4 ms·
There is some good information in there and I agree with the limitations he states, but his conclusion is completely made up. "To lift some of these limitation
by toisanji 9y ago
There is some good information in there and I agree with the limitations he states, but his conclusion is completely made up.
"To lift some of these limitations and start competing with human brains, we need to move away from straightforward input-to-output mappings, and on to reasoning and abstraction."
There are tens of thousands of scientists and researchers who are studying the brain from every level and we are making tiny dents into understanding it. We have no idea what the key ingredient is , nor if it is 1 or many ingredients that will take us to the next level. Look at deep learning, we had the techniques for it since the 70's, yet it is only now that we can start to exploit it. Some people think the next thing is the connectome, time, forgetting neurons, oscillations, number counting, embodied cognition,emotions,etc. No one really knows and it is very hard to test, the only "smart beings" we know of are ourselves and we can't really do experiments on humans because of laws and ethical reasons. Computer Scientists like many of us here like to theorize on how AI could work, but very little of it is tested out. I wish we had a faster way to test out more competing theories and models.
- deleted 9y ago[deleted]
- fchollet 9y agoHardly a "made-up" conclusion -- just a teaser for the next post, which deals with how we can achieve "extreme generalization" via abstraction and reasoning, and how we can concretely implement those in machine learning models.
- eli_gottlieb 9y ago>I wish we had a faster way to test out more competing theories and models. Luckily, the state of actual cognitive science and neuroscience is fairly far ahead of, "Gosh there's all these things and we just don't know." Unfortunately, MIT-style cogsci hasn't generated New Jerseyan fast-though-wrong algorithms for Silicon Valley to hype up, so the popular press keeps spreading the myth of our total ignorance. Besides which, we do know what's missing from deep learning: the ability to express anything other than a trivial Euclidean-space topological structure. We know that real data is sampled from a world subject to cause-and-effect, and that any manifold describing the data should carry the causal structure in its own topology.
- bjt 9y ago> we can't really do experiments on humans because of laws and ethical reasons. Ethics and laws constrain but do not forbid experimenting on humans. We do experiments on humans all the time, including experiments on how people learn and reason. There are numerous academic journals devoted to these topics.
- landon32 9y agoI don't think he's talking about literally mimicking how the human brain works. It seems like he's just talking about making neural nets more effective in certain tasks by allowing for more types of abstraction, just like a human brain has more types of abstraction than Artificial Neural Networks do.
- deleted 9y ago[deleted]