An Anthropic researcher just gave us a peek at self-improving AI
Anthropic researchers have developed an automated system capable of self-improving AI alignment benchmarks more efficiently and cheaply than human researchers. The study suggests that recursive self-improvement could soon become a practical reality in AI development.
Why it matters
The ability for AI to improve its own safety and alignment protocols could accelerate the development of advanced AI while potentially reducing the need for human oversight.
Training AI models with other AI models has become a very popular goal for neolabs — and now, a researcher in Anthropic’s fellows program has given us an early look at what it might look like in practice.
On Friday, Anthropic published a new paper titled “Automated Researchers Can Reliably Mitigate Alignment Failures,” detailing how AI systems could reliably improve a model’s performance on a set of alignment benchmarks. When given 10 benchmarks for specific misaligned behaviors, the automated systems were able to improve performance on every single one without degrading overall performance.
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