AI systems will learn bad behavior to meet performance goals, suggest researchers

Two researchers at Stanford University suggest in a new preprint research paper that repeatedly optimizing large language ...

AI Agents, LLMs & Economic Growth : Karpathy’s Surprising Predictions

Discover Andrej Karpathy's insights on AI agents, LLMs, and economic growth. Insights on memory, education, and economic ...

Which Machine Learning Models Are Most Used In Crypto Signal Generation?

Machine learning is transforming how crypto traders create and understand signals. From supervised models such as Random Forests and Gradient Boosting Machines to sophisticated deep learning hybrids ...
Frontiers

Brain-Inspired and neurally grounded algorithms in learning and control of advanced robots

In recent years, the field of robotics has undergone significant transformation, driven increasingly by advances in brain-inspired and neurally grounded ...
eLife

Critique of impure reason: Unveiling the reasoning behaviour of medical large language models

A survey of reasoning behaviour in medical large language models uncovers emerging trends, highlights open challenges, and introduces theoretical frameworks that enhance reasoning behaviour ...
Unite.AI

The End of Tabula Rasa: How Pre-Trained World Models are Redefining Reinforcement Learning

For a long time, the core idea in reinforcement learning (RL) was that AI agents should learn every new task from scratch, like a blank slate. This "tabula rasa" approach led to amazing achievements, ...

Inside Ring-1T: Ant engineers solve reinforcement learning bottlenecks at trillion scale

Ant Group, an affiliate of Alibaba, released Ring-1T which it says is the first trillion parameter open-source model.

Berkeley boffins build better load balancing algo with AI

The UC Berkeley crew has now shown the value of AI-based optimization work by having OpenEvolve work out a more efficient approach to load balancing across GPUs handling LLM inference.
Communications of the ACM

Shields for Safe Reinforcement Learning

Evaluating the advantages and potential drawbacks of shielding as a method for safe RL. Bettina Könighofer is an assistant ...

Self-improving language models are becoming reality with MIT's updated SEAL technique

Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing ...
NextBigFuture

AI Legend Sutton Wrote the Bitter Lesson- Gives His Suggestions for True Continual Learning

Sutton believes Reinforcement Learning is the Path to to Intelligence via Experience. Sutton defines intelligence as the computational part of the ability to achieve goals. It is rooted in a stream of ...
IEEE

Actuator–Fault–Tolerant Adaptive Tracking Control of Delayed Fuzzy Systems Using Reinforcement Learning

Abstract: In nonlinear systems, monitoring control behavior, fault occurrence, and latency factor continue to be major obstacles. Traditional control models frequently handle edge–case situations ...