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If we're talking about "AI" as a nebulous catch-all and arbitrary label for whatever hype people are chasing, then sure, you're free to define it however you li
by uoaei 3y ago
If we're talking about "AI" as a nebulous catch-all and arbitrary label for whatever hype people are chasing, then sure, you're free to define it however you like, by construction.
Popularity and status quo doesn't change the definition of the underlying theory. I will push back forever on some demotion of the importance of ML (i.e. theory) as distinct from some hype-driven notion of AI.
- CuriouslyC 3y agoIt's not really a hype-driven notion. The underlying machinery is statistical models, but those models are being trained into higher order structures which are worthy of research in their own right due to emergent properties, and eventually work in understanding and controlling those emergent properties will be more important than foundational ML advances.
- uoaei 3y agoI am sympathetic to this idea of emergence creating a new field of AI as distinct from the statistics that underlies it. Then the focus should be to identify something like a fundamental unit of intelligence in order to formalize a higher-order science out of the foundations. An analogy can be drawn between physics and chemistry: we needed to properly identify "the atom" and its component parts to get anywhere with the science of chemistry. But it took a whole lot of physics to get to that point. It seems similar with the ML-cum-AI transition where we'll still need to dig very deep into statistics and information theory before being able to abstract them away in favor of higher-order concepts. To me it seems we're really far from anything like that yet. Like at least a couple decades if not more. Friston's got some cool ideas that make me think he may have his name on some stuff later on but again the theory on learning systems is barely getting started.