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I think it's actually the complete opposite. The current biomimetism direction being privileged by the modern A.I. trends is most likely the correct way to achi
by Chabsff 6y ago
I think it's actually the complete opposite. The current biomimetism direction being privileged by the modern A.I. trends is most likely the correct way to achieve "True" A.I.
However, Inference-based A.I. is actually needed to properly tackle many of the business problems that are being thrown at ML systems today. Explainability and bias reduction can probably only be be taken so far with the current approaches, and I suspect that this "so far" is not far enough for many tasks. It's definitely the case from a practical standpoint today.
So inference-based A.I. is due for a comeback indeed, but I'd say AGI is looking like one of the main areas where the current techniques might work.
- psoy 6y agoThe inference-based methods in AI qre more likely to he of the "probabilistic graphical models" variety (there are already marriages of Bayesian and Deep Learning techniques), and less likely to be of the "first order predicate calculus" variety. We humans don't have a particularly strong logical inference engine either - unless properly trained, people make the silliest logical mistakes and arrive at wrong conclusions all the time.
- Chabsff 6y agoRight, I meant "prolog-style formal inference engine". My point is that this "human imperfection" also present in probabilistic engines have artifacts that are fundamentally at odds with many business requirements where we are attempting to shoehorn then. It's almost more of a cultural issue than a technical one. The level of explainability currently desired from automated systems is possibly not achievable, simply because we want to uphold them to a different standard.