41–50 of 128 posts
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Why do you think that?
They smell the urgency in the air, and they're close enough to the center to get a good and accurate whiff
I think even if you have intuitions about an approach, and have promising results, if you're trying to arrive at something new, it's really hard to know how far away you are.
This is lunacy. The first country/company to replace human labour with general bipedal robots, will reap wealth beyond imagination. The short sitedness is astonishing, if you ask me. I genuinely believe how we as a society act once human labour is replaced is first aspect of the great filter.
Humans ARE genral bipedal robots. The price of these robots is determined by the minimum wage.
Is the prevailing opinion that progress in reinforcement learning is dependent on algorithmic advances, as opposed to simply scaling existing algorithms? If that is the case, I could see this decision as an acknowledgement that they are not well positioned to push the frontier of reinforcement learning - at least not compared to any other academic or industry lab. Where they have seen success, and the direction it se…
Are there any Open* organizations for robotics that could perhaps fill the void here? I think robotics is really important and I think the software is a big deal also, but it's important that actual physical trials of these AIs are pursued. I would think that seeing something in real space like that offers an unparalleled insight for expert observers.
I remember the first time I ever orchestrated a DB failover routine, my boss took me into the server room when it was scheduled on the testing cluster. Hearing all the machines spin up and the hard drives start humming, that was a powerful and visceral moment for me and really crystallized what seemed like importance about my job.
I'm sure the overhead and upkeep of a robotics lab far outweighs that of a computer lab for software research. Are there any Open* organizations for robotics that could perhaps fill the void here? I think robotics is really important and I think the software is a big deal also, but it's important that actual physical trials of these AIs are pursued. I would think that seeing something in real space like that offers a…
Is the prevailing opinion that progress in reinforcement learning is dependent on algorithmic advances, as opposed to simply scaling existing algorithms? If that is the case, I could see this decision as an acknowledgement that they are not well positioned to push the frontier of reinforcement learning - at least not compared to any other academic or industry lab. Where they have seen success, and the direction it se…
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Has robotics had such an 'ImageNet moment'? Nothing springs to mind, just slow advancement over decades. If suddenly robot manipulators could grasp any object, operate any knob/switch, tie knots, manipulate cloth, with the same manipulator, on first sight, that would be quite a feat. But then there's still task planning which is a very different topic. And ... and .... So much still to develop for generally useful ro…
Not yet. I have a four wheel drive robot I designed with four 4k cameras feeding in to an Nvidia Jetson Xavier. [1] Just getting it to navigate itself using vision would mean building a complex system with a lot of pieces (beyond the most basic demo anyway). You need separate neural nets doing all kinds of different tasks and you need a massive training system for it all. You can see how much work Tesla has had to do…
RL, which I think this particular story is about, is an odd-duck. I have papers on this and I personally have mixed feelings. I am a very applications/solutions-oriented researcher and I am a bit skeptical about how pragmatic the state of the field is (e.g. reward function specification). The argument made by the OpenAI founder on RL not being amenable to taking advantage of large datasets is a pretty valid point.
Finally, you raise interesting points on running multiple complex DNNs. Have you tried hooking things to ROS and using that as a scaffolding (I'm not a robotics guy .. just dabble in that as a hobby so curious what the solutions are). Google has something called MediaPipe, which is intriguing but maybe not what you need. I've seen some NVIDIA frameworks but they basically do pub-sub in a sub-optimal way. Curious what your thoughts are on what makes existing solutions insufficient (I feel they are too!)
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Reinforcement learning itself is a dead-end on a road to AI. They seem to slowly starting to realize it, probably ahead of academia.
Why do you believe this to be the case?
The achievements of RL are so dramatically oversold that it can probably be called the new snake oil.
This is lunacy. The first country/company to replace human labour with general bipedal robots, will reap wealth beyond imagination. The short sitedness is astonishing, if you ask me. I genuinely believe how we as a society act once human labour is replaced is first aspect of the great filter.
Imagine that there only needs to be ten people to “run the world”. What is the population size going to be then? Ten? As large as possible? Somehow it seems that the way we’re headed, it’ll be ten plus some administrative overhead.
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Not yet. I have a four wheel drive robot I designed with four 4k cameras feeding in to an Nvidia Jetson Xavier. [1] Just getting it to navigate itself using vision would mean building a complex system with a lot of pieces (beyond the most basic demo anyway). You need separate neural nets doing all kinds of different tasks and you need a massive training system for it all. You can see how much work Tesla has had to do…
I am a researcher on the AI/Systems side and I wanted to chime in. Transformers are amazing for language, and have broken all the SOTA is many areas (at the start of the year, some people may have wondered if CNNs are dead [they are not as I see it]). The issue with Transformer models is the insane amount of data they need. There is some amazing progress on using unsupervised methods to help, but that just saves you…
Yes unless the industry sees value in a step change in the scale on offer to regular devs, progress on massive nets will be slow.
Hooking things together is pretty much my job. I have used ROS extensively in the past but now I just hook things together using python.
But I consider what Tesla is doing to be pretty promising, and they are layering neural nets together where the output of three special purpose networks feed in to one big one etc. They call that a hydra net. No framework like ROS is required because each net was trained in situ with the other nets on the output of those nets, so I believe all compute logic is handled within the neural network processor (at some point they integrate standard logic too but a lot happens before that). Definitely watch some Karpathy talks on that.
And currently I am simply not skilled enough to compose multiple networks like that. So I could use multiple standalone networks, process them separately, and link them together using IPC of some kind, but it would be very slow compared to what's possible. That's why I say we're "not there yet". Something like Tesla's system available as an open source project would be a boon, but the method is still very labor intensive compared to a self-learning system. It does have the advantage of being modular and testable though.
I probably will hand compose a few networks (using IPC) eventually. I mean right now I am working on two networks - an RL trained trail following network trained in simulation on segmentation-like data (perhaps using Dreamer V2), and a semantic segmentation net that is trained on my hand labeled dataset with "trail/not-trail" segmentation. So far my segmentation net works okay. And a first step will actually be to hand-write an algorithm to go from segmentation data to steering. My simulation stuff is almost working. I built up a training environment using Godot video game engine and hacked the shared memory neural net training add on to accept image data, but when I run the sim in training on DreamerV2, something in the shared memory interface crashes and I have not resolved it. [1]
But all of this is a hobby and I have a huge work project [2] I am managing myself that is important to me, so the self driving off road stuff has been on pause. But I don't stress about it too much because the longer I wait, the better my options get on the neural network side. Currently my off road rover is getting some mechanical repairs, but I do want to bring it back up soon.
[1] https://github.com/lupoglaz/GodotAIGym/issues/15
[2] https://community.twistedfields.com/t/a-closer-look-at-acorn...