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> If it's passing the turing test, we're pretty close, 3 years max Well yes? If a Turing test you realize the simulation of some idiot in the online chat, it h

by gambler 10y ago

> If it's passing the turing test, we're pretty close, 3 years max Well yes? If a Turing test you realize the simulation of some idiot in the online chat, it has long been there - and nobody wants. But the system, which can lead a meaningful conversation, today there is no trace. And there is even no harbingers of its occurrence. <- This was translated by Google Translate from a piece of perfectly intelligible and grammatically correct text in another language. If this is the state of the art in machine translation, how on Earth can you expect a machine that can converse on human level in three years?

XenophileJKO 10y ago
Sadly, I read the google translated text and it read like a person where english was their second language. I didn't realize it was an "example" until I read your next section. So it had me fooled.
- gambler 10y ago
  You could probably replace 90% of YouTube comments with a simple trigram-based chat bot and no one would notice. But that's hardly a good measure of AI quality. Although, your comment illustrates the main problem with the Turing Test. It depends on too many factors and assumptions that have nothing to do with the AI itself. A good AGI test should be constructed in such a way that any normal person passes it with 100% certainty and no trivial system can pass it at all.
dave_sullivan 10y ago
> If this is the state of the art in machine translation, how on Earth can you expect a machine that can converse on human level in three years? Here are 2 good papers--one from last year, one very recent--that might change your mind. https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1605.06065v1.pdf https://arxiv.org/pdf/1605.06065v1.pdf Over the past 10 years (deep learning got going in ~2006, really), the state of the art has improved at an exponential rate, and that's not slowing down. There are plenty of reasons to be bullish. Or at least, now seems like a bad time to leave the field.
- gambler 10y ago
  I've read those papers when they came out. Correct me if I'm wrong, but they were not peer-reviewed. The first one looks very impressive from the examples they've provided, but extraordinary claims require extraordinary proof. I will believe it only when I see an interactive demo. It's been nearly a year and I haven't seen it surfacing in a real product or usable prototype of any sort. Why? Somehow all the papers that have "deep neural" stuff get 1/100th of the scrutiny applied to other AI research. I don't see anyone hyping up MIT's GENESIS system, for example. The second paper has a really weird experiment setup. The point of one-shot learning is to be able to extract information from a limited number of examples. The authors, however, pretrain the network on a very large number of examples highly similar to the test set. Whether or not their algorithm is impressive depends on how well it is able to generalize, and they're not really testing generality -- at all. Again, why?
  - dave_sullivan 10y ago
    Ok, here's some code for anyone that wants to try it themselves: https://github.com/tristandeleu/ntm-one-shot https://github.com/tristandeleu/ntm-one-shot https://github.com/macournoyer/neuralconvo https://github.com/macournoyer/neuralconvo

by gambler 10y ago

XenophileJKO 10y ago

Sadly, I read the google translated text and it read like a person where english was their second language. I didn't realize it was an "example" until I read your next section. So it had me fooled.

gambler 10y ago
You could probably replace 90% of YouTube comments with a simple trigram-based chat bot and no one would notice. But that's hardly a good measure of AI quality. Although, your comment illustrates the main problem with the Turing Test. It depends on too many factors and assumptions that have nothing to do with the AI itself. A good AGI test should be constructed in such a way that any normal person passes it with 100% certainty and no trivial system can pass it at all.

dave_sullivan 10y ago

> If this is the state of the art in machine translation, how on Earth can you expect a machine that can converse on human level in three years? Here are 2 good papers--one from last year, one very recent--that might change your mind. https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1506.05869.pdf https://arxiv.org/pdf/1605.06065v1.pdf https://arxiv.org/pdf/1605.06065v1.pdf Over the past 10 years (deep learning got going in ~2006, really), the state of the art has improved at an exponential rate, and that's not slowing down. There are plenty of reasons to be bullish. Or at least, now seems like a bad time to leave the field.

gambler 10y ago
I've read those papers when they came out. Correct me if I'm wrong, but they were not peer-reviewed. The first one looks very impressive from the examples they've provided, but extraordinary claims require extraordinary proof. I will believe it only when I see an interactive demo. It's been nearly a year and I haven't seen it surfacing in a real product or usable prototype of any sort. Why? Somehow all the papers that have "deep neural" stuff get 1/100th of the scrutiny applied to other AI research. I don't see anyone hyping up MIT's GENESIS system, for example. The second paper has a really weird experiment setup. The point of one-shot learning is to be able to extract information from a limited number of examples. The authors, however, pretrain the network on a very large number of examples highly similar to the test set. Whether or not their algorithm is impressive depends on how well it is able to generalize, and they're not really testing generality -- at all. Again, why?
- dave_sullivan 10y ago
  Ok, here's some code for anyone that wants to try it themselves: https://github.com/tristandeleu/ntm-one-shot https://github.com/tristandeleu/ntm-one-shot https://github.com/macournoyer/neuralconvo https://github.com/macournoyer/neuralconvo