Pakistan now in 'open war' with Afghanistan, defence minister says, after countries trade attacks

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To this end, the company has voluntarily constrained itself: pledging not to release AIs above certain capability levels until it can develop sufficiently robust safety measures. Amodei hopes this approach—known as the Responsible Scaling Policy—will pressure competitors to make similar commitments, and eventually inspire binding government regulations. (Anthropic's main competitors OpenAI and Google DeepMind have since released similar policies.) "We're not trying to say we're the good guys and the others are the bad guys," Amodei says. "We're trying to pull the ecosystem in a direction where everyone can be the good guy."

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Consider an example. An AI rewrites a TLS library. The code passes every test. But the specification requires constant-time execution: no branch may depend on secret key material, no memory access pattern may leak information. The AI’s implementation contains a subtle conditional that varies with key bits, a timing side-channel invisible to testing, invisible to code review. A formal proof of constant-time behavior catches it instantly. Without the proof, that vulnerability ships to production. Proving such low-level properties requires verification at the right level of abstraction, which is why the platform must support specialized sublanguages for reasoning about timing, memory layout, and other hardware-level concerns.

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