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We knew hand-crafted program in limited domains can work for NLP, computer vision and voice recognition long time ago. The challenge is always, that the limited

• joe_the_user 6y ago
  The thing that qualifies as "faffing" in my opinion isn't the statistical NLP programs, which "are what they are" but claims of progress based primarily on benchmarks, as YeGoblynQueene rightly states. And "limited domain" is relative. A program that gets many aspects of language right talking about a small world might be said to have a larger domain than a program that outputs stream of semi-plausible, semi-gibberish involving the whole of English language. Which again isn't saying modern NLP is nothing but rather we should a somewhat better to talk of it (and machine learning generally) than "hitting benchmarks".
• YeGoblynQueenne 6y ago
  >> We knew hand-crafted program in limited domains can work for NLP, computer vision and voice recognition long time ago. Yes, we did. So- where are the natural language interfaces by which we can communicate with artificial agents in such limited domains? Where are the applications, today, that exhibit behaviour as seemingly intelligent as SHRDLU in the '60s? I mean, have you personally seen and interacted with one? Can you show me an example of such a modern system? Edit: Note again that SHRDLU was created by a single PhD student with all the resources of ... a single PhD student. It's no stretch to imagine that an entity of the size of Google or Facebook could achieve something considerably more useful, still in a limited domain. But this has never even been attempted. Yes, it is faffing about. Basically NLP gave up on figuring out how language works and switched to a massive attempt to model large datasets evaluated by contrived benchmarks that serve no other purpose than to show how well modern techniques can model large datasets.
  • rvense 6y ago
    My impression is that the systems never progressed much after SHRDLU even though there were attempts at larger scale "expert systems". But adding more advanced rules and patterns proved extremely difficult and did not always have the expected effect of making the systems more general. There was the whole AI winter thing, of course, but that was as much a result of things not living up to the hype as a cause.
    • YeGoblynQueenne 6y ago
      This doesn't directly address your question, though it perhaps can give you some pointers if you want to read about the history of AI and the AI winter of the '80s, but in a way SHRDLU featured prominently in the AI winter, at least in Europe, particularly in the UK. So, in the UK at least the AI winter was precipitated by the Lighthill Report, a report published in 1973, compiled by a Sir James Lighthill and commissioned by the British Research Council, i.e. the people who held all the research money at the time in the UK. The report was furiously damning of AI research of the time, mostly because of grave misunderstandings e.g. with respect to combinatorial explosion and basically accused researchers of, well, faffing about and not doing anything useful with their grant money. The only exception to this was SHRDLU, that Lighthill praised as an example of how AI should be done. Anyway, if you have time, you can watch the televised debate between Lighthill and three luminaries of AI, John McCarthy (the man who named the field, created Lisp and did a few other notable things), Donald Michie (known for his MENACE reinforcement-learning program running on... matchboxes, and basically setting up AI research in the UK) and Richard Gregorie (a cognitive scientist from the US for whom I confess I don't know much). The (short) wikipedia article on the Lighthill Report has links to all the youtube videos: https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report It's interesting to see in the videos the demonstration of the Freddy robot from Edinburgh, that was capable of constructing objects by detecting their components with early machine vision techniques. In the 1960's. Incidentally: Even with today's knowledge, methodology, software tools, and so on, getting a robot to do this kind of thing would be a fairly complex and ambitious project. http://www.aiai.ed.ac.uk/project/freddy/ http://www.aiai.ed.ac.uk/project/freddy/ The above was written sometime in the '90s, I reckon but it is still true today. Unfortunately, Lighthill's report killed the budding robotics research sector in the UK and it has literally never recovered since. This is typical of the AI winter of the '80s. Promising avenues of research were abandoned not because of any scientific reasons, as is sometimes assumed ("expert systems didn't scale" etc) but, rather, because pencil pushers in charge of disbursing public money didn't get the science. Edit: A couple more pointers. John McCarthy's review of the Lighthill Report: http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthill.html http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi... An article on the AI winter of the '80s by the editor of IEEE Intelligent Systems: https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020002/13rRUyeCkdP https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020...
• ericbarrett 6y ago
  I read grandparent's post as saying that despite all the research and untold amounts of compute power poured into NLP over the decades, its practitioners have yet to address the original real-world goals that led us to study it in the first place. Missing the forest for the trees, if you will. (I don't assert that it has hasn't. I know just enough about the topic to see how little I know. But it seems, from the outside, a valid criticism, and not one unique to the field.)
• tmalsburg2 6y ago
  Since this post is receiving downvotes, I'd like to know which part of the HN guidelines it is violating.