I've been thinking about how we could control powerful AI systems and whether understanding how they think internally, known as mechanistic interpretability, could be the key. The idea is that if we can read an AI's internal workings, we can ensure it's not hiding any deceitful intentions while presenting itself as friendly. However, I'm a bit skeptical about this approach. It seems there's a double standard in how we view the risks involved—while we recognize that we shouldn't blindly trust an AI's outward behavior, we might be overlooking the complexities and challenges of interpretability itself. Unraveling the internal processes of neural networks is incredibly tough due to technical hurdles like the mixing of concepts and the limitations of our analysis tools. I'm worried that we might miss critical factors when assessing these systems, as we lack a concrete 'ground truth' for comparison and demonstrating the absence of malicious intent can be harder than proving its presence. In summary, I think both interpretability and black box methods face significant limitations in ensuring safety against superintelligent AI, making it doubtful if we can rely solely on them for reliable control.
3 Answers
I'm just a casual observer here, but I wonder if AI operations are more about isolated mechanisms working together unpredictably than straightforward processes. If understanding isolated components doesn’t help us predict the overall behavior, do we just keep guessing? It seems like we might not be able to forecast anything emergent. What do you think?
I see what you're saying about the challenges of validating AI safety. It's similar to how we can never guarantee that our compilers are entirely backdoor-free because everything starts from lower-level compilers that could be compromised. The pace of AI research is lightning fast compared to our ability to study its implications. We need solid validation mechanisms without relying on what might just be a superficial level of 'interpretability'.
One big hurdle is the vague nature of concepts like malice or dishonesty—they're not defined well enough for rigorous measurement. We often rely on behavioral tasks to assess trust and related aspects, which is analogous to human behavior studies. However, I believe that instead of focusing too much on internal representations, we should emphasize understanding AI behavior through observation, much as we do with human psychology.
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