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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

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.
erikpukinskis 11y ago
Do you not recognize how weak this inference is? "We are going faster and faster so we will get there" is not the way you calculate the likelihood of reaching a destination within a certain time.
- sapphireblue 11y ago
  Of course there is no certainty. For example the Moore's "law" (~ economic trend) has officially died. We can't rely on free bi-annual compute doubling anymore. Still, deep learning has become a very powerful tool and we haven't discovered all possible applications just yet. DL models can learn a huge array of tasks, most of these tasks haven't been tried yet. Maybe there is a way to achieve some limited form of general intelligence even with current state of art (deepmind's A3C agent) if one trained it in some special way nobody haven't thought of yet.
- monkmartinez 11y ago
  >"We are going faster and faster so we will get there" is not the way you calculate the likelihood of reaching a destination within a certain time. Why not? With incomplete information, you can not say the inverse is true either. At the end of the day, most of us have no direct control over the AI situation.
  - erikpukinskis 11y ago
    I think a better approach would be to start with a more opinionated model of cognition, and then look at how the space defined by the model is getting coverage in research. "The brain is a Von Neumann machine and our ability to model it tracks with Moore's law" is demonstrably false. There are better models than that.

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.

erikpukinskis 11y ago

Do you not recognize how weak this inference is? "We are going faster and faster so we will get there" is not the way you calculate the likelihood of reaching a destination within a certain time.

sapphireblue 11y ago
Of course there is no certainty. For example the Moore's "law" (~ economic trend) has officially died. We can't rely on free bi-annual compute doubling anymore. Still, deep learning has become a very powerful tool and we haven't discovered all possible applications just yet. DL models can learn a huge array of tasks, most of these tasks haven't been tried yet. Maybe there is a way to achieve some limited form of general intelligence even with current state of art (deepmind's A3C agent) if one trained it in some special way nobody haven't thought of yet.
monkmartinez 11y ago
>"We are going faster and faster so we will get there" is not the way you calculate the likelihood of reaching a destination within a certain time. Why not? With incomplete information, you can not say the inverse is true either. At the end of the day, most of us have no direct control over the AI situation.
- erikpukinskis 11y ago
  I think a better approach would be to start with a more opinionated model of cognition, and then look at how the space defined by the model is getting coverage in research. "The brain is a Von Neumann machine and our ability to model it tracks with Moore's law" is demonstrably false. There are better models than that.