At an AI conference in Montreal, Prof. Yoshua Bengio said:
"Concentration of wealth leads to concentration of power. That's one reason why monopoly is dangerous. It's dangerous for democracy."
One consequence of deep learning becoming more mainstream is that the barriers of learning and practicing it are continually being lowered. In the past, working on state-of-the-art machine learning requires clusters of high-end machines. But now, it is possible to use a PC with a good GPU card, and do some experiments on your own.
At the same time, however, such democratization doesn't extend upstream to the cutting-edge research side. Deep learning research has increasingly been concentrated in hands of a few large companies. Such drain of talents from academia is becoming a problem, and perhaps why Bengio is sounding the alarm.
If you are interested in SDC, you would notice that the Waymo-Uber lawsuit is still going on. In fact, unlike the seemingly more gentle stance earlier in Summer, Waymo in reinitiating charges against Uber after discovering new Uber technical documents.
The whole drama centers on one person, Mr. Anthony Levandowski. This Wired piece is a good profile on him.
While peripheral, it's an interesting study into a key person (up till recently at least) whose interesting worldview include robots taking over the world. As quote in the Wired article:
“We’re going to take over the world. One robot at a time,” wrote Levandowski another time [to Kalanick]"
Intel just released a chip but it is not a standard deep learning chip as Google's Tensor processing unit (TPU) or Nvidia's recently released NVLDA.
Intel's line of work seems to base on spiking neural network (SNN) which was always described as closer to human brain than the usual deep neural network. What's the difference? For starters, deep neural network doesn't quite consider spike train as human brain neurons do. And timing of the spikes usually convey a lot of information within our biological neural networks.
Neuromorphic design also uses less energy. In the case of Lolhi, the new chip, it could use down to 2000 times less energy consumption.
Perhaps one thing we are not sure is how fast the new chip could be. Currently we only heard about test case in MNIST.