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I had a question regarding the following passage: >"So there were two main theories that came out of this initial founding of the field. One theory was humans

I had a question regarding the following passage: >"So there were two main theories that came out of this initial founding of the field. One theory was humans are intelligent because we can learn. So if we can replicate the ability to learn in machines, then we can create machines that have human intelligence. And the other theory was humans are intelligent because we have a lot of knowledge. So if we can encode all of our knowledge into machines, then it will have human intelligence. And so these two different directions have kind of defined the entire trajectory of the field. Almost everything that we hear today is actually from this learning branch and it’s called machine learning or deep learning more recently." Is there still development of the other branch, the "encode all of our knowledge into machines, then it will have human intelligence" branch then? If so what is the branch of AI called then?

YeGoblynQueenne 7y ago
I think the "two theories" are meant to be machine learning and knowledge representation and reasoning (KRR). Knowledge representation and reasoning is one of the main fields of AI research, on the same broad level as machine learning or robotics and with its own journals and conferences (KR 2020 will be held in Rhodes, Greece in September). It enjoys much less recognition than machine learning in software development circles because it doesn't receive such broad coverage in the lay press as machine learning does, but it's an active area of reserach. Google's Knowledge Graph is probably the best known example of appplications of the techniques that have originated in research from that field. I don't really know why the author says that machine learning and KRR are "the two main theories" in the field. Perhaps she has access to historical information that I ignore. She says, a little earlier than the passage you quote that "[AI] was started 70 years ago", which must mean the workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was first introduced (by John McCarthy, perhaps more recognisable as the creator of Lisp to a programmer audience). There's sure been many binarily-opposed "camps" in AI, like the symbolicists vs the connectionists, or the "scruffies" vs the "neats" and so on. While I recognise the "machine learning vs knowledge representation" as one of the classic dichotomies, I don't really think it's such an ancient and fundamental dichotomy as the interviewee makes it sound. I wonder if the interviewee is mixing up the "ML vs KRR" distinction with a more subtle distinction between different forms of machine learning. I'm thinking of Alan Turing's original description of a "learning machine" from the classic 1950 Mind paper ("Computing machinery and intelligence", where he introduced the "imitation game"). Turing's learning machine would learn incrementally, from a small original knowledge base and from human instruction and from contact with the world, whereas today's machine learning tries to learn everything from scratch, in an end-to-end, no-human-in-the-loop, approach. This distinction, "incremental vs all-at-once learning" seems to fit the interviewee's description of the "two main theories" better. There's a paper, "The child machine vs the world brain", by the Australian AI scientist Claud Sammut, that goes into some detail in this distinction, based on Turing's paper and later developments in data mining and big data: https://www.semanticscholar.org/paper/The-Child-Machine-vs-the-World-Brain-Sammut/bde00d8180626e80dd73b3a7869c743b9cbd27a4 https://www.semanticscholar.org/paper/The-Child-Machine-vs-t... I recommend reading at least its introduction and then digging in to the references if you're interested in the history of AI in general and machine learning in particular and different ideas on those subjects that have been explored and abandoned over the years. Warning: contains ancient lore.
- bogomipz 7y ago
  Oh wow, thank you for the wonderfully thorough and detailed response. Quick follow up question - Has KRR not benefitted the same as ML has from advances in GPUs and cheap fast storage? Is that maybe why you hear less about it than ML? I wonder if the journalist was being reductionist simply because having only two competing branches makes for a slightly more digestible and compelling narrative for the average reader?
  - YeGoblynQueenne 7y ago
    >> Quick follow up question - Has KRR not benefitted the same as ML has from advances in GPUs and cheap fast storage? I don't really know- I'm from the machine learning side of things. I study symbolic machine learning for my PhD and I have a background in logic programming so I know what KRR is, but I'm no expert and I'm not up-to-date with the work in the field. I think the reason the lay press doesn't cover KRR and other AI fields is that they are, well, fields of academic research and as such not terribly interesting to the press and to most software developers. My intution is that most software developers don't really know much about AI in general, not to mention particular sub-fields of it. This includes machine learning. It's just deep learning that is an exception to this (quick reminder: AI ⊋ machine learning ⊋ deep learning). We could spend a long time discussing the reasons for that but, basically, I think you don't hear about KRR that much because it's not deep learning :)

