Comment by dbreunig
You should read the post. You might find the “domain” discussion interesting.
You should read the post. You might find the “domain” discussion interesting.
It’s not clearly defined. Nowadays by default it means generative AI (https://en.wikipedia.org/wiki/Generative_artificial_intellig...).
AI is defined by algorithmic decision making. ML, a subset, is about using pattern matching with statistical uncertainty in that decision making. GenAI uses algorithms of classical ML, including deep learning based on neural networks, to encode the decode input to output, jargonized as a prompt. Whether diffusion or next token prediction, the patterns are learned during ML training.
AI is not totally encapsulated by ML. For example, reinforcement learning is often considered distinct in some AI ontologies. Decision rules and similar methods from the 1970s and 1980s are also included though they highlight the algorithmic approach versus the ML side.
There are certainly many terms used and misused by current marketing (especially the bitcoin bro grifters who saw AI as an out of a bad set of assets), but there actually is clarity to the terms if one considers their origins.
It's a fun rabbit hole.
Classical ML tasks (e.g. classification, regression ), perception (vision, speech) and pattern recognition, generative AI capabilities (text, image, audio generation), knowledge representation and reasoning (symbolic AI, logic), decision-making and planning (including reinforcement learning for sequential decisions), as well as hybrid approaches (e.g. neuro-symbolic methods, fuzzy logic).
The capability areas outside of classical ML have been overlapped now to a degree by GPT architectures as well as deep learning, but these architectures aren't the whole game.
Yea, I think it's one of those things that I won't understand from the outside looking in. I'm in semiconductor software so I do a lot of classical numerical methods, graph theory, and ML research, like converting obscure ML algorithms heavy on math from academia for our ML teams. I don't think I'll get the technical side of what is now called ai without OJT in it.
That's what I was alluding to, I don't think it defines ai, do you? These pieces seem like classical ML pieces to me plus LLM. Is that ai? Like from a technical standpoint, is it clearly defined?