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  • Gc - Tuesday, September 26, 2017 - link

    "deep learning requirements in autonomous machines"

    "Learning" requirements? Or "inference/recognition" requirements?

    Some safety critical devices, like medical devices, are licensed based on behavior during licensing tests. Any software change requires expensive retesting, so no updates. The most advertised pattern recognition capabilities of future cars and drones regard self-driving, which means those are safety critical capabilities. Testing them to be sure they cannot learn behavior outside the licensed safety requirements, no matter what driving conditions they experience over years of use, would seem to make model testing even more complex. Are self-driving controls, that learn, licensed now?

    Rather than only testing the model design once for all cars of that model, another approach is to test every car at its annual inspection. The test might take long to do physically, so someone might propose disconnecting the control unit and testing virtually, perhaps with accelerated time. That adds additional risks (disconnectable controls may become less reliable, changeable clock may not reset correctly).

    If learning is enabled, then training seems a little like dreaming, rehearsing situations remembered or imagined variations. When would it dream, at stop lights? If it is an important dream lesson from a near miss that just occured, or a wheel that is getting out of balance, it might not 'want' to wake up and drive before it learned the lesson.
  • Fallen Kell - Wednesday, September 27, 2017 - link

    I think you misunderstand deep learning. There are multiple parts to deep learning, first of which is the construction and design of the neural network itself (i.e. how many layers it should have, how many inputs and outputs, etc., etc.). Then there is the training of the network. This is the part where the actual "learning" occurs. Then finally there is the operation of the already trained network. This final stage is the part that Nvidia is targeting with Xavier. There is no more actual learning occurring, just the implementation of the already learned behaviors.
  • MrSpadge - Wednesday, September 27, 2017 - link

    I think he understands this well. That's why he asked in the first place whether "Learning" requirements?" actually meant "inference/recognition requirements". It seems rather obvious they mean the latter, as the former would lead to all kinds of problems he mentioned.
  • Fallen Kell - Wednesday, September 27, 2017 - link

    Except that it isn't "inference/recognition" in deep learning, it is simply a complex probability function, based on the training data typically with risk/reward functions taken into account (for instance with driving, the network is probably rewarded for getting to the destination in a timely fashion, while obeying various driving rules/laws, and at the same time keeping the passengers safe, as well as other humans safe without damage to the vehicle or other property). It is unclear if higher weights are placed on the passenger, or other humans, or to the vehicle or other vehicles, that all depends on the reward set.

    As the network is trained, weights are adjusted between the various nodes in the network, and in the case of something like driving, previous states are also part of the input (as the algorithm would need to remember what it just did and why, to take those previous actions into account for the next action to take). Once the weights have been defined on the links between the various nodes and layers of the network, using that network is just a simply matter of running through the probabilities to generate the highest probable action/decision that received the highest reward from during the training phases.

    Inference/recognition makes an assumption that a pattern is being followed, when in reality, it is just a probability function.
  • skavi - Tuesday, September 26, 2017 - link

    I wish they would come back to mobile. We need more competition and custom (non ARM) designs in the Android space.
  • milkod2001 - Wednesday, September 27, 2017 - link

    NV likes fat margins the same as Intel or Apple. I don't see NV entering Android space anytime soon.The place is already overcrowded.
  • Santoval - Wednesday, September 27, 2017 - link

    I assume by "non ARM" you mean "custom or semi-custom CPUs with ARM ISA", right? Currently there are only two options for ISA of mobile CPUs : ARM and MIPS. The latter currently undergoes a process of either restructuring or shattering, x86 is out of the mobile game, so what remains is only ARM.
  • lada - Friday, September 29, 2017 - link

    and RISC-V , which is free and open. And very efficient, as much that nVidia chose it to be the ISA for the GPUs' control MCUs. But it has a great potential to be the main CPU's ISA (32bit, 64bit and 128bit ISAs are specced).
  • tipoo - Wednesday, September 27, 2017 - link


    Yeah, Denver 1 was one of the few designs that went as wide as Apple. Shame about its binary translator choking up on anything harder to predict. Denver 2 with that shortcoming addressed may have been interesting.

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