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2029 is a long way still and I would be surprised if the Turing Test wasn't passed. The past few years have had massive progress in AI. A few years ago machine

2029 is a long way still and I would be surprised if the Turing Test wasn't passed. The past few years have had massive progress in AI. A few years ago machine vision was an impossible problem believed to require strong AI, and now we are exceeding humans. Massive progress is being made in natural language understanding too. A number of papers have just come out on using deep NNs to do question answering or conversation.

lgas 12y ago
Do you happen to have any links to any of those papers? I would love to read them.
- Houshalter 12y ago
  I was thinking mostly of this paper: http://arxiv.org/abs/1503.02364 http://arxiv.org/abs/1503.02364 Here is some other relevant work of recent: http://arxiv.org/abs/1502.05698 http://arxiv.org/abs/1502.05698 http://arxiv.org/abs/1410.3916 http://arxiv.org/abs/1410.3916 https://www.reddit.com/r/thisisthewayitwillbe/comments/2zi8i6/is_google_working_on_lstmbased_conversational/ https://www.reddit.com/r/thisisthewayitwillbe/comments/2zi8i... https://www.reddit.com/r/thisisthewayitwillbe/comments/25a4r1/ibm_watsons_new_conversational_capabilities/ https://www.reddit.com/r/thisisthewayitwillbe/comments/25a4r... https://www.reddit.com/r/thisisthewayitwillbe/comments/307pth/abstract_of_naacl_hlt_2015_article_on_neural_nets/ https://www.reddit.com/r/thisisthewayitwillbe/comments/307pt... http://www.noahlab.com.hk/topics/ShortTextConversation http://www.noahlab.com.hk/topics/ShortTextConversation http://allenai.org/content/publications/hixon_naacl_2015.pdf http://allenai.org/content/publications/hixon_naacl_2015.pdf
  - lgas 12y ago
    Thanks!
ForHackernews 12y ago
> now we are exceeding humans. I mean, for some particular, limited version of "exceeding", yes: http://www.wired.com/2015/01/simple-pictures-state-art-ai-still-cant-recognize/ http://www.wired.com/2015/01/simple-pictures-state-art-ai-st...
- Houshalter 12y ago
  Those are images incredibly optimized to fool and exploit those neural networks. However on the actual image recognition tasks they were designed for they beat humans. Anyway this paper has a method for addressing this problem: http://arxiv.org/abs/1412.6572 http://arxiv.org/abs/1412.6572
  - ForHackernews 12y ago
    That's exactly my point. Computer vision is a "solved" problem within certain specific domains for which a particular algorithm has been trained/tuned ("the actual image recognition tasks they were designed for"). They are nowhere near as good as humans at the general-looking-at-any-random-things task yet. Similarly, I imagine we will get very good domain-specific "AI" in the next ten years: some sort of amped-up Siri that can, say, answer questions about hotels in a travel destination and book one for you. I do not think we will get a chatbot that can hold a conversation on any conceivable topic at a level that you would confuse for a human.
    - Houshalter 12y ago
      Imagenet is a pretty open task. The images are taken randomly from the internet with real world clutter and noise and other issues. There are 1,000 classes which is pretty large compared to previous datasets. When it started getting attention a few years ago algorithms sucked at it. No one would have believed in a few years we could beat humans on it. It seems like a case of moving goal posts, where every time a milestone is reached, it's disregarded.
      - ForHackernews 12y ago
        > It seems like a case of moving goal posts, where every time a milestone is reached, it's disregarded. I think it's sort of a case of AI researchers tried moving the goalposts closer, and now we're moving them back. Back in the 60s, a bunch of people thought a general-purpose artificial intelligence was a realistic goal in the near future. Then it turned out that problem was hard, really hard. AI researchers instead defined-down the problem. Instead of building AI, they worked on building "chess-playing AI" or "traffic-optimizing AI". These are useful and interesting in their own right, but they're not really what people mean when they talk about "intelligence". Over time, this meant that the entire field of "AI" became discredited, which is why nobody talks about AI anymore, they talk about "machine learning", or "deep learning", etc. If somebody says they're going to make a human-level general-purpose AI, they're no longer taken seriously.

lgas 12y ago

Do you happen to have any links to any of those papers? I would love to read them.

