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The grading rubric for all engineering units here in Australia require that students develop an understanding of ethics and professional practice. There are a number of units dedicated to teaching project work, legal, and business aspects of engineering.

Typically the software engineers also do more maths than CS students. Reasoning from first principles is an important skill for professional engineers to have.

Huh. This reminds me of the asymptotic equipartition theorem. Samples taken from higher and higher dimensional spaces will concentrate into a typical set.

Does model performance also concentrate into a 'typical case' where things work pretty well and a non-typical case where it's completely unpredictable as the number of parameters increase?

That's also plausible.

No. The official report seems to suggest that software in IMS core (the subsystem that controls VOIP signalling in the mobile network) bugged out after the time was shifted back 20 years. That doesn't narrow down the specific failure that stopped calls from going through very much. IMS core is full of timers, but it could also have been a license server issue for all the report said about it.

I think most of these problems could be solved through contracts.

A contract for purchasing Parent Co.'s HR, legal, and compliance expertise for child co.

And a contract for sharing technology infrastructure.

The terms and pricing of the contract would likely be strictly regulated to prevent preferential treatment and encourage a clean separation over time.

I'm not a lawyer though, so who knows.

It's still impressive, but it's not enough.

Australia's bar for skilled employment is pretty low and deflates wages as well. That's before we get into the prevalence of exploiting working holiday visas for practically slave wages.

You can't really force someone to work for X years at a place they don't want to. I don't know if what you're describing is an enforceable contract term.

I mean. That's how profiteering works for the most part. Extracting wealth from people who don't know better is many times easier than creating more wealth for everyone.

Most hardware tinkerers are not EE's. But will rely on EE's to certify that their electronics are safe.

You don't need to be an "Electrical Engineer" to do simple design work like this.

Professional Engineers are qualified to take on more liability than just creating a design. So you don't need an Engineer to design a product, but (depending on jurisdiction) you will need an engineer to certify that your product won't hurt or kill people.

If you do a lot of design work, then you may want to hire a Professional Engineer in-house so that your company has more confidence that they will produce compliant designs with fewer iterations.

Engineers are often the best people to work with if you need to do things that are done infrequently since theoretically, they're trained in the prerequisite first principles so they can make judgments that are rooted in rigorous analysis in addition to their practical experience.

If you can call yourself an Engineer if you feel like it, then it's not a protected title.

"Doctor", is for instance a protected title. You can not advertise yourself or your services as a doctor unless you're a qualified and practicing medical professional. I believe some kinds of legal practices are the same.

I don't think Software Engineers in the US would sign any documents that expose them to legal liability as a professional engineer either.

It's still not clear to me from this discussion whether the term "Software Engineer" is legally protected in Germany (or any other European country) or if it's just a cultural convention.

The distinction is basically, if a software developer started a consultancy developing custom software solutions and called the company XY Software Engineering, would they be penalized for false advertising if they didn't have professional engineering qualifications?

In which countries are Software Engineers not allowed to call themselves engineers unless they are professionally qualified engineers?

I think most people who write code are the latter and not the former. The industry has diluted the term "engineer" so much that they maybe don't even realise that traditional engineering projects are about more than just implementation work.

I think whether "taste" was ever important comes down to whether software is a commodity to be bought and sold, or something more strategic.

The company that needs a webapp because everyone else has one is never going to value "taste".

To adapt to this AI world, we need to either ship more lower-quality software, or start seeking out higher value problems. I don't see any other way around it.

Interesting. Do you have any more specific insights into where you feel AI was a big value-add to these problems? I don't want to be overly dismissive of AI, but I also feel that the AI hype engine frequently positions claims as being 'ground-breaking' when they are really just interesting incremental results.

The general consensus of developers is that AI can only do the work of a strong 'junior'. Yet as soon as we are presented with pure mathematical results, people seem incredibly ready to accept that AI can do more than what a strong student could achieve.

I have no idea how many PhD's have spent how much time of their careers tackling these very specific problems, and I doubt you do either.

I'm trying to understand if these specific problems were the kinds of problems that would have justified an expert investing weeks or months to solve. Or if they were the kinds of problems that would normally have been given to students to investigate.

I assume you're talking about No. 5, the arithmetic circuit complexity bound? The existence of a lower bound than state-of-the-art is certainly a significant result and worth publishing.

But the wording of the result makes it sound like we don't know what the lowest possible complexity bound might be. So, prior to this result did we think there couldn't be a lower possible bound? Or did the arithmetic circuit community think there were lower possible bounds but didn't see it as a high value target for experts to tackle (maybe a problem that was instead regularly given to students to study).

I'm not arguing that this isn't innovative or worthy of publication. Basically any result that moves the needle meets those criteria. I'm interested in how the results that OpenAI has published here differs from finding optimality solutions for incredibly niche optimization problems by throwing the problem in an enormous solver.

Not being an expert in any of the fields OpenAI has "advanced" I don't want to prematurely downplay the significance of this contribution. However, I am worried that the language they are using in this blog post is exaggerating for the sake of marketing.

It is true there hasn't been a reliable computational approach to solving these problems before. But do these proofs contribute new ideas to the mathematical corpus, or are they simply an effective method to exhaustively search the literature for the right combination of existing tools to apply to the problem?

Essentially, did these problems seem like they had an intuitive answer and were feasible to prove before, just not high enough value targets for an expert to invest time into? Or were they fundamentally difficult prior to this point and it appears that AI has done something more than just throw the problem into a big solver.

I don't think the problem is that memory footprint can't be reduced. The problem is that the token generation rate is simply far too low.

OP suggests that the token rate of of their solution is ~5 per second. That's at least an order of magnitude slower than commercially available models.

How will open-source and open-weight models continue to thrive after financial incentives die off? Surely open models will suffer from outdated knowledge cutoffs if noone will pay for model training?

Well said! The lines from this movie are written in a way that make it easy for people to relate to this vague notion that 'experience' is different from 'learning'. But quantitatively speaking it's less clear what the difference really is. After all isn't experience simply another type of learning?

Everyone knows there is bias. The problem this article highlights is that by delegating screening and human judgment to a few AI vendors those vendors will bias all employers in the same way.

It's a side effect of rewarding 20-somethings with lots of money to do 'smart' things with stuff they learn in an undergraduate degree.

It's easy to conflate recognition with achievement when that's all you know in life.

If both systems have a good clock. Then the synchronization messages only need to contain the time delta to correct the time (phase?) drift to achieve full synchronization.

The highest profile recent case that I can find is Rambler vs Igor Sysoev on the development of Nginx.

https://news.ycombinator.com/item?id=21771144

Although in this particular case, I tend to agree with Igor as he was employed as a system administrator not a software developer so it's unlikely that there were any real contractual constraints imposed on him in relation to copyright or invention transfer.

Absolutely! The backlog is enormous though, and much of mathematics requires a great deal of work to understand it to the depth required before a novel application becomes apparent.

The problem with exams is that everyone has a bad experience with a poorly written one. Well-written exams will have questions that test students at different levels of understanding across the whole curriculum.

So a student who only understands the basics should be able to answer most of the easy questions and students who have a deeper understanding can answer the harder ones.

Well-written exams should feel pretty fair and leave students feeling like the result they got is proportional to the effort they put into studying the material (or at least how well they personally felt they understood the material).