4 ms·

> The second is far more interesting. Strong AI. The distant future of 30 years, and maybe sooner. I think it's very likely this will happen in our lifetime Wh

by jahnu 11y ago

> The second is far more interesting. Strong AI. The distant future of 30 years, and maybe sooner. I think it's very likely this will happen in our lifetime Why? I'd genuinely like to know. I've yet to read anything to convince me this is true.

AnimalMuppet 11y ago
Pro: We see things like chess and go. We see more areas that we thought computers could never tackle being solved. So we seem to be getting closer. Con: We don't actually understand what intelligence is, except by human example. We don't actually have any idea of how far we have to go to create something similar.
maaku 11y ago
Some would argue that we're finally at the place where most of the pieces necessary for strong AI are known, it's just a massive engineering challenge to put them all together. Like going to the moon in 1958, or nuclear energy in 1938. We can draw smooth exponential lines like Kurzweil does and see when the level of complexity of our AI servants reaches the same level as the human brain (~30 years), or we can hope for a concerted human effort as was seen in both the 40's and the 60's. Either way it looks to be in our near future.
sapphireblue 11y ago
The progress in machine learning/deep learning for last 5 years has been literally staggering. Read about new state of art results. There is already superhuman visual object recognition, voice recognition, good machine translation, description of scenes in natural language, question answering (facebook babi dataset), visual question answering, algorithm learning (neural turing machines, neural GPUs), and finally really strong reinforcement learning (deepmind's DQN, A3C algorithms). The last one, reinforcement learning, is really important because it's a basis for general intelligence. By rewarding/punishing a reinforcement learning agent you can train it to execute very complex tasks, for example navigating a maze with pixel input or controlling a robot body to do some tasks. DeepMind is on the cutting edge of this kind of research, you can read about their latest agent here http://arxiv.org/abs/1602.01783 http://arxiv.org/abs/1602.01783 With such rate of progress it's not inconceivable if a sub-human but general intelligence could be developed in 10-20 years, or even sooner.
- visarga 11y ago
  It would have a lot to do with dialogue systems (chat bots). I am very interested in this domain but the news seem to be slow. Siri didn't improve, Google Now is no conversationalist, and the Microsoft's Tay disappeared within 24 hours of being put online. I tried some other chat bots but they seem incapable of bouncing back ideas and forming a proper conversation. Maybe they could apply reinforcement learning to training a chatbot how to direct the flow of a conversation in order to make it interesting. I think it could be huge if only the dialogue was a little better.
  - Houshalter 11y ago
    The best chatbots are not open to the public. Because the current RNN tech is just way too computationally expensive to have on the public internet. Watson was able to answer most the questions on Jeopardy, but it required a room full of servers. Google published some promising results of an RNN chatbot that was trained on movie scripts, but as far as I know it was never released publicly. Microsoft supposedly has an AI chatbot that is really incredible in China. Tay was supposed to be the start of something like that for the English world, but had that stupid scandal.
    - sapphireblue 11y ago
      To be honest Watson didn't use any deep learning (I read the paper, see http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165 http://www.aaai.org/ojs/index.php/aimagazine/article/view/23... ). It uses an ensemble of dozens of question-answering algorithms. Still it's very cool. A name DeepQA was a bit misleading, perhaps "wide QA" would be better. RNN chatbots are the future, but for now they aren't stable enough to fulfill customers' elevated expectations. Also there is a problem with objective function that rewards interesting conversation, there is some new research on this problem that hasn't made it into open RNN chatbots yet. The standard objective function was giving most probable answers, it turned out to be boring. I agree with grandparent that Reinforcement Learning is a right approach for training an intellectual chatbot, but there is a problem of well-designed training curriculum (i.e. some systematic scheme of rewarding and punishing the RL agent). I wouldn't wonder if facebook's M team tries different approaches, including RL, to train on their data.

by jahnu 11y ago

AnimalMuppet 11y ago

Pro: We see things like chess and go. We see more areas that we thought computers could never tackle being solved. So we seem to be getting closer. Con: We don't actually understand what intelligence is, except by human example. We don't actually have any idea of how far we have to go to create something similar.

maaku 11y ago

Some would argue that we're finally at the place where most of the pieces necessary for strong AI are known, it's just a massive engineering challenge to put them all together. Like going to the moon in 1958, or nuclear energy in 1938. We can draw smooth exponential lines like Kurzweil does and see when the level of complexity of our AI servants reaches the same level as the human brain (~30 years), or we can hope for a concerted human effort as was seen in both the 40's and the 60's. Either way it looks to be in our near future.

sapphireblue 11y ago

The progress in machine learning/deep learning for last 5 years has been literally staggering. Read about new state of art results. There is already superhuman visual object recognition, voice recognition, good machine translation, description of scenes in natural language, question answering (facebook babi dataset), visual question answering, algorithm learning (neural turing machines, neural GPUs), and finally really strong reinforcement learning (deepmind's DQN, A3C algorithms). The last one, reinforcement learning, is really important because it's a basis for general intelligence. By rewarding/punishing a reinforcement learning agent you can train it to execute very complex tasks, for example navigating a maze with pixel input or controlling a robot body to do some tasks. DeepMind is on the cutting edge of this kind of research, you can read about their latest agent here http://arxiv.org/abs/1602.01783 http://arxiv.org/abs/1602.01783 With such rate of progress it's not inconceivable if a sub-human but general intelligence could be developed in 10-20 years, or even sooner.

visarga 11y ago
It would have a lot to do with dialogue systems (chat bots). I am very interested in this domain but the news seem to be slow. Siri didn't improve, Google Now is no conversationalist, and the Microsoft's Tay disappeared within 24 hours of being put online. I tried some other chat bots but they seem incapable of bouncing back ideas and forming a proper conversation. Maybe they could apply reinforcement learning to training a chatbot how to direct the flow of a conversation in order to make it interesting. I think it could be huge if only the dialogue was a little better.
- Houshalter 11y ago
  The best chatbots are not open to the public. Because the current RNN tech is just way too computationally expensive to have on the public internet. Watson was able to answer most the questions on Jeopardy, but it required a room full of servers. Google published some promising results of an RNN chatbot that was trained on movie scripts, but as far as I know it was never released publicly. Microsoft supposedly has an AI chatbot that is really incredible in China. Tay was supposed to be the start of something like that for the English world, but had that stupid scandal.
  - sapphireblue 11y ago
    To be honest Watson didn't use any deep learning (I read the paper, see http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165 http://www.aaai.org/ojs/index.php/aimagazine/article/view/23... ). It uses an ensemble of dozens of question-answering algorithms. Still it's very cool. A name DeepQA was a bit misleading, perhaps "wide QA" would be better. RNN chatbots are the future, but for now they aren't stable enough to fulfill customers' elevated expectations. Also there is a problem with objective function that rewards interesting conversation, there is some new research on this problem that hasn't made it into open RNN chatbots yet. The standard objective function was giving most probable answers, it turned out to be boring. I agree with grandparent that Reinforcement Learning is a right approach for training an intellectual chatbot, but there is a problem of well-designed training curriculum (i.e. some systematic scheme of rewarding and punishing the RL agent). I wouldn't wonder if facebook's M team tries different approaches, including RL, to train on their data.