Ricordando errori del passato, sappiamo abbastanza per le decisioni che stiamo prendendo in tema di AI?.

Principles of the movement

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Ricordando errori del passato, sappiamo abbastanza per le decisioni che stiamo prendendo in tema di AI?.

(December 2024) - We provide evidence that classic lottery anomalies like probability weighting and loss aversion are not special phenomena of risk. They also arise (and often with equal strength) when subjects evaluate deterministic, positive monetary payments that have been disaggregated to resemble lotteries. Thus, we find, e.g., apparent probability weighting in settings without probabilities and loss aversion in settings without scope for loss. Across subjects, anomalies in these deterministic tasks strongly predict the same anomalies in lotteries. These findings suggest that much of the behavior motivating our most important behavioral theories of risk derive from complexity-driven mistakes rather than true risk preferences.

Deux anciens ministres de l’éducation nationale, Najat Vallaud-Belkacem et Jean-Michel Blanquer, livrent leur regard sur la méritocratie républicaine. Entre impact des réformes et poids des déterminis...

Deux anciens ministres de l’éducation nationale, Najat Vallaud-Belkacem et Jean-Michel Blanquer, livrent leur regard sur la méritocratie républicaine. Entre impact des réformes et poids des déterminis...

TLDR: AI-powered analytics tools use probabilistic systems (LLMs, semantic search, RAG) to answer...

I’ve been experimenting with a different approach to code generation using a small local LLM. The initial idea was simple: could a 4B model actually generate a usable Python application? It could. Well… sometimes. Instead of trying to make the model responsible for everything, I started moving decisions that can be formalized into a deterministic Python kernel. **LLM** * interprets the specification * makes semantic decisions * handles the parts that require reasoning **Deterministic kernel** * builds the known structure * renders predictable code * wires components together * validates invariants The result is **Esus** , an open-source experimental code-generation system. The interesting question for me is not: _“How do we make a 4B model code like a much larger model?”_ It’s: **“How much do we actually need the model to decide?”** I’ve written up the architecture, experiments and benchmark results here: Devops agentique, et si le LLM ne devait pas tout décider ? The project is open source: GitHub - stengerg-sfeir/esus: Agent Smith · GitHub I’d be interested in feedback from people working with small/local models: **Which parts of your agents do you think should remain probabilistic, and which parts should be deterministic?**

Deux anciens ministres de l’éducation nationale, Najat Vallaud-Belkacem et Jean-Michel Blanquer, livrent leur regard sur la méritocratie républicaine. Entre impact des réformes et poids des déterminis...

Stimulating, thought-provoking text by one of the 20th century's most creative philosophers makes accessible such topics as probability, measurement and quantitative language, causality and determinis...

A practical collection for embedded software engineers covering deterministic systems, software architecture, abstraction, and modern C++.

Quantum Dynamic Range Video: floating-point format & toolchain succeeding integer HDR/Dolby Vision, Float64 linear-light mastering, Float32 PQ/HLG delivery, Open Dynamic Metadata v2, determinis...

Deux anciens ministres de l’éducation nationale, Najat Vallaud-Belkacem et Jean-Michel Blanquer, livrent leur regard sur la méritocratie républicaine. Entre impact des réformes et poids des déterminis...

Harvard lanzó la Black Hole Initiative, el primer centro interdisciplinario que combina la astronomía, la física y la filosofía para descifrar los secretos de los agujeros negros. Con figuras como Stephen Hawking, el centro presenta murales cósmicos de Robbert Dijkgraaf y los desafíos del determinis