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I feel like the places where CV always proves most useful are places where either we need more eyes than would be financially viable, or we need eyes in places

I feel like the places where CV always proves most useful are places where either we need more eyes than would be financially viable, or we need eyes in places its hard/unsafe/expensive to have them. The tipping point on training+pay for a human vs an ML/CV system for a lot of tasks is coming down. I saw a Microsoft spotlight last fall on a company that's using drones + CV to inspect power infrastructure in Scandinavia. As much as the tech to do that has cost, its probably cheaper than sending out helicopters with specialists all the time to check power line towers for wear/damage.

nostrademons 10y ago
Or we need eyes that can drive a physical response faster & more accurately than a human can react. This is the whole robotics/drone/hardware market. Right now, most of these efforts work at the same speed humans work (probably because we still need to monitor them to make sure they're doing the right thing). But imagine self-driving cars with no traffic lights, because the cars can react & communicate fast enough to avoid a collision without any macro-scale signaling. Manufacturing processes where molten metal is shaped directly into whatever shape desired, where computers do real-time calculations of the effect of gravity & cooling rate on the position of the material. Airliners that never land and can take on passengers anywhere, because passengers ascend in a personal drone that mates with the mothership under computer control. Unfortunately CVaaS isn't really suitable for this, because the network delays transmitting the data to a datacenter will outweigh any speed benefits of computerizing it. But I could see a big market for services that let you train a model in the cloud at high-speed, and then download the computed model for execution on a local GPU or TPU.
- sharun 10y ago
  For human level vision and speech (and even search) long term we might not need computational clouds and data centers. We don't see them in nature. The models/indexes will likely just come prepackaged on device.
  - nojvek 10y ago
    This is only a matter of time. The imagenet model can already run on my iPhone on the gpu with BNNS. Not real-time but going there very soon. Once you have real time local inferencre on your mobile phone. Boom!

nostrademons 10y ago

Or we need eyes that can drive a physical response faster & more accurately than a human can react. This is the whole robotics/drone/hardware market. Right now, most of these efforts work at the same speed humans work (probably because we still need to monitor them to make sure they're doing the right thing). But imagine self-driving cars with no traffic lights, because the cars can react & communicate fast enough to avoid a collision without any macro-scale signaling. Manufacturing processes where molten metal is shaped directly into whatever shape desired, where computers do real-time calculations of the effect of gravity & cooling rate on the position of the material. Airliners that never land and can take on passengers anywhere, because passengers ascend in a personal drone that mates with the mothership under computer control. Unfortunately CVaaS isn't really suitable for this, because the network delays transmitting the data to a datacenter will outweigh any speed benefits of computerizing it. But I could see a big market for services that let you train a model in the cloud at high-speed, and then download the computed model for execution on a local GPU or TPU.

sharun 10y ago
For human level vision and speech (and even search) long term we might not need computational clouds and data centers. We don't see them in nature. The models/indexes will likely just come prepackaged on device.
- nojvek 10y ago
  This is only a matter of time. The imagenet model can already run on my iPhone on the gpu with BNNS. Not real-time but going there very soon. Once you have real time local inferencre on your mobile phone. Boom!