throwawayjava

My reasons are right there in the original post: "bathroom trips... bad breath... stain your teeth". Those things become more obnoxious as I aged.

Throw away java ;-)

IME: technically-trained PMs are worth their weight in gold. PMs without a technical background are extremely dangerous liabilities.

Context: Mostly b2b products where careful judgement on technical feasibility is the difference between "useless" and "game-changer".

> Engineers who understand anything to business and product management are extremely rare. And people who are good at it and genuinely interested in it usually move to this role

It depends. Only if they don't take a huge paycut by moving into the PM role, and only if the org is willing to let them move into the PM role.

Many business are not willing to lose highly productive engineers with niche skillsets and are not willing to pay PMs as much as they pay their senior engineers.

IME, senior engineer -> executive/founder is a lot more common than senior engineer -> PM. But it's not because engineers can't be product people... sorta the opposite.

> a lot of PMs are actually just projects managers instead of being product managers.

No true Scotsman, right?

Unfortuantely, many companies/projects/teams don't budget properly for the PM role and you end up with MBAs or other non-technical business folk, who are often 1/3 to 1/2 the price of good technical PMs.

There's a significant difference between not having nepotism and eliminating family bonds.

> We are tribal and protect our families.

Many of virtuous_signal's arguments boiled down to the observation that protecting your family via nepotism or dynastic preference isn't even necessarily good for the effected family member in the long run. I think that poster makes a good argument that dynastic preference in university admissions is a net negative for its beneficiaries.

The negatives of nepotism also show up in business, where, with rare exception, nepotism tends to genereate a huge drain on both productivity and external respect.

This is exactly how once-great tech companies die. This is how governments lose the faith of their citizenry.

Selling all your stock comp immediately is more about diversification of risk.

If rsus are a nontrivial aspect of your comp, then you're already extremely exposed to risk in your company's stock price stock price (if it tanks take a big pay cut and they might even lose their job).

Or rather, Who knows? Maybe. But certainly, at least today, a SoTA model generating a quality encyclopedia certainly is not doing what human writers do, and is certainly effectively copy/pasting.

Maybe in 50 years -- or 10 years with a major breakthrough on the level of general relativity -- that statement might be true. but it's certainly not true of today's deep NLP systems.

In the US, this question has been settled since at least the 1990s (e.g., in the context of videogames). The output of algorithms is, in general, copyrightable, although there are some rather common-sense exceptions.

The question isn't whether you can copyright the output of an algorithm. The more salient question, in my mind, is whether the output of ML algorithms belongs to the owner's algorithm or to the owner of the training set.

One possible legal theory: because the algorithm was trained on a text corpus upon which the algorithm's owner has no legal claim.

In this particular case, I don't think that theory would hold much water.

However, consider, e.g., a model that produces encyclopedia entries and is trained on a half dozen existing encyclopedias. IMO, if that model is using techniques similar to SoTA and isn't producing utter garbage, then the owner of that model should have a very difficult time claiming that the output of their model is anything more than a sophisticated round-about way of copy/pasting from existing encyclopedias.

But still, in that case, the output is still covered by copyright. It's just that the owner of the training set -- not the owner of the algorithm -- is the one with the valid claim to copyright.

I could pretty comfortably raise a family of 6 on $100K in a midwestern city, or a family of 6 on $80K in the rural midwest/south. Without sufficient retirement savings, mind you, and my safety net would be non-exisent. I'd need at least another $30K-$50K on those numbers to build a strong safety net (remember, it's a safety net for 2 parents + 4 kids, not for one person...).

Doing the same in SFBA would probably require at least $250K. Maybe more.

> Surely you don't believe less than 1% of people in the US can live comfortably and have a safety net, do you?

The number is larger than 1%, but probably still smaller than you think.

The average American definitely doesn't have a sufficient safety net. IME, the average American raising more than 1 or 2 kids almost certainly doesn't have a sufficient safety net.

So, no, you don't need to be in the top 1% of earners in the US to have a safety net. But if you have a large family, you probably have to be in the top 10% to live comfortably and have a safety net and retirement.

Neither is wrong, but insisting that a naming clash carries any substantive significance on an underlying issue is just silly. Similarly, insisting that nonmathematicians should stop using a certain word unless they use it how mathematicians use it is a tad ridiculous.

Of anything, it's more reasonable for mathematicians to change their language. After all, their intended meaning is far less commonly understood.

I'm also pretty wary of interpretability/explainability research in AI. Work on robustness and safety tends to be a bit better (those communities at least mathematically characterize their goals and contributions, and propose reasonable benchmarks).

But I'm also skeptical of a lot of modern deep learning research in general.

In particular, your critique goes both directions.

