coderenegade

I think what the OP is getting at is that if you can only prompt the model and aren't bringing anything to the table yourself, you're going to get a generic answer that looks like all of the others. The point is to figure out what differentiates you as a developer. Yes, that will favor those with more experience, but that's always been the case.

But pure math is a game that only cares about the rules and definitions as you choose to define them. It's not about understanding the world at all. The application of math in science (which is about understanding the world) is usually (but admittedly not always) after the fact.

Thank you. The OP is urging others to take an ethics class, when they need to be taking a history class. The golden rule apparently didn't apply to the guy whose heart you had to eat to keep the sun rising.

Right, and that's the point.

The law isn't a moral benchmark, because morality shifts over time, both for what constitutes a crime, and what constitutes appropriate punishment. At various points in time, it's been legal to own slaves, illegal to be homosexual, and illegal for certain groups to vote (and I consider voting at all to be a subset of this); those laws (and ideas) were eventually updated, largely on moral grounds.

Similarly, most western countries no longer execute criminals.

But those ideas aren't universal, even today. Assuming that they are is imo, extraordinarily foolish, because there's ample evidence to show that they're not.

Who the consensus extends to is really the heart of the matter. Most people agree that killing certain people is wrong, but if they disagree on who those certain people are, there's no useful commonality. Some people believe that all killing of others is wrong, some believe that it's okay to kill others under certain circumstances (which can range from a threat to one's life, to petty "dishonor"), and some people historically believed that killing was a necessary duty (e.g. human sacrifice). There is no useful commonality to be had there. You can't appeal to the humanity of someone who doesn't see you as being the same as them, and the restrictions for inclusion as one of them can be arbitrary, broad, and fickle.

By definition, variation is the opposite of consensus.

The law isn't and never has been a moral benchmark. At best, a law reflects social consensus at the time of its enactment, but even that's not a given.

And proportionality matters. Community service and counseling, loss of time (imprisonment), and loss of a hand have all been used to punish theft. Which is correct? Which is the more moral, because it doesn't seem to me that they're the same?

Hogwash. We don't even have a consistent set of ethics on how to treat animals today, in the age of the Internet. We have people who refuse to eat meat because of how the animals are treated, and we have places where the locals make sport out of torturing animals to death, and indeed, our own recent history includes that as well (look up badgering).

Not to mention, the variation in treatment of homosexuals (including what constitutes homosexuality), people of different skin colors, handedness, how rape is treated in various cultures (often tied in with notions of property and ownership of all things), or even the various definitions of rape... The list goes on.

Maybe there's a core there in terms of how we treat a given in-group, but equally important is who belongs in that group and why, and that part seems to be tremendously flexible. Ditto for what constitutes acceptable treatment of outsiders.

My bad. I skimmed the early part of the video with the sound down on the train this morning and thought I saw a servo in there. That's clever, but it doesn't really change much. There's not really any advantage in terms of payload or performance over existing designs.

This design still requires an additional servo for rotor actuation, so the minimum number of actuators is the same as for a fixed wing (roll and yaw are coupled, so the bare minimum is a single rudder servo).

A fixed wing drone only needs one motor, the control is easier, and the physics are more favorable for carrying high ordnance. I'm skeptical that this is going to change much.

It's cool in that it can hover and do quadcopter type flight with only a single motor, and it's that second part that is interesting, because a fixed wing can also do a prop hang, but it can't do quadrotor-like flight from there.

It's usefulness is likely to be in other areas -- mainly applications where that flight mode is important.

A good engineering course would cover most (if not all) of the skills necessary to do this. This is notable because the person is young and wielding a broad slice of the discipline with maturity.

What exactly would you want to regulate? And keep in mind that regulations are very effective at protecting incumbents, which, as of right now, also includes openAI.

This is a track car for the uber wealthy. It's only rarely going to be on public roads, and it'll probably have some unlock mechanism for the truly stupid stuff. Honestly, I'm here for it. Automakers have gotten complacent. Between Chinese automakers and stunts like this, we might see the return of outrageous engineering just to prove a point.

I'm not even an EV guy, and this is the coolest thing to come out of the automotive space in years. This is almost a throwback to the homologation days of rallying, when automotive companies sold unchained racing cars to the general public because the FIA and WRC deemed it so. Impractical cars in every sense of the word, but phenomenal engineering.

It should also be said that "can't differentiate a function" is a pretty high bar for Monte Carlo methods, which will still give you a gradient for un-differentiable functions.

You'll need to be a bit more specific in your claims here, because I'm not following. What is being centralized? Because the last 40 years has been about decentralizing supply chains and manufacturing. What political movement are you referring to, and how are large corporations and the wealthy benefiting?

Unless you're testing your knowledge with practice (that the LLM isn't doing for you), you probably aren't actually learning. Doing is learning.

It's both. Governments correctly realized that you don't outsource technologies that are critical to your defense toolchain, which is why Boeing still makes planes in Everett and not in China. The oversight was not realizing the extent to which all technologies feed into one another. Eventually, you're trying to get electromagnetic launchers working on your aircraft carriers, and you're discovering that you don't have that capability, because administrations 30 years ago let electronics manufacturing go to China. So not only do you not have the capability of doing the thing that you wanted to do, but your adversary has reached a level of electronics manufacturing that your country never achieved.

It's treason if you or I leak secrets to China, and I'd argue that it would also be treason if Lockheed or Boeing wanted to make jets vital to US military interests in China. But if you sell public assets that are subsequently bought by the Chinese, or you move manufacturing that feeds the logistical supply chain, and subsequently erode local capability, that's apparently fine.

