jbay808

If the proof of concept takes an hour to code up, or proving the market exists just takes a bit of googling, then sure, you can prepare that before the first meeting where you suggest the idea.

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."

You must have very different kinds of meetings than I do. Unless you're going into that meeting with a rehearsed PowerPoint presentation, or there's a strict agenda that doesn't allow any time for exploration, I expect to hear imperfect-ideas-in-infancy. One of the reasons we have meetings is to allow collaboration to happen. It's a format for working together.

You can fire the shot and then patch the hole at the same time, proposing solutions to the same problem you pointed out, rather than just shooting and letting one person handle defense from every attack.

Definitely impressive as a proof of concept. A lot of the other problems can be solved with iteration. There are some IP67-rated drone motors meant to both fly and run underwater (available from Westmag for example).

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.

You can do a lot with chaos. One of the things it lets you do is find an unforced trajectory from the vicinity of any state to the vicinity of any other (accessible) state. Sensitivity to initial conditions means sensitivity to perturbations, which also means sensitivity to small control inputs, and this can be leveraged to your advantage.

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.

You have to account for the energy required to break the bonds of the CH4, though. This means if you burn methane the usual way you get (CH4 + 2O2 --> CO2 + 2H2O + 803 kJ/mol); if you burn it with an ideal zero-emissions reaction, you get (CH4 + O2 --> C + 2H2O + 409 kJ/mol), or just a little more than half the energy from the same gas.

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...

Interesting to see this on the HN front page. On the subject of methane pyrolysis, it turns out if you look at the Gibbs free energy calculation, about half of the energy of methane combustion is released from the formation of water, and the other half from the formation of carbon dioxide. That suggests that if you can be efficient with conserving the heat of pyrolysis, you can make a methane power plant that starts with a pyrolysis step to separate out the carbon atoms in an oxygen-free environment, and then burn the remaining hydrogen to power the cycle, and the end result would be a zero-emissions natural gas power plant. It would require twice as much gas to run, but if you can find a good value-added use for the carbon, it could potentially still be cost effective.

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.

It's mainly the laser itself that is the expensive part. If you only care about resolution it's easy, you just need a single-mode laser. But if you care about accuracy it's very difficult, because then the wavelength needs to be stable, and that requires a much more expensive laser. Most people looking for an interferometer are interested in accuracy, unless they're just measuring vibrations.

Thanks, it should be fixed now!

This one still keeps me up at night, especially the figure on the 6th page.

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.

I disagree with the assertion that "VLMs don't actually see - they rely on memorized knowledge instead of visual analysis". If that were really true, there's no way they would have scored as high as 17%. I think what this shows is that they over-weight their prior knowledge, or equivalently, they don't put enough weight on the possibility that they are being given a trick question. They are clearly biased, but they do see.

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 given five seconds to glance at the picture of a lion and then asked if there was anything unusual about it, I doubt I would notice that it had a fifth leg.

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.

This might be a great alternative to beryllium copper for the spring contact element in high-current electrical connectors.

Can you say more precisely what you mean?

I mean that needing to scan the full context of tokens before the nth is inherent to the problem of sorting. Transformers do scan that input, which is good; it's not surprising that they're up to the task. But pairwise numeral correlations will not do the job.

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.

I don’t really understand what you’re testing for?

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.

It might seem like you could sort with just pairwise correlations, but on closer analysis, you cannot. Generating the next correct token requires correctly weighing the entire context window.

I was interested in this question so I trained NanoGPT from scratch to sort lists of random numbers. It didn't take long to succeed with arbitrary reliability, even given only an infinitesimal fraction of the space of random and sorted lists as training data. Since I can evaluate the correctness of a sort arbitrarily, I could be certain that I wasn't projecting my own beliefs onto its response, and reading more into the output than was actually there.

That settled this question for me.

Yes, this is how frequency-doubled lasers work (eg. 532 nm green laser pointers, which are generated as a harmonic from a 1064 nm Nd:YVO4 laser by a nonlinear KTP crystal).

