Welcome to the belated 261st edition of the Data Science Briefing! This week we went back to the recording studio to record a brand new on-demand video on Visualization in Python. It should be out later this year. Stay tuned for more details!
We're also happy to announce that our final webinar for 2024 has just been confirmed. The next edition of the Generative Artificial Intelligence with the OpenAI API for Developers is scheduled for Dec 18th and you can already register!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we learn to Master AI Agents, explore Full Text Search on PDFs With Postgres, and dive intoAutoencoders and What are Diffusion Models?
On the academic front, we use sequences of life-events to predict human lives, learn What Matters in Transformers?, explroe Dynamic models of gentrification and how Autoregressive Large Language Models are Computationally Universal.
This week's book recommendation is "Life as No One Knows It: The Physics of Life's Emergence" by S. I. Walker. You can find all the previous book recommendations on our website. In this week's video, we have a Stanford lecture on Building Large Language Models (LLMs).
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Semper discentes,
The D4S Team
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.
- Mastering AI Agents: From Basics to Multi-Agent Systems [medium.com/@vinitgela]
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Full Text Search on PDFs With Postgres [tselai.com]
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Autoencoders: An Ultimate Guide for Data Scientists [towardsdatascience.com]
- New in NotebookLM: Customizing your Audio Overviews and introducing NotebookLM Business [blog.google]
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Torching the Modern-Day Library of Alexandria [theatlantic.com]
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Google, Microsoft, and Perplexity Are Promoting Scientific Racism in Search Results [wired.com]
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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)
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What Matters in Transformers? Not All Attention is Needed (S. He, G. Sun, Z. Shen, A. Li)
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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)
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Dynamic models of gentrification (G. Mauro, N. Pedreschi, R. Lambiotte, L. Pappalardo)
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Large Language Models in Finance: A Survey (Y. Li, S. Wang, H. Ding, H. Chen)
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Autoregressive Large Language Models are Computationally Universal (D. Schuurmans, H. Dai, F. Zanini)
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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)
All the videos of the week are now available in our Youtube playlist.
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