If the proof of concept requires spending a few days in the machine shop making jigs and parts, purchasing equipment, and a custom PCB, then I really hope you'll bring it up for discussion beforehand in a meeting. Ten minutes of discussion with colleagues might be as useful as several iterations of prototyping. Not so that they'll shoot it down, but because someone might say "oh yeah, we have a spare mcguffin from last year's demo that you can use, should save you lots of time."

There might be less that can be done about the underwater drag, but if it doesn't need to go long distances underwater that's not as much of a problem. For the RF signal, it can either run autonomously underwater, or use a fibre-optic umbilical, or even convert from an umbilical to wireless when it takes to the air.

Multibody orbits are one such chaotic system, which means you can take advantage of that chaos to redirect your space probe from one orbit to another using virtually zero fuel, as NASA did with its ISEE-3 spacecraft.

Your accounting works if someone else does the pyrolysis for you and you're left with just the H2 and C at the end, but mine includes the energy consumed by the pyrolysis step that breaks the methane molecule (albeit neglecting any thermodynamic losses, which there will be several -- for example you need to recapture the heat carried away by the hot carbon atoms). On the other hand, you can hardly wish for a better feedstock for CVD diamond production...

This would probably be much more efficient than doing pyrolysis to extract the hydrogen for use in electricity generation somewhere else, because you don't lose the substantial stored heat energy in the process of cooling that hydrogen back down.

And I can't help but wonder if fossil fuel companies might suddenly start endorsing aggressive zero-emissions targets if there's a way for this to double the demand for their products, rather than eliminating it.

https://web.archive.org/web/20180513182952/http://burro.case...

The short summary of this hypothesis is that the ocean develops hypoxic zones, anaerobic bacteria boom, and eventually the ocean starts releasing masses of poisonous H2S gas that wipes out most life on land (and strips the ozone layer for good measure).

They speculate that this might have been a mechanism behind the "great dying" at the end of the Permian. I'm sure the thinking has advanced in the last 20 years, but whenever people ask what the worst-case scenario for global warming could be, my mind drifts back to this.

But I think it's not very different from what people do. If directly asked to count how many legs a lion has, we're alert to it being a trick question so we'll actually do the work of counting, but if that image were instead just displayed in an advertisement on the side of a bus, I doubt most people would even notice that there was anything unusual about the lion. That doesn't mean that humans don't actually see, it just means that we incorporate our priors as part of visual processing.

If I were asked to count the number of legs, I would notice right away of course, but that's mainly because it would alert me to the fact that I'm in a psychology experiment, and so the number of legs is almost certainly not the usual four. Even then, I'd still have to look twice to make sure I hadn't miscounted the first time.

Comments URL: https://news.ycombinator.com/item?id=43778238

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As for avoiding certain cases, that could be done to some extent. But remember that the untrained transformer has no preconception of numbers or ordering (it doesn't use the hardware ALU or integer data type) so there has to be enough data in the training set to learn 0<1<2<3<4<5<6, etc.

For this hypothesis: The intelligence illusion is in the mind of the user and not in the LLM itself.

And yes, the notion was provided by the training data. It indeed had to learn that notion from the data, rather than parrot memorized lists or excerpts from the training set, because the problem space is too vast and the training set too small to brute force it.

The output lists were sorted in ascending order, the same way that I generated them for the training data. The sortedness is directly verifiable without me reading between the lines to infer something that isn't really there.

That settled this question for me.