A marriage of formal methods and LLMs seeks to harness the strengths of both.

As a movement, it has largely refused to engage seriously with AI, ceding debate about a threat and opportunity to the right ...

Despite years of progress, trust remains one of the biggest barriers to AI adoption in healthcare. Patient privacy is their ...

AI training is dominated by hyperscale data centers, but inference and everyday workloads are opening real space for decentralized GPU networks. Decentralized GPU networks are pitching themselves as a ...

Leaders adopting AI must prioritize security, governance, clean data, partner integration, and scalable use cases.

Microsoft has announced the launch of its latest chip, the Maia 200, which the company describes as a silicon workhorse designed for scaling AI inference. The 200, which follows the company’s Maia 100 ...

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...

AI-driven identity proofing and continuous verification are becoming essential for agencies looking to counter deepfakes, ...

AI is shifting from training to inference, which could drive demand for AMD. AMD designed its new chips with double the memory bandwidth, enabling faster AI inference workloads. Early partnerships ...

AI is capable of highly accurate conversation and information search, as well as solving highly difficult mathematical problems. However, there are significant differences between human and AI ...