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> Humans only have one sensor: a rotatable stereo camera. So at least in theory the number of sensors seems not the most important element ;-) I hear this comm

> Humans only have one sensor: a rotatable stereo camera. So at least in theory the number of sensors seems not the most important element ;-) I hear this comment a lot when defending Tesla's choices, and it's a red herring. The fact that humans only rely on two cameras means nothing. Repeating old comments of mine: You also don't "need" megawatts of power to play top-level Go: humans do it with 100 watts of energy. Yet Google needed who knows how many megwatts of energy to train and run AlphaGo on their massive server farm. Imagine two companies competing to win at Go, and one company had the attitude that megawatts of energy was not necessary for training and prediction, and another company threw the biggest GPU farm they could. The second company just played top-level Go this year. The first company is ~10 years away from a low-energy elite Go computer. Humans implicitly perform SLAM (simulataneous localization and mapping). What do I mean? Look around your room. Close your eyes. Visualize the room. As a human, you've built a rough 3D model of the room. And if you keep your eyes open and walk through the room, that map is pretty fine-grained/detailed too and humans can keep track of where they are in the map. Doing this accurately in moving environments (especially with lots of pure forward motion) with just two cameras is still a wide open research problem. Doing this with LIDAR/GPS/IMU/recorded maps is solved. That's why people use LIDAR. Matching the abilities of human perception is an insanely hard problem. Don't let cute problems like image classification fool you. Why make the problem even harder?

taneq 10y ago
I've been wondering why no self driving systems seem to use DTAM or similar methods. Realtime dense 3D reconstruction and camera localisation on commodity hardware seems perfect for the job.
- argonaut 10y ago
  There is a big open research problem in the state of the art in visual SLAM (visual SLAM = SLAM from cameras), it doesn't work when the environment is moving (!!!). Visual slam is still linear-algebra/geometric/keyframe based traditional computer vision (including variants that incorporate GPS/accelerometer info). I think the state of the art is stereo LSD-SLAM, but I could be wrong.
  - jayjay71 10y ago
    A great way to explain this to people would be to ask them if they've ever seen a panoramic photo glitch, and tell them that's what the car would be seeing most of the time (well, assuming anything is moving, like a car or a person or a piece of trash).
demosthenes111 10y ago
Your point is generally valid, but it's worth noting that power =/= energy. It is conceivable that at 100 watts of power, a human could use on the order megawatt hours of energy while practicing to achieve top-level performance. 10000 hours of practice is not unreasonable to achieve mastery which would be 1MWh. It's also likely that AlphaGo is reasonably efficient, given that it used custom ASICs.
- argonaut 10y ago
  According to Google they used 280 GPUs and 1,920 CPUs. I'm sure they used custom chips in addition.
- Cybiote 10y ago
  There is also a difference between learning and playing. During play, the human operated at ~20 watts on computation while the machine ran at a rate of anywhere from 26,000 watts to 260,000 watts, depending on how efficient the TPUs are (and assuming 10x as the ideal case). The human is also learning new things about Go as it plays, planning complex muscle firing programs, filtering audio and managing attention, working on subconscious goals, running complex vision tasks, all while running its autonomic subsystem. Low power is also still important due to issues of heat and energy availability. Low power also implies high efficiency which is important for several reasons. The human brain is estimated at 20 watts (when people talk about computing systems they tend to not include the power needed for all the auxiliary infrastructure needed to keep it networked and cooled); it's also estimated that beyond 4 hours a day, learning effectiveness drops precipitously. If we take the case of Go, you can take a 4 year old human and have a professional player by 13. This is about 950 megajoules spent by the brain while learning Go. For the machine, if you look at the learning part (self play, value and policy on 50 GPUs for several weeks) the estimate on energy spend is about 30,000 megajoules. The policy network is itself ~20,000 MJ, while the full AlphaGo system playing on a single GPU and 48 CPUs is just a strong amateur. But this is not even an apples to apples comparison since the brain is not spending all of its energy on learning Go. In fact, learning how to play Go is very far from the most difficult thing the brain is learning how to do.
  - 10y ago
Qworg 10y ago
Even outside of the good points made by argonaut - our eyes are _amazing_ cameras. They are super high resolution, have many color bits, and work well in low light and bright situations.

taneq 10y ago

I've been wondering why no self driving systems seem to use DTAM or similar methods. Realtime dense 3D reconstruction and camera localisation on commodity hardware seems perfect for the job.

argonaut 10y ago
There is a big open research problem in the state of the art in visual SLAM (visual SLAM = SLAM from cameras), it doesn't work when the environment is moving (!!!). Visual slam is still linear-algebra/geometric/keyframe based traditional computer vision (including variants that incorporate GPS/accelerometer info). I think the state of the art is stereo LSD-SLAM, but I could be wrong.
- jayjay71 10y ago
  A great way to explain this to people would be to ask them if they've ever seen a panoramic photo glitch, and tell them that's what the car would be seeing most of the time (well, assuming anything is moving, like a car or a person or a piece of trash).

demosthenes111 10y ago

Your point is generally valid, but it's worth noting that power =/= energy. It is conceivable that at 100 watts of power, a human could use on the order megawatt hours of energy while practicing to achieve top-level performance. 10000 hours of practice is not unreasonable to achieve mastery which would be 1MWh. It's also likely that AlphaGo is reasonably efficient, given that it used custom ASICs.

argonaut 10y ago
According to Google they used 280 GPUs and 1,920 CPUs. I'm sure they used custom chips in addition.
Cybiote 10y ago
There is also a difference between learning and playing. During play, the human operated at ~20 watts on computation while the machine ran at a rate of anywhere from 26,000 watts to 260,000 watts, depending on how efficient the TPUs are (and assuming 10x as the ideal case). The human is also learning new things about Go as it plays, planning complex muscle firing programs, filtering audio and managing attention, working on subconscious goals, running complex vision tasks, all while running its autonomic subsystem. Low power is also still important due to issues of heat and energy availability. Low power also implies high efficiency which is important for several reasons. The human brain is estimated at 20 watts (when people talk about computing systems they tend to not include the power needed for all the auxiliary infrastructure needed to keep it networked and cooled); it's also estimated that beyond 4 hours a day, learning effectiveness drops precipitously. If we take the case of Go, you can take a 4 year old human and have a professional player by 13. This is about 950 megajoules spent by the brain while learning Go. For the machine, if you look at the learning part (self play, value and policy on 50 GPUs for several weeks) the estimate on energy spend is about 30,000 megajoules. The policy network is itself ~20,000 MJ, while the full AlphaGo system playing on a single GPU and 48 CPUs is just a strong amateur. But this is not even an apples to apples comparison since the brain is not spending all of its energy on learning Go. In fact, learning how to play Go is very far from the most difficult thing the brain is learning how to do.
- 10y ago

Qworg 10y ago

Even outside of the good points made by argonaut - our eyes are _amazing_ cameras. They are super high resolution, have many color bits, and work well in low light and bright situations.