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A good measure of any of the techniques are the results. We see record object recognition and speech recognition results. How many of these results are from Num
by chimtim 11y ago
A good measure of any of the techniques are the results. We see record object recognition and speech recognition results. How many of these results are from Numenta or IBM neomorphic chips? Most successes have been on deep learning architectures over GPUs (and large datasets). While these fancy architectures may have their applications, they have nothing to do with recent advancements in last 3 years.
- emcq 11y agoTo be fair, the goal of those new computing architectures is not to advance algorithms. That at best is a side benefit. Those algorithmic performance improvements seen in DNN have come from improved datasets and training. Numenta and IBM arent focusing on training AFAIK. Google's Quantum Annealing [0] is the only hardware I'm aware of focused on training, although there are rumors Nervana Systems may produce something [1]. I'm sure there are others; accelerating training of DNN isnt a particularly new idea. The goal of these other computing architectures is typically to provide lower power, higher frequency/lower latency, or smaller form factor execution of trained models, but there is a question of how much value they can provide over more conventional chips to be worth the chip design costs. However without these architectures becoming as mainstream as say a GPU, I think we will continue to see advances come from the typical everyday computer. The ML community seems to be much more democratic than others. [0] http://googleresearch.blogspot.com/2015/12/when-can-quantum-annealing-win.html http://googleresearch.blogspot.com/2015/12/when-can-quantum-... [1] http://www.nervanasys.com/about/ http://www.nervanasys.com/about/