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Im on the hiring side, and I can tell you that these questions are there to serve one purpose only - to give the candidate a chance to stand out.

We were getting some 30-40 applications a day when we were at peak search, and when you get so many, after a few weeks, you start looking for anomalies, show me some glint of greatness, a spark of wit, evidence of original thought, something to show youre not just pasting slop or ticking boxes.

The candidate on the other side of this might say "but theres so many applications to do, I cant do that for every one" and maybe thats the crux, the candidate who puts in the extra effort to stand out will win, and thats the purpose of these questions.


If you're serious about building a business for this technology you need to realize that people who are interested in trust, data provenance, digital signatures and verification systems are looking for signals of credibility, trust and reliability - as such hosting this on a domain "lyfe.ninja" and having "by lyfe.ninja" written everywhere sounds like a 14 year old script kiddie created this and regardless of whether that is true or not, destroys any credibility and trust straight away

Its scary because its so amateurish, unprofessional, out of tone with the rest of the site, but still has an underlying ominous tone.

During the normal course of Whitehouse communication I would expect that a review of the visual style, the messaging tone, and the overall professional style of communication would set the bar for the standard that institution represents.

This feels like a dorky intern, prompted an LLM and kept saying "make it scarier" until this garbage showed up, and then it was fast tracked to an 80 year old president who is stretched so thin physically and cognitively that his judgemrnt is impaired to the point where this campy, whacky, garbage seems like a good idea


Its classified, but the second hardest was simulating Dynamic nuclear polarization, ab initio.

Much more likely is that it would hallucinate a plausible sounding but incorrect answer and send intermediate and junior engineers on a wild goose chase

if an LLM is capable enough to be used this way it would be used to generate scenarios for the people who would otherwise have to be the ones to generate them. those people would then evaluate the scnearios. those people would then be in a position to decide if the LLM saves them time.

> a plausible sounding but incorrect answer

That is an incorrect but plausible hypothesis. Do you really think that people can't make such mistakes?

If you want to say that people have understanding, then define understanding in an operationalizable way first.

It doesn't mean that I would recommend a general-purpose AI model without additional training to do a fault analysis.


>That is an incorrect but plausible hypothesis. >Do you really think that people can't make such mistakes?

Where did I say that? You just pulled that out of nowhere and then refuted it - strawman https://en.wikipedia.org/wiki/Straw_man


> it would hallucinate a plausible sounding but incorrect answer

"Hallucinate" as used in this context does not apply to humans and presupposes a qualitative difference.


I was saying that an AI would more likely hallucinate an incorrect answer than correctly diagnose the root cause failure. At no time was I comparing an AI to a human, thats the bit you made up.

The knee-jerk reaction to pointing out any failure modes of AI with, "but meatbags bad!" is a tiring strawman to deal with. It immediately turns the discussion into something else.

Humans crash millions of cars a year, and you're worried about one dog driver running over four measly people?

So, your message is "Unspecified AI models with or without additional training aren't ready to do aerospace fault analysis and they can lead experienced engineers astray." OK, it might or mightn't be true depending on the free parameters in your statement.

I used the word "likely" meaning there is a chance, your re-phrasing of what I said into a certainty ... and then refuting that certainty, is another textbook strawman argument, you made the same logical fallacy again.

Also I said "intermediate and junior" engineers - meaning INexperienced engineers, not experienced ones, so you quoted me wrong in that part too.


Rsync for my mum, rsync for my sister, rsync for my lawyer, my teacher, my plumber, rsync for people who just want a folder with an icon that says it's working.

Rsync for people who just want a folder and it automatically shows up on all of their computers and phones at once. Rsync for people who are never going to use rsync because an ancient command line application with a zillion flags on it is just about as user hostile as you can get.

Is there a bunch of tech geeks who can rsync themselves? Of course there are, and this product absolutely, categorically and unambiguously isnt for them. But guess what? There's 100,000,000 people who both want their folders synced and have no idea how to use rsync (and dont want to). Thats what they are building, and that's who its for, and thats why the have 2.5 billion in annual revenue. Because they famously ignored the "iTs jUsT rSYnC" crowd, and built a product that actually works for 100 million people


Is that actually true in the real world? Or is that some comp sci algorithm dream? I suspect it might be an engineers fallacy where the romantic desire to reduce everything to an algorithm or scalar value that can then be maximized or minimized blinds the engineer to the reality of the situation - the businesses doing route planning already have something thats close enough to optimal so that if the travelling salesman problem was solved, it wouldn't make a material difference to the business.

The algorithm engineer is so in love with the idea that an algorithm is the solution to everyone's problem (its a natural human bias to think the world desires what we have) that they way overweigh the importance of route planning improvements which are incremental or worse - would be thrown away because the practicalities of implementation doesn't warrant the marginal improvements.


Absolutely true in the real world; I was part of a real team that explored quantum optimization algorithms as part of a strategic initiative (my day job is algorithmic optimization on classical computers).

Our problem is similar (but not identical) to the traveling salesman problem. We run on a tight time constraint (measured in days for the complex type and measured in minutes for the simple type).

We're running approximations on classic computers but estimate that we'd save billions if we could reach optimum.


> We're running approximations on classic computers but estimate that we'd save billions if we could reach optimum.

Wont some Monte Carlo sim get you quite close?


We are as close as you can get, but we estimate that we're about 3.5% away from true optimum on the hardest problem we try to optimize.

AI is a golem

Random sampling! Known by computer scientists everywhere to be the worst search strategy


The challenge with modelling HEAs is that they have very complex electronic structures, its very tempting for a newbie to throw an MLIP at the problem but in reality you have many complicated bonding arrangements that are not captured by these models, this is also compounded by the fact that you dont just have a slab with a bunch of itinerant electrons but you end up with covalent and even ionic-like bondings forming in the SRO substructures. Then theres spin treatment (which matters a lot), and also because the configuration space is combitatorially large you also have to do some high throughout studies with statistical interpretation since by definition theres no such thing as a representative unit cell in an HEA

How do I know? We have invented multiple via simulation and have them in the lab for synthesis now!


Fascinating! Where is this written up?


This is knowledge I have gained through experience building my company to study these things over the last couple of years. Theres a lot LOT more to this, if youre interested I would recommend reading all the papers you can find on HEA. Getting a subscription to Advanced Materials from Wiley, and then trying to simulate some of the materials yourself. Don't start with HEAs they are hard and you need a lot of computing power, start with simple systems like bulk copper, aluminum and iron. Then move to binary systems, ternary systems and increase the complexity of what youre modelling while always checking against experimental data. Learn about all of the shortcomings of the simulations and then ask yourself "ok how can I improve that", while you're learning this you're developing an intuition for all of the settings in the simulations (whether atomistic. Meso-scale, macroscale or other)

I have a 15 GPU cluster in my house just so I can study HEAs - but I understand thats out of budget for a hobbiest so that's why I recommend you start with simpler systems and slowly increase complexity.

You might see various datasets for HEA, HEA property prediction, and synthesis predictors, but cold hard truth of the matter is that the quantum interactions at the interatomic level are so complex, the configuration space youre searching is so massive, that no dataset is going to make a dent in it, so models are only really useful as VERY VERY VERY approximate screening tools (sometimes) - and thats not even talking about micro-scale phenomena and macroscale phenomena - which are enormous subjects on their own and just as important!

You must simulate all of these, you can't just do a Microsoft Mattergen that spits out an idealized crystal structure at 0 Kelvin, because in the real world, thats barely the first step.


Seriously!


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