What will prevent LLMs from designing robot control circuitry and participating in increase of chip production/design and physical experimentation?
How do you think why there's this fad of producing general purpose humanoid robots?
What will prevent LLMs from designing robot control circuitry and participating in increase of chip production/design and physical experimentation?
How do you think why there's this fad of producing general purpose humanoid robots?
> How do you think why there's this fad of producing general purpose humanoid robots?
For doing physical work?
So a swarm of robots builds the shell of your fab overnight, and then what? Where is the EUV machine coming from?
So far the most we're seen TeslaBot do is serve drinks via tele-operation, and I don't think it's exactly built for construction site work.
For example, TSMC uses behavioral cloning to scale up human-bottlenecked parts of the manufacturing process to meet the growing demand, while automated research laboratories do thousands experiments in parallel to find better manufacturing processes.
The production bottleneck in a fab isn't the human workers - the process is mostly automated. The bottleneck more derives from how many wafers per hour you can process, which comes down to the etching process and EUV throughput.
ASMLs EUV machines are literally the most complex machine that mankind has ever built, which is why no other country, including the US, has yet been able to duplicate it. It's not just the machine itself, but a global supply chain of irreplaceable components such as focusing mirrors made by Zeiss to an incomprehensible level of accuracy - differences in surface height no more than the size of a hydrogen atom (or if you scaled the mirror up to the size of the country of Germany, then surface differences in height of 0.1mm).
Robots are useful to automate things, but they are zero help when trying to build tech like this that you are incapable of building in the first place.
The US has fallen way behind in manufacturing expertise, and no swarm of robots is going to help.
You've missed a part where it's TSMC that does behavioral cloning (to build more EUV machines). The full vertical integration is a bit farther down the line.
Etching a model's weights on silicon is another way to utilize non-top-notch tech-processes, while maintaining or improving performance. (and it suits robotics well)
TSMC doesn't know how to build EUV machines - they are stuck buying them from ASML like everyone else.
Putting a model's weights in read-only memory close to the processor is certainly a way to increase token/sec generation speed, but of course does nothing to increase intelligence. Robots aren't going to help though - semiconductor manufacturing is semiconductor manufacturing regardless of whether you are etching GPUs or memory onto your wafers.
Ah, sorry, it's ASML expertise that needs to be cloned and scaled up. I don't see how it changes things, though.
Robots don't need that much intelligence. High-speed joint control, "hand-eye coordination", the higher level tasks can be delegated to external models. Distillation already works quite well for isolating the required functionality.
I'm pretty sure ASML, and their supply chain, do have plans to increase production, as do the chip fabs - they all see the demand, while at the same time being leary of boom and bust which is the historical reality of the chip business.
But, the production expansion rate of none of these companies is being limited by lack of trained personnel, and if it were it would surely be faster to hire/train more humans since robots are still very far from human dexterity, not to mention intelligence.
Robots and AI are tools of automation, a way to replace humans with machines, but not all the problems in the world are bottle-necked by lack of humans, or the cost of humans.
Investing in training a person gets you one trained person. Investing in training an ML system gets you a cloneable ML system that can be scaled on demand much faster. ROI might change quickly.
Sure, but we're simply not at the point, maybe never will be, where lack of employees is the bottleneck to chip production. A fab takes billions of dollars and multiple years to construct - there are many constraints.
> What will prevent LLMs from designing robot control circuitry and participating in increase of chip production/design and physical experimentation?
Money, regulations, EUV machine lead-times, global helium supply, reality ...
It's funny that we've got the Dwarkesh contingent saying that GPUs will become infinitely expensive, and now another contingent saying that they will become infinitely abundant.
Even if compute were free, and/or the AI was so smart that it picked the right experiments to run every time ("make no mistakes"), you still have to actually train the model, which takes months, and if model Ver. N+1 depends on model Ver. N, then it's iterative regardless of how much compute you have.
Who's saying that compute will become infinitely abundant? "Singularity" is just a way of saying that known models begin to give absurd predictions. Anyway, intelligence is a way of overcoming obstacles. 10 million tonnes of helium is a nice head start and retraining models from scratch is not guaranteed to last forever.
AFAIK the notion of a/the technological "singularity" is a point in time where technology is building upon itself (RSI!) so fast, at an ever increasing pace, that the speed of change effectively becomes infinite and incomprehensible to humans.
The word "singularity" is presumably coming from math or space, like a black hole singularity where matter becomes infinitely dense and the known laws of physics break down.
> 10 million tonnes of helium is a nice head start
Yeah, but then you need to refine it to 99.9999% purity, to be able to use it.