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The thing I like the most about the current AI wave is the pressure is putting on computing hardware. Yes, mobile phones with long battery lives are cool and al
by dsign 4y ago
The thing I like the most about the current AI wave is the pressure is putting on computing hardware. Yes, mobile phones with long battery lives are cool and all of that, but most cool things I like are locked behind huge computational requirements.
- TaylorAlexander 4y agoAgree. I work in robotics and we never have enough compute. I want to see us get to the point where the most advanced robot ever has all the compute it needs onboard, and that means huge growth in compute density and efficiency are needed.
- ben_w 4y agoThat's genuinely surprising. What sort of on-board compute do you typically have today?
- bick_nyers 4y agoI don't work in the field but just to kind of put it into perspective, a 12v 100A LiFePO4 battery has 1200 Watts capacity and weighs 30 pounds. A typical gaming PC (which to be fair, is more willing to trade power for performance) consumes about 600 Watts per hour. Problem for a Tesla? Not so much. Problem for a lightweight drone? Definitely.
- idiotsecant 4y agoahhh the units in this post are making my eye twitch.
- imtringued 4y agoI would be mad too, if my gaming PC was demanding 300 Watts of power but it took half an hour to ramp up ;)
- bick_nyers 4y agoI know Watts per hour is not the right way to phrase it but I feel it helps for those that don't know. Also, I just don't like saying Amp. Hours :)
- idiotsecant 4y agoWatts per hour implies watts/hour. Watt-hour implies a number of watts multiplied by a length of time. Also known as energy. Watts are power. Watt hours are energy. Two different things. Watts/hour is nothing.
- slaw 4y agoWatt/hour is speed of power. It doesn't make any sense.
- RugnirViking 4y agoa common example from my robotics experience (mainly mobile robots) has been getting something powerful enough to run our image recognition/interpreting sensor data. We often have something like several microprocessors (think:arduino equivalent running c++ or c) which run all the motor control etc and a high level system (used to often be raspberry pi, now more often nvidia jetson nano) listening to all of those and using most of it's computing power on some kind of sensor data, usually image recognition or processing TOF camera/lidar/radar data etc. We often have to optimise hard to get a couple of cycles or "frames" per second with these, which really puts limitations on how robots respond (250ms delay is veeeery noticable, especially if it's in obstacle avoidance - relatively common)
- zamnos 4y agoLimiting ourselves to onboard compute available on mobile robots is one thing, but even for fixed installation robots, aka an arm in a factory where space and power aren't limited, we're very much still limited by compute capacity. Trying to use robots to do something as simple as folding clothes still cannot be done at a reasonable speed. Yeah, on a personal level, just buck up and spend the 20 minutes folding your clothes, or hire a maid to do it for you, but the complexity of automating the task of folding clothes by a robot is a stand in for other tasks in industry that we still can't automate because the complexity is still too high for our current computing power, and have to hire a human for. Researchers at US Berkeley came out with the algorithm they named SpeedFolding in October of last year. Watch https://youtu.be/UTMT2WAUlRw?t=511 https://youtu.be/UTMT2WAUlRw?t=511 and then realize that linked excerpt is sped up 9x. If we had 9x faster compute we could have laundry folding robots which is one thing, but that amount of compute would enable robots to do tons more tasks in industry.
- mlsu 4y agoRobotics is a double whammy, you have compute problems but you also have actuation. Getting robots to move quickly is easy; getting them to move quickly to exactly where you want them, or with exactly as much force... that is much, much more difficult. Double for mobile robots where you don't have a good energy source. If cost is an issue that is another dimension -- powerful and accurate actuators are extremely expensive.
- TaylorAlexander 4y agoThe NVIDIA Jetson boards are popular, but even with a full desktop processor and state of the art GPU, you can easily down them in data from a LIDAR sensor or a few cameras. Especially since robots may also need fast response times. There is another reply to your comment that shares a lot of what I have experienced. You have so many pieces of code that need to run and a good handful of them are working on something like LIDAR point clouds with a million 3D points in them, plus some cameras running several different image recognition and segmentation algorithms, and you want to have fast cycle times, it just all adds up. Every serious robot I have ever worked on is maxing out its system, even ones at Google X with a full desktop CPU, a high end NVIDIA graphics card, and a couple secondary ARM CPUs.
- ben_w 4y agoThanks! :) That definitely helps me understand why the footage of the robots in this video had to be sped up: https://youtu.be/Ybk8hxKeMYQ https://youtu.be/Ybk8hxKeMYQ
- aqme28 4y agoCrazy to me that as soon as one GPU wave is dying (crypto), another one is picking up slack.
- atleastoptimal 4y agoWhich is a good thing. So glad all that GPU compute is being used on cool stuff rather than running SHA-256 18 quintillion times
- DJBunnies 4y agoThey can’t both be cool?
- nightfly 4y agoOne is a contest to waste the most resources, one has potential to actually have useful results
- super256 4y agoWhy do you think Bitcoin does not have useful results?
- ben_w 4y agoStrictly speaking, the comment you're replying to doesn't say which of the two is a contest to waste resources and which has potential to have useful results.
- super256 4y agoI assumed that this was doubling down on atleastoptimal’s view.
- speedgoose 4y agoCan you cite an useful result? I can't but I don't think that some people getting richer is useful.
- pjmlp 4y agoWhat we will get are specialized hardware, with not so open APIs anyway. With a bunch of people trailling behind with "it kind of works" open alternatives.
- dsign 4y agoIt sounds like you are complaining about capitalism :-) It's not so bad. Nvidia could come and say, "hey, I'm going to lock down your GPU so that you can only use it to render polygons in my whitelisted list of video-games, and then you pay us $$$$$$ to buy our 'datacenter' thingy for anything else." But if they do it, people will go and buy the competitor's product. And yes, probably their 4090 are being bought by some rich kids with their parents' money, but I reckon most of it are sales to professionals, people who would justify their purchase decision with more than playing First-person-shooters. I for example play videogames with my gf, and we have equivalent GPUs. Hers is AMD and costs less than mine, even if it does the same, but I went for Nvidia so that PhysX were available and I could use Pytorch and Numba+GPU and even C++ CUDA. The moment Nvidia locks that down, I'll have to switch to AMD.
- HeartStrings 4y agoJohn Hopkins are working on organoids that will replace silicon GPUs for AI.
- pelagicAustral 4y agoIf they can get pass these new ethical committees...
- BizarroLand 4y agoI'm sure there are rat and mouse brain cells free for the taking from almost any pharmaceutical testing lab. If organics is the only factor, I don't know why those wouldn't perform as well as human or ape brain cells.
- colordrops 4y agoReminds me of this Choose Your Own Adventure book from 1984. It was about how PCs had organic AI components and each was unique, and you happened to get your hands on a super intelligent one. https://www.goodreads.com/en/book/show/755062 https://www.goodreads.com/en/book/show/755062
- ben_w 4y agoAs is one of the YouTubers I follow. Meatcubator: https://youtu.be/Z_ZGq8Tah0k https://youtu.be/Z_ZGq8Tah0k Growing human brain cells: https://youtu.be/V2YDApNRK3g https://youtu.be/V2YDApNRK3g
- TecoAndJix 4y agoHere is an article from JHU on that topic - https://hub.jhu.edu/2023/02/28/organoid-intelligence-biocomputers/ https://hub.jhu.edu/2023/02/28/organoid-intelligence-biocomp...