Issue #261

October 25, 2024

This weeks book is "Life as No One Knows It: The Physics of Life's Emergence" by S. I. Walker. In this book, Walker offers a groundbreaking reimagining of life’s nature, challenging traditional definitions and exploring the origins of living systems from a fresh perspective. At the heart of the book lies Assembly Theory, which proposes that all matter can be viewed as information, with life representing a highly complex assembly of causal information. This paradigm-shifting framework not only redefines life universally but also paves the way for identifying non-terrestrial life forms.

In an interdisciplinary approach that blends insights from Physics and Philosophy, the book explores key distinctions between knowledge, information, and consciousness. It encourages readers to move beyond anthropocentric perspectives, prompting us to consider the existence of life forms vastly different from anything encountered on Earth.

1. Mastering AI Agents: From Basics to Multi-Agent Systems [medium.com/@vinitgela]
2. Full Text Search on PDFs With Postgres [tselai.com]
3. Autoencoders: An Ultimate Guide for Data Scientists [towardsdatascience.com]
4. New in NotebookLM: Customizing your Audio Overviews and introducing NotebookLM Business [blog.google]
5. Torching the Modern-Day Library of Alexandria [theatlantic.com]
6. Google, Microsoft, and Perplexity Are Promoting Scientific Racism in Search Results [wired.com]
7. What are Diffusion Models? [lilianweng.github.io]

• Using sequences of life-events to predict human lives (G. Savcisens, T. Eliassi-Rad, L. K. Hansen, L. H. Mortensen, L. Lilleholt, A. Rogers, I. Zettler, S. Lehmann)
• What Matters in Transformers? Not All Attention is Needed (S. He, G. Sun, Z. Shen, A. Li)
• LLMD: A Large Language Model for Interpreting Longitudinal Medical Records (R. Porter, A. Diehl, B. Pastel, J. H. Hinnefeld, L. Nerenberg, P. Maung, S. Kerbrat, G. Hanson, T. Astorino, S. J. Tarsa)
• Dynamic models of gentrification (G. Mauro, N. Pedreschi, R. Lambiotte, L. Pappalardo)
• Large Language Models in Finance: A Survey (Y. Li, S. Wang, H. Ding, H. Chen)
• Autoregressive Large Language Models are Computationally Universal (D. Schuurmans, H. Dai, F. Zanini)
• The Dynamics of Social Conventions in LLM populations: Spontaneous Emergence, Collective Biases and Tipping Points (A. F. Ashery, L. M. Aiello, A. Baronchelli)

Building Large Language Models (LLMs)

October 25, 2024

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