retrieve and interpret relevant information quickly and accurately.

Why This is Important

For anyone engaged with the development or application of AI, understanding these evolving strategies is crucial. Łukasz's insights provide a roadmap for how AI can continue to develop in a sustainable and effective manner, even as traditional resources become constrained.

The latter part of the video involves a talk by Jan Chorowski (CTO at Pathway), a former colleague of Łukasz Kaiser from Google Brain, around what are the best ways in which get better retrieval for better reinforcement learning of foundational LLMs. This part is best covered after we've understood RAG later in this course. Till then, let's wait and consolidate what we've already learned. 😄