Unite.AI

A Fracture in AI Innovation: Y Combinator Cuts Out Canada

Y Combinator, the legendary Silicon Valley accelerator that helped launch Stripe, Airbnb, Dropbox, and hundreds of others, has taken a quiet but seismic step: it is no longer accepting companies ...

Reinforcement learning and organizational management

Artificial reinforcement learning is just one lens to evaluate organizations. However, this thought experiment taught me that ...
IEEE

Federated Reinforcement Learning for Distributed Dependent Task Offloading with Batch Processing in CPU-GPU Heterogeneous MEC

Abstract: By offloading tasks to proximal edge servers, mobile edge computing (MEC) has emerged as a promising paradigm to enable computation-intensive and latency-sensitive applications on mobile ...
marktechpost

MemAgent: A Reinforcement Learning Framework Redefining Long-Context Processing in LLMs

Handling extremely long documents remains a persistent challenge for large language models (LLMs). Even with techniques such as length extrapolation and sparse attention, models often suffer from ...
Forbes

The Autonomous Advantage: Reinforcement Learning’s Role In The Next Era Of AI

The age of truly autonomous artificial intelligence, where systems proactively learn, adapt and optimize amid real-world complexities instead of simply reacting, has been a long-held aspiration. Now, ...
Geeky Gadgets

Why Reinforcement Learning Could Be AI’s Biggest Flaw Yet

What if the very techniques we rely on to make AI smarter are actually holding it back? A new study has sent shockwaves through the AI community by challenging the long-held belief that reinforcement ...
Scientific Research Publishing

Ma, Y., Jayaraman, D. and Bastani, O. (2021) Conservative Offline Distributional Reinforcement Learning. Advances in Neural Information Processing Systems, 34, 19235-19247.

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
Scientific Research Publishing

Lyu, J., Ma, X., Li, X. and Lu, Z. (2022) Mildly Conservative Q-Learning for Offline Reinforcement Learning. Advances in Neural Information Processing Systems, 35, 1711-1724.

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
Forbes

How Auto-Classifying Feedback Can Improve Reinforcement Learning

Having spent the last two years building generative AI (GenAI) products for finance, I've noticed that AI teams often struggle to filter useful feedback from users to improve AI responses.