71–80 of 128 posts
I think most people believe that the problem with robots is that we don't have the right software, and if we just knew how to program them then today's robots could be incredibly useful in everyday life. From that perspective, this move from OpenAI seems dumb. That belief is wrong. Today's robots can't be made useful in everyday life no matter how advanced the software. The hardware is too inflexible, too unreliable,…
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.
Robotics research is going to be extremely binary. It's expensive and frustrating, and there's little use for it until it works as well as human labor, which is a high bar.
But, once that Rubicon is crossed, I believe there will be a sort of singularity in that space. It's related to but somewhat orthogonal to the singularity that's prognosticated for g a i.
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.
We are decades away from being able to build a general bipedal robot that can snake out a plugged toilet or dig a trench or nail shingles to a roof. It's just not a rational goal yet. Aim lower.
Earlier quoted context omitted.
I'm going to need you to unpack that a bit. Isn't interacting with an environment and observing the result exactly what natural cognition does? What area of machine learning do you feel is closer to how natural cognition works?
> What area of machine learning do you feel is closer to how natural cognition works? None. The prevalent ideas in ML are a) "training" a model via supervised learning b) optimizing model parameters via function minimization/backpropagation/delta rule. There is no evidence for trial & error iterative optimization in natural cognition. If you'd try to map it to cognition research the closest thing would be behaviorist…
The missing component is memory. Once models have memory at runtime — once we get rid of the training/inference separation - they’ll be much more useful.
The cynical-but-likely-accurate take is that researching language modeling has a higher ROI and lower risk than researching robotics.
Yes. Also, regular ML researchers sit at tables with laptops. Robotics people need electronics labs and electronics technicians, machine shops and machinists, test tracks and test track staff... If you have to build stuff, and you're not in a place that builds stuff on a regular basis, it takes way too long to get stuff built.
I wonder why they don't invest in establishing the competency for robotics. The potential return might seem enormous, though their choices might signify that they don't agree.
Or maybe they just aren't willing to leave their comfort zone. 'Software will eat the world' is a convenient idea for people who want to stay in that comfort zone.
I think most people believe that the problem with robots is that we don't have the right software, and if we just knew how to program them then today's robots could be incredibly useful in everyday life. From that perspective, this move from OpenAI seems dumb. That belief is wrong. Today's robots can't be made useful in everyday life no matter how advanced the software. The hardware is too inflexible, too unreliable,…
"When the hardware is ready, the software will be comparatively easy to develop." I take it you've never written any software for a robot? The long tail of the real world takes years and years to handle. Probably the most advanced robotics company, at the cutting edge of the ML+Robotics, is Covariant and their entire business model rests on an understanding that the long tail can and should be handled by humans.
I agree that OpenAI is right to cut out the hardware, but all your reasoning about why is wrong.
The reason, which they state, is that data collection on physical devices is slow and modification to those devices is slow and maintenance on those devices is expensive. You want to simulate everything, not because it reproduces the real world in high fidelity, that doesn't matter, but because it gives you approximations with sufficient variety and complexity that you can continually challenge your AI, and you can do all that at 1M fps.
I think most people believe that the problem with robots is that we don't have the right software, and if we just knew how to program them then today's robots could be incredibly useful in everyday life. From that perspective, this move from OpenAI seems dumb. That belief is wrong. Today's robots can't be made useful in everyday life no matter how advanced the software. The hardware is too inflexible, too unreliable,…
Dangerous, fast, reliable. Pick three.
Later you will have a Spot robot chasing a person and getting it to stop, surrendering at the machine without being threatened by it, but just by recognizing that there's no longer a point in running away.
I think most people believe that the problem with robots is that we don't have the right software, and if we just knew how to program them then today's robots could be incredibly useful in everyday life. From that perspective, this move from OpenAI seems dumb. That belief is wrong. Today's robots can't be made useful in everyday life no matter how advanced the software. The hardware is too inflexible, too unreliable,…
You're demonstrably wrong. I can waldo any number of commercially available arms to do work humans do today. Surgeons waldo precise robots to conduct surgeries as a matter of course. Every piece of construction machinery operated by a human today is an incredibly useful robot lacking sufficiently capable software. "When the hardware is ready, the software will be comparatively easy to develop." I take it you've never…
Earlier quoted context omitted.
> What area of machine learning do you feel is closer to how natural cognition works? None. The prevalent ideas in ML are a) "training" a model via supervised learning b) optimizing model parameters via function minimization/backpropagation/delta rule. There is no evidence for trial & error iterative optimization in natural cognition. If you'd try to map it to cognition research the closest thing would be behaviorist…
For what it’s worth, I agree with this take. But I think RL isn’t completely orthogonal to the ideas here. The missing component is memory. Once models have memory at runtime — once we get rid of the training/inference separation - they’ll be much more useful.
Earlier quoted context omitted.
You're demonstrably wrong. I can waldo any number of commercially available arms to do work humans do today. Surgeons waldo precise robots to conduct surgeries as a matter of course. Every piece of construction machinery operated by a human today is an incredibly useful robot lacking sufficiently capable software. "When the hardware is ready, the software will be comparatively easy to develop." I take it you've never…
Off-topic but this is the first time I've seen waldo used as a verb. It's not in the Urban Dictionary, but from the way you use it I take it that it means "to find the correct thing out of the sea of so many similar things", right?