If I had a penny for every dissertation in the past few years that boiled down to "I built an absurdly over-fit/wrongly-fit model in domain D and claimed it beats SoTA in that domain. Unfortunately, I never took a course about D and ignored or wildly misused that domain's competitions/benchmarks. No one in that community took my amazing work seriously, so I submitted to NeurIPS/AAAI/ICML/IJCAI/... instead. On the Nth resubmission I got some reviewers who don't know anything about D but lose their minds over anything with the word deep (conv, residual, variational, adversarial, ... depending on the year) in the title. So, now I have a PhD in 'AI for D' but everyone doing research in D rolls their eyes at my work."

> Those same people will likely at some point call for a strict regulation of AI...

The most effectual calls for regulation of the software industry will not come from technologists. The call will come from politicians in the vein of, e.g., Josh Hawley or Elizabeth Warren. Those politicians have very specific goals and motivations which do not align with those of researchers doing interpretability/explainability research. If the tech industry is regulated, it's extremely unlikely that those regulations will be based upon proposals from STEM PhDs. At least in the USA.

> faking results of their interpretable models

Jumping from "this work is probably not valuable" to "this entire research community are a bunch of fraudsters" is a pretty big jump. Do you have any evidence of this happening?

That's a very dynamicist viewpoint. I don't necessarily disagree.

However, in what sense to the prototypical deep learning models predict the data generating process?

I tend to agree that a lot of work with "interpretable" in the title is horseshit and misses the forest for the trees.

I need people who I can trust to given presentations to clients, executives, or directors of other divisions of the business.

I need people who can communicate clearly in writing with clients/stakeholders.

I need people who can write good commit logs, comments, and technical documentation for other engineers.

In the extremely limit, I need people who can write a white paper or research paper.

I'm not looking for the next Dickens or Fitzgerald.

I agree with this assessment. Even some top-tier CS programs seem to routinely graduate students with poor writing skills and sub-par cultural awareness.

I always recommend to interns that they take some writing-intensive courses, and possibly even pick up a minor in a writing-intensive field like English or Philosophy. And then not just take the course, but also use what they learned to write about things free time they way they (should be) writing code outside of their CS courses.

I think a lot of the strong disagreement in HN re: the value of college basically boils down to the huge amount of variety in the college experience.

Surprisingly, this variety has less to do with the quality of the institution and more to do with the quality/preparedness of the student.

A good litmus test for whether someone is wasting college might be to ask the question: what did you read/build/do this semester outside of your courses that used stuff you learned in your curses during the last year or so?

The less impressive the answer to that question, the more likely it is the student should maybe think about leaving college until they're ready to fully engage.

(Obviously, that litmus test only makes sense for full-time undergraduate students... for working students, it might be something more like "how are you using what you're learning in your day job".)

Can anyone familiar with CC processing provide insight on whether that's a reasonable explanation?

Regardless, a problem that requires a "software fix" from the vendor and manual visitations to each individual machine doesn't sound like a mere "setting"

There is a lot of value in building concise and easy-to-understand explanations of extremely complex phenomenon. Be careful not to throw out Occam's razor with the bathwater.

I tend to agree that the pure mathematics and theoretical physics communities get obsessive. The hero-worship of theory builders in those sciences compounds matters. However, pure math and theoretic physics are the worst offenders by far in the natural science. Theorists in both fields are typically a small minority even within their own departments. The other natural sciences and the engineering disciplines are much less infected.

Some of the over-obsession with beauty in mathematics has its roots in the Church's heavy patronage of mathematics and natural philosophy, as well as religion's overall grip on nearly every intellectual mind prior to the 20th century.

An actuary's kid will have an easier time becoming an actuary. Same for lawyers and doctors. That doesn't mean that college is a non causal factor in those students lifetime earnings... Quite the opposite. You're not becoming any of those things without college.

The student went to college precisely because they understand viscerally and precisely how college sets them up for a life of higher than average earnings. And what they need to do at college to tap into that potential.

Similarly, I'm not sure I believe the claim that there's no obvious causal link between education and financial acumen.

The big difference seems to be "do you know WHY you are at college?" And in cases where the answer is yes, it's still a good choice.

I've done a lot of programming where my productivity was input-constrained, so I know that that such jobs do exist.

I much prefer jobs where my productivity is idea-constrained. The final code tends to be a lot more interesting, the process of creating it a lot more joyful, and the final software artifact a lot more useful.

As a bonus, I tend to be paid at least an order of magnitude more for idea-constrained code.

...depending on both the student and the career.

Many tech jobs require not much more than literacy, 6th grade algebra, and a tiny bit of grit. Most smart middle schoolers could make mid five figures slinging PHP part time. I did in early high school, and I'm not particularly smart or hard working.

But there are many jobs, even in relatively easy fields like programming, where nonnegative productivity requires years of practice.

And this is sensible. How do you compensate a family for a dead body? You can't. So the purpose of the law should be to prevent the need for that compensation in the first place.

I understand your sentiment, but the reality of just shedding a large fraction of those jobs is hard to even imagine. Unemployment in 2008-2009 peaked at around 10%.