[autonomousErwin]

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 to follow what the CEO is talking about half the time.

[pclmulqdq]

Quantum computing people have been selling this exact spiel (including the convoluted talking points) for decades and it keeps working at getting funded. It has not produced any results for the rest of us, though.

[duped]

One difference is that baking mathematical models into electronic analogs is older than integrated circuits. The reason we deviated from that model is because the re-programmability and cost of general purpose, digital computers was way more economical than bespoke hardware for expensive and temperamental single purpose analog computers. The unit economics basically killed analog computing. What Extropic (and others) have identified is that in the case of machine learning, the pendulum might have to swing back because we do have a large scale need for bespoke hardware. We'll see if they're right.

Quantum computing has been exploring an entirely new model of computation for which it's hard to even articulate the problems it can solve. Whereas using analog computers in place of digital is already well defined.

[pclmulqdq]

A lot of quantum computing companies have the same idea of hard-baked analog computing for a useful algorithm. D-Wave was the biggest one to go bust.

[pillusmany]

Neither has fusion research produced anything for us yet. Should we stop funding it?

[pclmulqdq]

Arguably yes, in a commercial sense. To give the fusion folks some credit, they haven't been promising that commercial products are "just around the corner" for the last 30 years the way QC people have, and the quacks (cold fusion) were excised from the field for making those false promises. I do think that if your field as a whole continually makes huge promises and never delivers, it should probably tarnish that field's reputation.

However, if you're thinking about research grants, no. That's the point of research grants.

[_sword]

The tech could be really cool if e.g. classifiers could be represented within the probability space modeled on their hardware. However their shaman-speak isn't confidence inducing.

[zoogeny]

Your summary seems to miss a later quote from the article:

> Extropic is also building semiconductor devices that operate at room temperature to extend our reach to a larger market. These devices trade the Josephson junction for the transistor. Doing so sacrifices some energy efficiency compared to superconducting devices. In exchange, it allows one to build them using standard manufacturing processes and supply chains, unlocking massive scale.

So, their mass-market device is going to be based on transistors.

The actual article read like a weird mesh of techno-babble and startup-evangelism to me. I can't judge if what they are suggesting is vaporware or hyperbole. This is one of those cases where they are either way ahead of my own thinking or they are trying to bamboozle me with jargon.

I personally find it hard to categorize a lot of AI hype into "worth actually looking into" vs. "total waste of time". The best I can do in this case is suspend my judgement and if they come up again with something more substantive than a rambling post then I can always readjust.

[schiffern]

> trying to bamboozle me with jargon

Am I the only one who thought the article was clear, lucid, and reasonably concise?

The company's success or failure will depend on execution, but the value proposition is quite sound. Maybe I've just spent too much time in the intersection between information theory, thermodynamics, and signal processing...

"Don't splurge on high SNR ('digital') hardware just to re-introduce noise later." == "Don't dig a hole and fill it in again. You waste energy twice!"

[semi-extrinsic]

> Doing so sacrifices some energy efficiency compared to superconducting devices.

In most applications superconductivity does not actually yield better energy efficiency at system level, since it turns out cooling stuff to negative several hundred degrees is quite energy demanding.

[riwsky]

Convolutional neural networks were a huge advancement in their time

[autonomousErwin]

I don't disagree. I just come away from the article feeling more confused as opposed to enlightened and excited about what they're building.

It even makes me think that they don't understand what they're talking about which is why they're using complicated terminology to mask it but I'm hopeful I'm wrong and this is an engineering innovation that benefits everyone.

[Animats]

I get that feeling, too.

There may or may not be something there, but the article is mostly buzzword-slinging. They wrote "This will allow us to put an Extropic accelerator in every home, enabling everyone to partake in the thermodynamic AI acceleration." Huh?

If they said something like "We are trying to cut the cost of stable diffusion by a factor of 100", that would sort of make sense. But then people would want to see a demo.

[autonomousErwin]

A proof-of-concept would be amazing and that's what I thought they were releasing and would justify the hype (as opposed to a whitepaper). Maybe in a couple of months we'll see a HN post doing exactly this and we can eat our words (which I really hope is the case).