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?
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.
"AI is not totally encapsulated by ML" that's the part I haven't been able to put my fingers on. I understand that it's not encapsulated, ML is not intelligence, it's gradient descent. So what is in that set AI - {ML}?
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?
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.
"AI is not totally encapsulated by ML" that's the part I haven't been able to put my fingers on. I understand that it's not encapsulated, ML is not intelligence, it's gradient descent. So what is in that set AI - {ML}?
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