conjecTech

Joined 14 years ago

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Comment · 1 month ago
CMU will henceforth stand for Central Miami University
Comment · 2 months ago
No, per capita electricity usage dropped slightly over the last 25 years, driven in part by LED adoption.
Comment · 2 months ago
Jevon's Paradox says nothing about the size of the incremental demand though. Lighting is a great example. The US went from spending 15% of our electricity on lighting to ~4%. LEDs are maybe 5x as efficient, so that would be 3% without any change in usage. So we did see maybe a 30% increase in consumption as a result of lower prices, but that was nowhere near large enough to offset the efficiency gain in terms of overall consumption. Las Vegas may have, but that is a very small part of overall usage.

I anticipate we will see something similar with intelligence. There is probably headroom to consume 100x as much intelligence in R&D. But that isn't most of the economy. Will run of the mill service jobs increase their use of intelligence by enough to offset the effect of cheaper prices? I think that's the real question.

Comment · 2 months ago
It will likely be on the enterprise side if it happens. Hard to control supply, but if demand is concentrated to a few hundred entities, that's easy to do.
Comment · 2 months ago
Both can be true simultaneously. American companies were comfortable with a high cost structure because it padded their revenue numbers. The government tried to block out competitors through import/export controls, and it ended up backfiring by creating more efficient competitors. Very similar to our competition with Japan in the 80s. We'll see if we give the AI labs the kinds of sweetheart protectionism that US car makers got.
Comment · 1 year ago
Very nice work. Training these from scratch is a big undertaking.

- Did you train the encoder & decoder together or separately? It would be nice to have the encoder representation be compatible with the existing whisper implementation since it would mean you could swap your implementation into models where its used as a component, like in the recent Voxtral model. I'd imagine it also might make training a bit faster as well.

- Did you consider training the turbo model as well?

Comment · 1 year ago
EPA range tends to be pessimistic for EVs as it assumes you are always traveling at highway speeds. Even small reductions in speeds can make EVs much more efficient since drag is quadratic. A quick google search shows Prius prime owners reporting 4-5.5 miles/kwh, so the 3-6 mile range is entirely plausible.
Comment · 1 year ago
You can buy stock in a solar "yieldco", which is exactly this. It holds the assets of solar farms and pays out the cashflows. There were a lot circa 2018, I believe Brookfield bought up a bunch, so there may be fewer options now.
Comment · 1 year ago
It's a very simple change in a vanilla python implementation. The encoder is a set of attention blocks, and the length of the attention can be changed without changing the calculation at all.

Here(https://github.com/openai/whisper/blob/main/whisper/model.py...) is the relevant code in the whisper repo. You'd just need to change the for loop to an enumerate and subsample the context along its length at the point you want. I believe it would be:

for i, block in enumerate(self.blocks): x = block(x) if i==4: x = x[,,::2]

Comment · 1 year ago
If you are hosting whisper yourself, you can do something slightly more elegant, but with the same effect. You can downsample/pool the context 2:1 (or potentially more) a few layers into the encoder. That allows you to do the equivalent of speeding up audio without worry about potential spectral losses. For whisper large v3, that gets you nearly double throughput in exchange for a relative ~4% WER increase.

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