5 ms·

When I was at Watson this is the first thing I told every customer: before you start with AI are you already doing the more mundane data science on your structu

• dheera 10y ago
  And before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough. It's just basic physics. I worked on one for my PhD research. You can get pretty clean depth data with some basic statistics and no AI algorithm wizardry. We're just waiting for someone to take it to a fab, build driver electronics, and commercialize it.
  • taeric 10y ago
    I think the hidden strife in your triplet is a land mine. That is, getting more data is at odds with cleaner data for most folks. Adding accuracy? I hope you remember the difference between precision and accuracy. :) (None of which takes from your point.)
  • pesenti 10y ago
    Right but in this case there is a trade-off: if you don't have the better data now how long before you will? Should you start now (and gain a time advantage at a cost) with what you have or wait for the better data (read sensor/process/technology/people etc). My experience is that waiting for cleaner data is often like waiting for Godot and will often be a project killer (sometimes justifiably). This is a key issue at the moment in advanced ML: clean large training sets ideal for supervised training are elusive and the companies making real-world advances are pretty much all using available data (and semi-supervised techniques) rather than expensive made up training sets.
  • agumonkey 10y ago
    ToF depth being single POV and depth infered from successive frames ?
    • sirclueless 10y ago
      No, that would also qualify as "AI algorithm wizardry" as dheera puts it. Instead it's just a hardware sensor that measures the time at which light arrives at each pixel. Knowing the speed of light you can calculate the depth of each pixel.
      • agumonkey 10y ago
        Oh I see, a physical "Z-Buffer".
    • blackguardx 10y ago
      A light source built into the camera is modulated in time. The camera uses this to infer depth. When the modulation is synced up right, the camera can see the light wave "travel" as it illuminates close objects first and then farther ones over successive frames. The camera isn't actually fast enough to capture the light wave traveling, but the timing between the shutter and the light source is shifted by nanoseconds each frame to accomplish this. They can have problems operating outside because it is hard to make a lightsource brighter than the sun.
      • sherbondy 10y ago
        Hey, 3D vision system amateur here, but very interested to learn more! Can anybody point me to some literature or reference materials about attempts to combine the inputs from multiple techniques simultaneously? E.g. a device with stereo conventional cameras and infrared cameras & emitters which compares the resulting model from each input source/technique and actively re-adjusts final depth estimate? Is "sensor fusion" the right jargon to use in this context? Or, even crazier, a control system which actively jitters the camera's pose to gain more information for points in the depth map with lower confidence scores / conflicting estimates? But maybe such a setup is overly complex and yields minimal gains in mixed indoor & outdoor scenarios?
  • kken 10y ago
    Already happening. The activity in the TOF-field is crazy. First project Tango smartphone will be out soon.
    • sherbondy 10y ago
      First consumer Tango phone is already out! http://shop.lenovo.com/us/en/smartphones/phab-series/phab2-pro/ http://shop.lenovo.com/us/en/smartphones/phab-series/phab2-p... I think it first went on sale Nov. 1 but just started shipping initial batches recently.
  • dTal 10y ago
    Stereo vision is obviously highly effective in biology as it has independently evolved a great many times. Time-of-flight may be poised for a renaissance, but it scales badly and is active, not passive. Stereo vision, and its big brother light fields, are far more general and are certainly not going to "die out".
    • revelation 10y ago
      Yet humans produce terrible depth data.
      • jonathankoren 10y ago
        Humans produce good enough depth data.
      • kgwgk 10y ago
        What does it mean? I'd say the ability to perceive depth is pretty useful.
      • kd0amg 10y ago
        Terrible for what purpose? Humans seem pretty good at throwing things to each other and catching them. I'm very bad at coming up with a good numeric estimate of linear size. As a fencer, I could never tell you how many inches between me and my opponent, how long his or my arms are, how tall he is, etc. I could definitely tell you which parts of our bodies are within reach of each other's arm extension, fleche, lunge, etc.