The market will always seek to maximize profits, but it's on the government to actually bat for its own sovereign interests. It's that second part that has been lacking, hence why I said in my previous post that this stuff was given away. It didn't need to be stolen.

LLMs do qualify though, because really they're graph neural nets applied to language. The advantage they had over RNNs was that they could leverage parallel compute and scale to colossal datasets in a way that RNNs just never will.

This battle was lost 20 years ago, and to reverse it you would need to go back 40 years to prevent US industry from outsourcing to China after it joined the WEF.

You can't divorce engineering from manufacturing, because designing better stuff heavily favors those who also make the stuff. The US was convinced otherwise by economists who don't believe in nation-states, because shareholders correctly saw that they could make massive profits by paying Chinese wages rather than American wages. But now China builds more ships in a year than the US does in a decade, and all of a sudden, the chickens have come home to roost. It will take generations for the US (and indeed the West in general) to regain what was given away, if it's even possible.

I don't see anything stopping the models from getting to that point, though. So far we've gone from the models not being able to do math at all, to improving on progressively more difficult problems, to claims of "it's just a counterexample", to now producing constructive proofs and new mathematics on the hardest problems we currently have. All within less than two years.

Being able to do a task well and explain what you're doing in an organized way are fairly orthogonal, even in humans. I don't see why the skills the models currently have wouldn't transfer, even if the results look psychedelic. The next step would obviously be to improve trust by improving the way in which results are communicated.

People are already finding stuff in the release to get excited about, and as the models get better at distilling proofs to make them more coherent, this will only amplify. Of all the things to worry about, human curiosity and the ability to run with new ideas probably aren't at stake.

In fact, I can't remember a time when I was more excited about the future of science. This could herald an end to the replication crisis, and kill off bullshit science completely. The danger of course is that we end up with two companies effectively dominating cutting edge research in every field, but it remains to be seen if that's even possible given the pace of improvement in open weight models.

Because research mathematics is a subset of all quantitative work that gets done, but it's by far the most technically difficult subset. If the models can handle research grade math and produce ironclad proofs, they can probably handle the quantitative side of just about any discipline in a trustworthy fashion. Think about how many dinky spreadsheets have gone on to become critical tooling for large organizations. Even if you consider that many disciplines hide technically demanding work behind tooling (e.g. essentially no one is writing a stiffness matrix FE routine by hand), this model would be capable of writing a direct competitor from scratch to produce the same result.

All of STEM relies on mathematical analysis, and new models are now superhuman at that. And yeah, I'd go a step further and say that the reasoning and creativity required to solve cutting edge math problems probably does translate to other tasks like interpretation of the law, or medical diagnosis, or accounting, etc., for the same reasons that I think most top tier mathematicians would excel at those tasks were they so inclined.

They're burying any doubt that the models are capable of superhuman performance on intellectual tasks. Neural nets aren't calculators, and were notably poor at mathematical reasoning tasks for a long time. Now they're not, and the labs are proving that by chewing through what would ordinarily be decades of progress in a month. And the reason to go for math in particular is because there's no wiggle room. You can't just dismiss it as hallucination.

If the models can do this, they're almost certainly good at just about everything, because the reasoning and creativity required to solve these problems will translate. And even if they were only good at this stuff, that's still a tremendously valuable thing, because quantitative reasoning and analysis is the bedrock for many, many industries.

oAI is gunning for the largest IPO in history at this point, and they might actually get there.

Tire performance is the limit; the mgu-k can in theory brake the crankshaft as hard as the tires and power electronics can handle. Lighter is always better if everything else is equal, but that's not really the case with hybrids.

It also depends on what your point of comparison is. Current vehicles are able to go faster with a V6 than the old V10s could, and that compactness is saving weight and adding performance elsewhere, so it's not as straightforward as modern F1 cars being too heavy. Modern cars also have a much bigger focus on safety than the old cars did, which, again, adds mass.

Regeneration happens directly on the crankshaft via the mgu-k, not the wheels. The mgu is essentially a governor, so you can set it to add or sink (absorb) torque however you like. If you're adding torque, you're draining the battery, and if you're sinking torque, you're charging the battery. Because everything is mechanically connected, sinking torque also wants to brake the car (i.e. regenerative braking).

In terms of power balance, on average, the draw needs to match generation for the system to work. Since this a race, the battery should be flat when you cross the finish line, because you don't want to leave power on the table. When cars are clipping out on the straights, they're trying to claw back some power for the battery so they can deploy it at a more impactful moment in the race. Maybe it's frustrating for viewers, but part of that is that the vehicle has a maximum sustained power, and a maximum total power, and those aren't the same thing.

The mgu-h was cool, but I believe the teams when they say that it was too difficult and too expensive to get right.

There's no reason the battery should run out on the straight, though. You can charge at any point while driving, not just during braking. Obviously you're taking torque from the engine to do so, but that's a strategic consideration.

It's not just about being environmentally friendly; hybrid engines have fairly significant performance advantages over non-hybrid engines.

F1 couldn't call themselves the pinnacle of motorsports if they moved away from hybrid engines. The performance advantages are fairly staggering.

This was the video that earned my sub. Even though it covers stuff I mostly already knew, it's done in such a way that that it breaks down the whys that may not be apparent to people without a background in this stuff.

This and the video on hybrids, are, imo important works, because they break things down in a way that disarms tribalism, and explain how it is that we got to where we are. There are very real stakes in ownership, right to repair, and freedom of movement that rest on people having a good baseline understanding of this stuff.