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But at least it is not the 5000th so-called AI-powered SaaS company that is using OpenAI API that has raised $20M+ to VCs and burning hundreds of thousands every month with little to no plan to generate revenue.
Will be watching this one closely, but highly skeptical of this company.
I have no idea about the merits of this approach, but I found this interview with the founders a lot more sensical than the linked article: https://twitter.com/Extropic_AI/status/1767203839818781085
This interview makes their product seem like BS. First, they literally cannot simply explain the problem or solution. Regardless, their pitch is that they're building a more power efficient probability distribution sampler. No one in AI research thinks that's a bottleneck. edit: btw the bottleneck in AI algos is matrix multiply and memory bandwith.
interesting that a company w/ no public repositories has 1.1k github followers https://github.com/extropic-ai
[1] https://en.wikipedia.org/wiki/Effective_accelerationism
[2] https://twitter.com/BasedBeffJezos
[3] https://knowyourmeme.com/memes/cultures/eacc-effective-accel...
I have no idea about the merits of this approach, but I found this interview with the founders a lot more sensical than the linked article: https://twitter.com/Extropic_AI/status/1767203839818781085
https://m.youtube.com/watch?v=8fEEbKJoNbU&pp=ygUVbGV4IGZyaWR...
Key Points
The demand for computing power in AI is increasing exponentially, but Moore's Law is slowing down due to fundamental physical limitations of transistors at the atomic scale.
Biology hosts more efficient computing circuitry than current human-made devices by leveraging intrinsic randomness in chemical reaction networks.
Energy-Based Models (EBMs) are a potential solution, as they are optimal for modeling probability distributions and require minimal data. However, sampling from EBMs is difficult on digital hardware.
Extropic is implementing EBMs directly as parameterized stochastic analog circuits, which can achieve orders of magnitude improvement in runtime and energy efficiency compared to digital computers.
Extropic's first processors are nano-fabricated from aluminum and run at low temperatures where they are superconducting, using Josephson junctions for nonlinearity.
Extropic is also developing semiconductor devices that operate at room temperature, sacrificing some energy efficiency for scalability and accessibility.
A software layer is being built to compile abstract specifications of EBMs to the relevant hardware control language, enabling Extropic accelerators to run large programs.
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Is this real or just theoretical?
So, basically this seems to be a way to replace PRNGs with real randomness with some knobs so you can adjust the distribution. Let's assume for the sake of argument that this can replace every single PRNG call in inference and training, how much savings in cost/energy/run time would there actually be?
"We're taking a new approach to building chips for AI because transistors can't get any smaller."
I really don't know what they gain by convoluting the point and it's pretty hard to follow what the CEO is talking about half the time.
I wouldn't want to write this off because you get the feeling these guys are on to something that could be hugely important (ignoring quantum this thermodynamic that) - but surely it feels like they need to get to the point a lot faster e.g. "We're taking a new approach to building chips for AI because transistors can't get any smaller." I really don't know what they gain by convoluting the point and it's pretty hard…
* The stochastic/random nature of processors is already used in cryptography for physically uncloneable functions. Dunno if this has any practical uses in industry, and it is crypto, so it is probably also BS, but it is the same phenomena you get if you log in into your BIOS and turn off ECC of your RAM.
* The very first computer capable of MCMC was designed by von Neumann himself and used uranium as a source of randomness as part of the Manhattan project.
Anyway semiconductors have never been my strong suit, but I guess this is more of a IP play then a consumer product business. Now let me get back to writing unit tests.