Houshalter 12y ago
I was thinking mostly of this paper: http://arxiv.org/abs/1503.02364 http://arxiv.org/abs/1503.02364 Here is some other relevant work of recent: http://arxiv.org/abs/1502.05698 http://arxiv.org/abs/1502.05698 http://arxiv.org/abs/1410.3916 http://arxiv.org/abs/1410.3916 https://www.reddit.com/r/thisisthewayitwillbe/comments/2zi8i6/is_google_working_on_lstmbased_conversational/ https://www.reddit.com/r/thisisthewayitwillbe/comments/2zi8i... https://www.reddit.com/r/thisisthewayitwillbe/comments/25a4r1/ibm_watsons_new_conversational_capabilities/ https://www.reddit.com/r/thisisthewayitwillbe/comments/25a4r... https://www.reddit.com/r/thisisthewayitwillbe/comments/307pth/abstract_of_naacl_hlt_2015_article_on_neural_nets/ https://www.reddit.com/r/thisisthewayitwillbe/comments/307pt... http://www.noahlab.com.hk/topics/ShortTextConversation http://www.noahlab.com.hk/topics/ShortTextConversation http://allenai.org/content/publications/hixon_naacl_2015.pdf http://allenai.org/content/publications/hixon_naacl_2015.pdf
- lgas 12y ago
  Thanks!

ForHackernews 12y ago

> now we are exceeding humans. I mean, for some particular, limited version of "exceeding", yes: http://www.wired.com/2015/01/simple-pictures-state-art-ai-still-cant-recognize/ http://www.wired.com/2015/01/simple-pictures-state-art-ai-st...

Houshalter 12y ago
Those are images incredibly optimized to fool and exploit those neural networks. However on the actual image recognition tasks they were designed for they beat humans. Anyway this paper has a method for addressing this problem: http://arxiv.org/abs/1412.6572 http://arxiv.org/abs/1412.6572
- ForHackernews 12y ago
  That's exactly my point. Computer vision is a "solved" problem within certain specific domains for which a particular algorithm has been trained/tuned ("the actual image recognition tasks they were designed for"). They are nowhere near as good as humans at the general-looking-at-any-random-things task yet. Similarly, I imagine we will get very good domain-specific "AI" in the next ten years: some sort of amped-up Siri that can, say, answer questions about hotels in a travel destination and book one for you. I do not think we will get a chatbot that can hold a conversation on any conceivable topic at a level that you would confuse for a human.
  - Houshalter 12y ago
    Imagenet is a pretty open task. The images are taken randomly from the internet with real world clutter and noise and other issues. There are 1,000 classes which is pretty large compared to previous datasets. When it started getting attention a few years ago algorithms sucked at it. No one would have believed in a few years we could beat humans on it. It seems like a case of moving goal posts, where every time a milestone is reached, it's disregarded.
    - ForHackernews 12y ago
      > It seems like a case of moving goal posts, where every time a milestone is reached, it's disregarded. I think it's sort of a case of AI researchers tried moving the goalposts closer, and now we're moving them back. Back in the 60s, a bunch of people thought a general-purpose artificial intelligence was a realistic goal in the near future. Then it turned out that problem was hard, really hard. AI researchers instead defined-down the problem. Instead of building AI, they worked on building "chess-playing AI" or "traffic-optimizing AI". These are useful and interesting in their own right, but they're not really what people mean when they talk about "intelligence". Over time, this meant that the entire field of "AI" became discredited, which is why nobody talks about AI anymore, they talk about "machine learning", or "deep learning", etc. If somebody says they're going to make a human-level general-purpose AI, they're no longer taken seriously.