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Me personally I'm not 100% convinced that the current ML/AI approaches (that I'm aware of) will yield new big steps forward. Neuromorphic computing could have

Me personally I'm not 100% convinced that the current ML/AI approaches (that I'm aware of) will yield new big steps forward. Neuromorphic computing could have the potential I think, if we can build hardware that's good enough.

visarga 8y ago
This year brought to us huge improvements in handling text (BERT), speech (Google TTS, especially the Allo demo), images (ProGAN, StyleGAN, BigGAN) and activities such as games (Alpha Zero), and robotics. Even music composition is improving a lot. I don't feel it is slowing down yet. And when it will eventually slow down, it will be all for the better - we will have time to reopen all those different approaches that have been more or less ignored because of the DL hype. I think the key fields in AI will become simulator based learning (RL) and graph processing neural nets, because graphs can express any kind of highly dimensional data and are useful for reasoning tasks. They marry the symbolic and connectionist approaches. These two subdomains have had rapid evolution over the last couple of years. They also solve the data problem - in simulation you can produce as much data as you want, and graphs have combinatorial generalisation, thus they work on new configurations without retraining.
PinkMilkshake 8y ago
I often feel the same. I could show someone a single photograph of an animal they have never seen before and they will recognize it forever, from different angles, in different lighting, in black and white, probably even from a silhouette. The more I dig in to ML the more it seems like it's cheating, like a mathematical trick. At least the way we are using it. I can't help but feel like it's a game of Pachinko with pixels instead of balls, and neural weights instead of pins and holes. I'm no expert though so take my opinion with a grain of salt.

visarga 8y ago

This year brought to us huge improvements in handling text (BERT), speech (Google TTS, especially the Allo demo), images (ProGAN, StyleGAN, BigGAN) and activities such as games (Alpha Zero), and robotics. Even music composition is improving a lot. I don't feel it is slowing down yet. And when it will eventually slow down, it will be all for the better - we will have time to reopen all those different approaches that have been more or less ignored because of the DL hype. I think the key fields in AI will become simulator based learning (RL) and graph processing neural nets, because graphs can express any kind of highly dimensional data and are useful for reasoning tasks. They marry the symbolic and connectionist approaches. These two subdomains have had rapid evolution over the last couple of years. They also solve the data problem - in simulation you can produce as much data as you want, and graphs have combinatorial generalisation, thus they work on new configurations without retraining.

PinkMilkshake 8y ago

I often feel the same. I could show someone a single photograph of an animal they have never seen before and they will recognize it forever, from different angles, in different lighting, in black and white, probably even from a silhouette. The more I dig in to ML the more it seems like it's cheating, like a mathematical trick. At least the way we are using it. I can't help but feel like it's a game of Pachinko with pixels instead of balls, and neural weights instead of pins and holes. I'm no expert though so take my opinion with a grain of salt.