YeGoblynQueenne 7y ago

I think the "two theories" are meant to be machine learning and knowledge representation and reasoning (KRR). Knowledge representation and reasoning is one of the main fields of AI research, on the same broad level as machine learning or robotics and with its own journals and conferences (KR 2020 will be held in Rhodes, Greece in September). It enjoys much less recognition than machine learning in software development circles because it doesn't receive such broad coverage in the lay press as machine learning does, but it's an active area of reserach. Google's Knowledge Graph is probably the best known example of appplications of the techniques that have originated in research from that field. I don't really know why the author says that machine learning and KRR are "the two main theories" in the field. Perhaps she has access to historical information that I ignore. She says, a little earlier than the passage you quote that "[AI] was started 70 years ago", which must mean the workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was first introduced (by John McCarthy, perhaps more recognisable as the creator of Lisp to a programmer audience). There's sure been many binarily-opposed "camps" in AI, like the symbolicists vs the connectionists, or the "scruffies" vs the "neats" and so on. While I recognise the "machine learning vs knowledge representation" as one of the classic dichotomies, I don't really think it's such an ancient and fundamental dichotomy as the interviewee makes it sound. I wonder if the interviewee is mixing up the "ML vs KRR" distinction with a more subtle distinction between different forms of machine learning. I'm thinking of Alan Turing's original description of a "learning machine" from the classic 1950 Mind paper ("Computing machinery and intelligence", where he introduced the "imitation game"). Turing's learning machine would learn incrementally, from a small original knowledge base and from human instruction and from contact with the world, whereas today's machine learning tries to learn everything from scratch, in an end-to-end, no-human-in-the-loop, approach. This distinction, "incremental vs all-at-once learning" seems to fit the interviewee's description of the "two main theories" better. There's a paper, "The child machine vs the world brain", by the Australian AI scientist Claud Sammut, that goes into some detail in this distinction, based on Turing's paper and later developments in data mining and big data: https://www.semanticscholar.org/paper/The-Child-Machine-vs-the-World-Brain-Sammut/bde00d8180626e80dd73b3a7869c743b9cbd27a4 https://www.semanticscholar.org/paper/The-Child-Machine-vs-t... I recommend reading at least its introduction and then digging in to the references if you're interested in the history of AI in general and machine learning in particular and different ideas on those subjects that have been explored and abandoned over the years. Warning: contains ancient lore.

bogomipz 7y ago
Oh wow, thank you for the wonderfully thorough and detailed response. Quick follow up question - Has KRR not benefitted the same as ML has from advances in GPUs and cheap fast storage? Is that maybe why you hear less about it than ML? I wonder if the journalist was being reductionist simply because having only two competing branches makes for a slightly more digestible and compelling narrative for the average reader?
- YeGoblynQueenne 7y ago
  >> Quick follow up question - Has KRR not benefitted the same as ML has from advances in GPUs and cheap fast storage? I don't really know- I'm from the machine learning side of things. I study symbolic machine learning for my PhD and I have a background in logic programming so I know what KRR is, but I'm no expert and I'm not up-to-date with the work in the field. I think the reason the lay press doesn't cover KRR and other AI fields is that they are, well, fields of academic research and as such not terribly interesting to the press and to most software developers. My intution is that most software developers don't really know much about AI in general, not to mention particular sub-fields of it. This includes machine learning. It's just deep learning that is an exception to this (quick reminder: AI ⊋ machine learning ⊋ deep learning). We could spend a long time discussing the reasons for that but, basically, I think you don't hear about KRR that much because it's not deep learning :)