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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

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.
    - Houshalter 12y ago
      So what metric are you using to measure progress in AI? There are plenty of benchmarks on very general tasks like image recognition or language modeling, etc. There is no room for bias or moving goal posts on a benchmark. All I'm saying is that AI is rapidly advancing. I'm not claiming its human level yet, and I don't really care what people in the 50s predicted.

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.
  - Houshalter 12y ago
    So what metric are you using to measure progress in AI? There are plenty of benchmarks on very general tasks like image recognition or language modeling, etc. There is no room for bias or moving goal posts on a benchmark. All I'm saying is that AI is rapidly advancing. I'm not claiming its human level yet, and I don't really care what people in the 50s predicted.