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There is a tendency among non-technical admirers of ML to regard deep learning methods as beyond their creators: independent entities that will one day, given r
by osmode 11y ago
There is a tendency among non-technical admirers of ML to regard deep learning methods as beyond their creators: independent entities that will one day, given refined enough algorithms and enough energy, out-comprehend their human creators and overwhelm humanity with their artificial consciousnesses. The term “neural networks” is itself a misnomer that doesn’t at all reflect the complexity of how human neurons represent and acquire information; it’s simply a term for nonlinear classification algorithms that began catching on once the computing power to run them emerged.
The question of whether or not deep neural networks are capable of “understanding” is largely a theoretical concern for the ML practitioner, who spends the bulk of his or her time undertaking the hard work of curating manually labeled data, fine-tuning his or her neural classifier with methods (or hacks) such as dropout, stochastic gradient descent, convolution and recursion, to increase its accuracy by a few fractions of a percentage point. Ten or twenty years from now, I imagine we’ll be dealing with a novel set of ML tools that will evolve with the rise of quantum computing (the term “machine learning” will probably be ancient history, too), but the essence of these methods will probably remain: to train a mathematical model to perform task X while generalizing its performance to the real world.
As fascinating and exciting as this era of artificial intelligence is, we should also remember that these algorithms are ultimately sophisticated classifiers that don't "understand" anything at all.
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- proc0 11y agoThis is true of ANN, and deep learning. They are mathematical models of learning that are finally practical after a couple decades (not to diminish anything the researchers have accomplished which is incredible). Then there are biologically inspired neural networks, like Hierarchical Temporal Memory (HTM), that actually correlate directly to how the cortex in mammals work. These have also demonstrated learning capabilities, and seem a lot more promising in the road map to general AI, in my opinion, because after all we should be piggy-backing on evolution (not that we can't find a mathematical model first). So yeah, the hype is just hype, but it could be justified for the wrong reasons if we see breakthroughs in biologically inspired AI (the Brain Project, to name another example).
- mhewett 11y agoBefore anyone believes the hype, they should read all the MIT research papers from the mid-1990s that mention the term "emergent intelligence". This was one of the biggest wastes of research money in the history of AI.
- argonaut 11y agoDemonstrated learning capabilities? I have not seen HTM models make any breakthroughs on any benchmarks. It's also stretching the facts to say it directly correlates to how mammalian cortexs work. At best, you could say it directly correlates to some theories on how mammalian cortexes work - neuroscience has an incredibly poor understanding of brains in general.
- Leszek 11y agoI agree with you, but I should point out that adding recursion is hardly "fine-tuning", it's changing the network into something entirely different.