The next wave of progress will deal with what deep learning cannot handle today -- reasoning and abstraction. But the thing is, human-level general capabilities are not one wave of innovation away, nor three
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maybe because back-propagation is not the most efficient algorithm? Is the world waiting for something better?
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Probably from a scientific perspective but from an engineering perspective we just got started. Eg voice rec still requires very directed speech to devices and cannot follow in natural settings. Same for vision.
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is there a source / some facts that support the "entered diminishing returns territory" argument?
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Now we just have to understand how to make these things work efficiently and robustly and develop standardized processes to deal with their development and maintenance. Not that simple...
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Means ? Can you explain this a bit ? Are you saying the ROI is not there anymore ?
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Perhaps the next wave is simply a further miniaturization and integrating application into all aspects of society.
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These areall perception related tasks. Not much happening in common sense reasoning for example
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Deep learning systems have no idea what they're seeing. We have to tell them with a label. But they still have no idea. Deep learning is irrelevant to AGI research. In fact, it's a hindrance because it's syphoning funds away from other approaches that hold much better promise.
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Are there any measures that make this clear? And should one be assessing absolute accuracy or log accuracy? (Since whole challenge is getting that last 9.)
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We should def be assessing it by log accuracy: there is a great report about the development of AI here: http://aiindex.org/2017-report.pdf
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