AWS recently announced the new Graviton5 processor and the preview of the first EC2 instances running on it, the general-purpose M9g instances. According to the cloud provider, the latest chip delivers up to 25% higher performance than Graviton4, introduces the Nitro Isolation Engine, and provides a larger L3 cache, improving latency, memory bandwidth, and network throughput. According to the press release, the new Arm-powered EC2 M9g instances provide up to 192 CPU cores per instance. The higher core density reduces inter-core latency by up to 33% and increases bandwidth, improving scaling for workloads such as databases, analytics, application servers, gaming, and Electronic Design Automation (EDA).
A long-standing goal of artificial intelligence is to build systems capable of complex reasoning in vast domains, a task epitomized by mathematics with its boundless concepts and demand for rigorous proof. Recent AI systems, often reliant on human data, typically lack the formal verification necessary to guarantee correctness. By contrast, formal languages such as Lean1 offer an interactive environment that grounds reasoning, and reinforcement learning (RL) provides a mechanism for learning in such environments.