3 ms·
Our human designs strive to work in many environmental conditions. Many early AI designs, if iterated in the real world, would incorporate local physical condit
by molticrystal 2y ago
Our human designs strive to work in many environmental conditions. Many early AI designs, if iterated in the real world, would incorporate local physical conditions into their circuits. For example, that fluorescent lamp or fan I'm picking up(from the AI/evolutionary design algorithm's perspective) has great EM waves that could serve as a reliable clock source, eliminating the need for my own. Thus if you move things it would break.
I am sure there are analogous problems in the digital simulation domain. Without thorough oversight and testing through multiple power cycles, it's difficult to predict how well the circuit will function, and how incorporating feedback into the program will affect its direction, if not careful, causing the aforementioned strange problems.
Although the article mentions corrections to the designs, what may be truly needed is more constraints. The better we define these constraints, the more likely correctness will emerge on its own.
- skissane 2y ago> Our human designs strive to work in many environmental conditions. Many early AI designs, if iterated in the real world, would incorporate local physical conditions into their circuits. For example, that fluorescent lamp or fan I'm picking up(from the AI/evolutionary design algorithm's perspective) has great EM waves that could serve as a reliable clock source, eliminating the need for my own. Thus if you move things it would break. This problem may have a relatively simple fix: have two FPGAs – from different manufacturing lots, maybe even different models or brands – each in a different physical location, maybe even on different continents. If the AI or evolutionary algorithm has to evolve something that works on both FPGAs, it will naturally avoid purely local stuff which works on one and not the other, and produce a much more general solution.
- reissbaker 2y agoThis is similar to why increasing the batch size during LLM training results in better performance: you force the optimizer to generalize to a larger set.
- IsTom 2y agoAnd then you change temperatre/elevation/move it next to a router and it falls apart, because after all there is going to be something correlated.
- FeepingCreature 2y agoGreat, so use ten. Use a hundred. Spread them around. Put one on the ISS. The problems just have to be uncorrelated.
- skissane 2y ago> Put one on the ISS. I can see it already: cloud provider offers orbital FPGAs for testing your AI hardware designs