That's a great question! I have no idea. At low frequencies it should be very easy, because the sound is just a pressure measurement, so you can compare against a calibrated pressure reference. So the main challenge is measuring the high-frequency amplitude and phase response. If I had to do this, I'd probably set up a speaker in a long box with standing-wave resonance modes, and put both the microphone-under-test and a laser interferometer at an antinode to measure the change in refractive index that occurs with air pressure. A photodiode should have a flat frequency response out to well beyond 20 kHz, so that would do well as a calibration. But this is probably overkill for audible frequencies.

This project might be exactly what you're looking for:

https://github.com/peterdsharpe/AeroSandbox

This is correct, although in my experience the distribution is often more like a lognormal than a Gaussian: https://thesearesystems.substack.com/p/task-estimation-conqu...

I'd expect Chebyshev polynomials to be much faster and easier to work with than splines, certainly, and probably Fourier series as well. (Especially if there aren't trig instructions in hardware, because then each sine or cosine is itself a chebyshev polynomial to evaluate).

One reason you can get away with this sometimes is that ADCs themselves act like a low-pass RC filter. The ADC input is itself a capacitor that gets charged to the voltage that needs to be measured, and it has an appreciable resistance (which can be supplemented with an additional input resistor). Sometimes that's all the low-pass filtering you need to prevent aliasing, and the rest can often be done in the digital domain.

But if the signal you're measuring is very weak (or poorly matched to the ADC range), you probably need some kind of amplifier for it anyway, and in that case you may as well make a filter out of it at the same time to maximize the signal-to-noise ratio.

The past decade has seen a huge number of problems widely and confidently believed to be "actual hard mode problems" turn out to be solvable by AI. This makes me skeptical that the problems today's experts think are hard aren't easily solvable too.

Good question. Temperature is a funny thing. When a system is far from equilibrium, the notion of temperature becomes a little unfamiliar.

The thermodynamic temperature of the radiation is connected to the entropy of its power spectrum. LEDs and especially lasers emit very low-entropy light, which can be focused and heat a surface up to very high temperatures. (Sunlight focused onto a surface cannot heat that surface above the temperature of the sun). Thermodynamically speaking, a low-entropy power source like a laser -- and whatever is driving it -- must have a very high exergy, which is equivalent to behaving like a high temperature heat source, even though it might feel cold to the touch.

Some more details here: https://en.wikipedia.org/wiki/Exergy#Quality_of_energy_types

Stored electricity's equivalence to a high-temperature heat source is one of the things that makes it so useful. It's intrinsically connected to why it takes a lot of low-grade heat to produce a small amount of electricity in the first place, and also why electric furnaces can produce such high temperatures. So while a battery can drive an LED that shines on a PV panel that generates power with everything feeling equally warm to the touch, a temperature gradient is still necessary; it's just been moved outside the boundaries of the system, to the process that distilled the entropy out of the energy that became the battery's stored charge. We can reversibly recreate this temperature gradient by driving a Carnot engine with that battery, instead of a laser.

A photovoltaic cell also requires a temperature gradient. In the case of a solar panel, it's the high temperature of the sun that shifts its blackbody spectrum into a range where the cell can generate electricity. But if you were to heat the PV cell to that same temperature as the surface of the sun (somehow without melting it), it would glow and radiate away just as much light as it absorbs from the sun, rather than converting any sunlight into usable electricity.

Likewise the phonovoltaic would need to operate on a phonon spectrum that is shifted away from that of the material's own temperature, in order to not violate the laws of thermodynamics.

Thanks; I'm working on products in this area and not especially satisfied by the parts available. Especially as Analog seems to be abandoning their micro-MPPT IC product line. My email is jacob at jbaylessconsulting dot ca.

One of the common issues with solar micro-MPPT is EMI emissions. Have you tested for compliance with emitted radio-frequency noise standards?

Do you have a spec sheet for your MPPT chip that you can share? The white paper and video presentation that you linked appear to be completely unrelated.

Great talk and very well presented.