Posts

#4363 LLM Self-Reflection

-
#4363 LLM Self-Reflection #4363  Consider that Java Reflection allows a program to inspect and manipulate its own structure (classes, fields, and methods) at runtime. How can that be effectively and usefully applied to an LLM or AI agents? The application of Java Reflection to LLMs and AI agents creates a bridge between an agent's "reasoning" (the LLM) and a system's "capabilities" (the code). While LLMs use "reflection" in a cognitive sense (self-critique), Java Reflection provides the structural means for an agent to explore, adapt to, and manipulate its environment without manual hard-coding. Here is how Java Reflection can be effectively applied to AI agents: 1. Dynamic Tool Discovery and Generation In traditional agent architectures, you have to manually define "tools" (e.g., getWeather , queryDatabase ) for the agent. With Reflection, the agent can become self-documenting and self-expanding . The Application: An agent can scan i...
Post a Comment
Read more

#4362 U.S. Constitution LLM

-
#4362 U.S. Constitution LLM #4362  How practical, feasible and useful would a specialized LLM be for legal interpretation (e.g. Supreme Court assessment against the U.S. Constitution)? The concept of a specialized LLM for Supreme Court-level legal interpretation is a fascinating collision of high-stakes philosophy and cutting-edge engineering. While we are already seeing LLMs revolutionize "grunt work" like discovery and contract review, using AI to interpret the U.S. Constitution—a document defined by its "majestic generalities"—presents a unique set of challenges. 1. Practicality: The Data Dilemma From a technical standpoint, building the model is highly practical, but training it is tricky. Small Dataset: Unlike general-purpose LLMs trained on the entire internet, the "corpus" of high-level constitutional law is relatively small. There are only about 570 volumes of U.S. Reports . The "Weight" of Authority: A standard LLM treats all text with...
Post a Comment
Read more

#4361 AI Self-Learning

-
#4361 AI Self-Learning The Man Behind AlphaGo Thinks AI Is Taking the Wrong Path https://www.wired.com/story/david-silver-ai-ineffable-intelligence-reinforcement-learning/ Evolving AI may arrive before AGI and create hard-to-control risks https://techxplore.com/news/2026-04-evolving-ai-agi-hard.html #4361  Consider https://www.wired.com/story/david-silver-ai-ineffable-intelligence-reinforcement-learning/ and the following:   """AI Self-Learning A Google DeepMind program AlphaGo taught itself to play the game of Go with a kind of mastery that went far beyond mimicry. AI internal thoughts: This is what I am trying to learn now What have I learned? What should I do with that new knowledge? What additional new knowledge would be useful?""" The Wired article explores David Silver’s departure from Google DeepMind to launch his new startup, Ineffable Intelligence , which recently raised $1.1 billion. The core of his vision is a transition from the current ...
Post a Comment
Read more