Employees, not the employers, are now running the show. The age of end-user first SaaS is here, it’s either adapt or die.
UC Berkeley's Sergey Levine “self-supervised offline reinforcement learning” as a promising direction of research for AI.
Hugging Face launched Endpoints on Azure in collaboration with Microsoft. But creating a business around transformers presents challenges that favor large tech companies and put companies like Hugging Face at a disadvantage.
Switching transformer models from deep to wide architecture results in significant improvements in speed, memory, and interpretability.
ChatGPT represents a major advance in self-learning AI. But to make the leap to AGI, researchers must shift their focus to biologically plausible systems.
A new paper by multiple academic institutions explores the limitations of RLHF and their possible solutions.
A new paper by Microsoft shows that with a small but diverse training dataset, you can fine-tune LLMs for coding tasks with impressive accuracy
LLMs with infinite context windows are making it easier to create proof-of-concepts and prototypes. But scale still requires careful engineering.
With so much developments, hype, and confusion around large language models, how should you approach LLM application development? This framework can help.
The integration of AI in real estate technology offers opportunities for strategic transformation, enhancing value, efficiency, & decision-making capabilities.