        • michaelt 10y ago
          Terrible for the purpose of proving a general-purpose stereo machine vision system is practical. The distance at which a stereo vision system can capture precise depths depends on the distance between eyes, and the eyes' angular resolution. Human depth perception works well for things within about 10m, but when you get out to 20-40m humans get a lot less info from stereo vision. When you get to that distance, humans seem to have a whole load of different tricks - shadows, rate of size change, recognising things of known size, perspective and so on. You can see a car and know how far it is even without stereo vision, because you know how big cars are, and how big lanes and road markings are. You can even see two red lights in the distance at night and work out whether they're the two corners of a car, or two motorbikes side-by-side and closer to you. On the other hand, your basic general-purpose stereo machine vision system doesn't try to understand what it's looking at - you just identify 'landmarks' that can be matched in both images (high contrast features, corners etc) and measure the difference in angle from the two cameras. This is relatively simple and easy to understand! For tasks that humans can do that involve depth perception of things more than ~40m away - flying a plane, for example, where most things are more than 40m away if you're doing it right! - nice simple stereo vision can't get the job done, because humans are actually using their other tricks. Of course, despite this limitation stereo vision comes up a lot in nature - it's still a beneficial adaption, because most things in nature that will kill you do so from less than 10m away :)
  • eli_gottlieb 10y ago
    >Better time-of-flight depth cameras will be here soon enough. It's just basic physics. I worked on one for my PhD research. You can get pretty clean depth data with some basic statistics and no AI algorithm wizardry. We're just waiting for someone to take it to a fab, build driver electronics, and commercialize it. You should talk to us at Leaflabs. Commercializing research-level tech in embedded electronics is what we do.
    • mathperson 10y ago
      are you guys looking to hire anyone with compressed sensing/neuro-imaging experience? possibly someone with the username /mathperson lol
      • eli_gottlieb 10y ago
        Neuro-imaging? Very probably. Send a resume and cover letter.
  • MrQuincle 10y ago
    However as ma2rten eloquently describes, for the purpose of AI research it might be beneficial to bite through the harder AI required when only having access to stereo vision. A lot of problems are not tackled on a fundamental level. Occlusion, context, proprioception, prediction, timing, attention, saliency, etc. A simple rat has more intelligence than whatever is behind a dashcam, security cam or webcam.
  • cma 10y ago
    Any links to your papers? How much better have things gotten over e.g. Kinect v2, which was pretty bulky and power hungry, and had active cooling? I'd imagine it has gotten a lot better.
  • AndrewKemendo 10y ago
    You're ignoring form factor for optimized technology. Yes, a quality TOF system would be great. However good luck convincing consumers to adopt hardware with lidar on it. The Tango is having enough trouble on it's own and it does pretty well for consumer systems with IR. Besides that you can't do FTDT with laser systems AFAIK. You need something to capture unseen places, such as ultrasonics/HF - which I guess you could argue fall into TOF but I haven't seen that work done. In the end my money (literally!) is on the opposite if your approach, namely building better RGB systems because there are already a trillion cameras deployed that we can extract from.
  • enriquto 10y ago
    > For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision Maybe for regular "cameras", but parallax still gives the most accurate results for 3D reconstruction from satellite images, where it is a very lively area of research. The resolution, and surface coverage, of radar satellites is far worse than what you can obtain by stereo matching optical images taken by the powerful telescopes on satellites. I guess in other fields like microscopy it makes a lot of sense also. Not all imaging happens indoor on commodity cameras!
• tptacek 10y ago
  So the basic criticism here is than Hanson, in suggesting firms clean up their data and then apply simple analytics to it, is defining away the problem that ML solves?
  • jeremyjh 10y ago
    Hanson is economist, not a computer scientist. He may not be right about exactly why the hype is unfounded. But he is not wrong about the fact that it is unfounded. There is no "awe-inspiring" rate of success with these projects, no massive revenue streams that are boiling up all over the landscape, which is absolutely what would have to be happening already to possibly justify predictions of "47% of US jobs in 20 years". The giant piles of dirty data are that way because for thirty years no one has considered them worth cleaning up. How will they create such astounding amounts of unexpected value?
    • pesenti 10y ago
      > The giant piles of dirty data are that way because for thirty years no one has considered them worth cleaning up. How will they create such astounding amounts of unexpected value? It was left that way because we didn't have the tools to process it. Imagine the amount of unprocessed video data that we can now annotate pretty accurately. What's the value of that data now?