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AI is not CS. AI implementation is CS but AI theory is firmly in the field of statistics. The newcomers with their strong opinions seem to have become confused
by uoaei 3y ago
AI is not CS. AI implementation is CS but AI theory is firmly in the field of statistics.
The newcomers with their strong opinions seem to have become confused because programmers hit APIs and think they're doing "AI".
Software architecture and neural network architecture are not synonyms.
- ethbr1 3y agoI'm hopeful AI will end up being probability/statistic's "CS", in the same way that CS relates to its mathematics. IOW, an intellectually-linked discipline that drives so much revenue (and thus has so much work to be done in it) that it comprises its own field (about 75% of which is unique "applications-of-thing" problems).
- uoaei 3y agoSoftware engineering is already CS's "CS". CS is a field of study, not a role. There may be something like "AI engineering" but I can't see a way it would be much different from existing ML engineer roles. And if you do ML engineer minus specialized statistics knowledge you're just back to plain old software engineering. I don't know that this particular aspect of the "AI revolution" needs some disruptive paradigm shift.
- ethbr1 3y agoPo-tato potat-o, re: CS vs SE and role vs field. I'm hesitant to appropriate "engineer" into what we do. There are certainly people who work with code who earn that title. There are also many who don't. I do think it would be healthy for people who work with ML to separate more decisively from people who work with general purpose code. There are enough unique problems and solutions in ML that a clear community would better serve the field's maturation. As opposed to getting an endless summer of "Why don't you just" software developers fouling things up, because it's "similar".
- fragmede 3y agoAs a nascent field, "why don't you just" ends up with new, useful, applicable techniques, along with many that aren't. We don't know which is which until after the fact.
- hotstickyballs 3y agoIf statisticians can build large scale software they’d be really angry.
- CuriouslyC 3y agoAI isn't really statistics at this point either though. The primitives are statistics tools, but parameter dynamics and macro-scale behavior of these models are very much their own area of study.
- uoaei 3y agoNo, it's still very much at the intersection of optimization theory and statistics/probability.
- CuriouslyC 3y agoIn the last year or so I've seen a big shift upwards in the focus of papers. Academics don't have the resources to beat Meta at building foundation models, so the low hanging fruit is in understanding the behavior of existing models more deeply, and how to extend or leverage them in new ways. Within a few years the fraction of papers in AI about new architectures, training, hyperparamter optimization, etc will be dwarfed by papers about things like controlnets, LoRA combinators, multi-model dispatch networks and few-shot embedding methods.
- deleted 3y ago[deleted]
- uoaei 3y agoIf 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.