Difference between revisions of "Probabilistic Computing"
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* 2025-05-19 [https://spectrum.ieee.org/thermodynamic-computing-normal-computing Prototype Computer Uses Noise to Its Advantage] Thermodynamic computing isn't just a rebrand of probabilistic computing | * 2025-05-19 [https://spectrum.ieee.org/thermodynamic-computing-normal-computing Prototype Computer Uses Noise to Its Advantage] Thermodynamic computing isn't just a rebrand of probabilistic computing | ||
| + | This approach is a natural fit for certain scientific computing applications that already include randomness, such as Monte-Carlo simulations. It is also well suited for AI image generation algorithm stable diffusion, and a type of AI known as probabilistic AI. Surprisingly, it also appears to be well-suited for some linear algebra computations that are not inherently probabilistic. This makes the approach more broadly applicable to AI training. | ||
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| + | “Now we see with AI that paradigm of CPUs and GPUs is being used, but it’s being used because it was there. There was nothing else. Say I found a gold mine. I want to basically dig it. Do I have a shovel? Or do I have a bulldozer? I have a shovel, just dig,” says Mohammad C. Bozchalui, the CEO and co-founder of Ludwig Computing. “We are saying this is a different world which requires a different tool.” | ||
==References== | ==References== | ||
[[Category: Programming]] | [[Category: Programming]] | ||
Revision as of 13:36, 21 May 2025
Contents
Meme
Start with noise, end up with information.
Context
Given some configuration, is it possible to just let the computer run and see where it gets?
Consider Cellular Automata, there the beginning slate is completely blank until some pattern is injected at the automata just runs it course.
Solutions
- 2025-05-19 Prototype Computer Uses Noise to Its Advantage Thermodynamic computing isn't just a rebrand of probabilistic computing
This approach is a natural fit for certain scientific computing applications that already include randomness, such as Monte-Carlo simulations. It is also well suited for AI image generation algorithm stable diffusion, and a type of AI known as probabilistic AI. Surprisingly, it also appears to be well-suited for some linear algebra computations that are not inherently probabilistic. This makes the approach more broadly applicable to AI training.
“Now we see with AI that paradigm of CPUs and GPUs is being used, but it’s being used because it was there. There was nothing else. Say I found a gold mine. I want to basically dig it. Do I have a shovel? Or do I have a bulldozer? I have a shovel, just dig,” says Mohammad C. Bozchalui, the CEO and co-founder of Ludwig Computing. “We are saying this is a different world which requires a different tool.”
