An Anthropic researcher just gave us a peek at self-improving AI
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.
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Key Facts
- Fact 1: When given 10 benchmarks for specific misaligned behaviors, the automated systems were able to improve performance on every single one without degrading overall performance.
- Fact 2: Each automated system searches the available literature, proposes a method, and trains the model using that method for 30 minutes, gradually increasing the benchmark over several iterations.
- Fact 3: “An AAR costs roughly $4 per hour in API inference against the $150 per hour we pay our human researchers.” In fairness, the paper also points out a few limitations to this approach.
- Fact 4: Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies.
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. Led by Anthropic fellow Chen Yueh-Han, the system replicates much of the traditional approach to research. Each automated system searches the available literature, proposes a method, and trains the model using that method for 30 minutes, gradually increasing the benchmark over several iterations.
(Original synthesis pending human/AI review — generated by the stub provider by selecting real sentences from the source material, not by writing new analysis or commentary.)
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Original source: TechCrunch