def openai_chat(system: str, user: str) -> str: resp = client.chat.completions.create( model=OPENAI_MODEL, messages=[ {“role”: “system”, “content”: system}, {“role”: “user”, “content”: user}, ], temperature=0.3 ) return resp.choices[0].message.content def heuristic_responder(context: str, question: str) -> str: lessons = re.findall(r”Lessons=(.*)”, context) avoid = re.findall(r”Avoid=(.*)”, context)…

In this tutorial, we explore how federated learning behaves when the traditional centralized aggregation server is removed and replaced with a fully decentralized, peer-to-peer gossip mechanism. We implement both centralized FedAvg and decentralized Gossip Federated Learning from scratch and introduce…

Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments…

To what extent can an artificial system be rational? A new MIT course, 6.S044 (AI and Rationality), doesn’t seek to answer this question. Instead, it challenges students to explore this and other philosophical problems through the lens of AI research.…

Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first…

In this tutorial, we walk through an end-to-end, advanced workflow for knowledge graph embeddings using PyKEEN, actively exploring how modern embedding models are trained, evaluated, optimized, and interpreted in practice. We start by understanding the structure of a real knowledge…