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Rather than spreading "meta" hype about AI being hyped. I highly recommend reading up on recent advances due to Deep Learning and ideas of Neural Processing. To

Rather than spreading "meta" hype about AI being hyped. I highly recommend reading up on recent advances due to Deep Learning and ideas of Neural Processing. To summarize briefly, a lot of core problems in applied machine learning, such as image recognition/segmentation/understanding, Natural Language Processing saw a significant improvement in performance over last five years, often bringing them to near human performance. This improvement was possible due to new approaches that were rooted in empiricism, availability of large datasets, powerful GPUs and algorithms such as Deep Convolutional & Recurrent Neural Networks. Combined with the advances in Drones, Batteries, Cloud computing & Smartphones a lot of application which previously would have been impossible to deploy can now be quickly deployed. Here is a good article that goes over the topic: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning http://spectrum.ieee.org/automaton/robotics/artificial-intel...

pron 10y ago
> ... often bringing them to near human performance That's quite far from the truth. You could say that they are now at the point where they are able to respond to direct, simple questions/commands. I am not saying that we haven't seen some great advances in machine learning and language algorithms, but they are still a far cry from anything I'd call intelligent. NLP is still rather primitive (although impressive compared to where we were two decades ago), and bees are able to navigate better than self-driving cars or drones, yet I'm not sure they would or should be called intelligent. I have no problem with using the term AI for current technology -- which is certainly cool and useful -- as long as we understand that the "I" is little more than a marketing gimmick. Otherwise people may think that these algorithms are the first step towards "true" intelligence -- i.e. that true intelligence is some version N.0 of those algorithms, that it is a complicated yet natural extension of them -- and at this point the knowledge we have to say whether this is true or not is pretty much zero.
- raverbashing 10y ago
  > That's quite far from the truth No, it is not. Because it's in stricter terms (like character or picture recognition, for example)
  - pron 10y ago
    aub3bhat was talking about natural language, not character recognition.
    - tlrobinson 10y ago
      aub3bhat also mentioned image recognition.
      - pron 10y ago
        Then I was talking about natural language :)
  - romaniv 10y ago
    Unfortunately, the ways AI researchers measure and compare their results to human performance are often rather weird and favor algorithms via.. let's say "unusual" experiment setup. For example, face recognition performance is often measured by comparing two random faces taken from a sample set. In real life, the "problem" is almost always to match a random face against the entire set of faces you remember. Performance in these two tasks is not at all equivalent. I've seen similarly weird problem formulations in many other AI research papers.

pron 10y ago

> ... often bringing them to near human performance That's quite far from the truth. You could say that they are now at the point where they are able to respond to direct, simple questions/commands. I am not saying that we haven't seen some great advances in machine learning and language algorithms, but they are still a far cry from anything I'd call intelligent. NLP is still rather primitive (although impressive compared to where we were two decades ago), and bees are able to navigate better than self-driving cars or drones, yet I'm not sure they would or should be called intelligent. I have no problem with using the term AI for current technology -- which is certainly cool and useful -- as long as we understand that the "I" is little more than a marketing gimmick. Otherwise people may think that these algorithms are the first step towards "true" intelligence -- i.e. that true intelligence is some version N.0 of those algorithms, that it is a complicated yet natural extension of them -- and at this point the knowledge we have to say whether this is true or not is pretty much zero.

raverbashing 10y ago
> That's quite far from the truth No, it is not. Because it's in stricter terms (like character or picture recognition, for example)
- pron 10y ago
  aub3bhat was talking about natural language, not character recognition.
  - tlrobinson 10y ago
    aub3bhat also mentioned image recognition.
    - pron 10y ago
      Then I was talking about natural language :)
- romaniv 10y ago
  Unfortunately, the ways AI researchers measure and compare their results to human performance are often rather weird and favor algorithms via.. let's say "unusual" experiment setup. For example, face recognition performance is often measured by comparing two random faces taken from a sample set. In real life, the "problem" is almost always to match a random face against the entire set of faces you remember. Performance in these two tasks is not at all equivalent. I've seen similarly weird problem formulations in many other AI research papers.