david-gpu

The OP does not appear to be a native English speaker. And, for example, languages like Spanish don't clearly differentiate between "last" and "latest".

We don't have very many coopers left, either. Sometimes technological progress makes some occupations obsolete.

I don't think software engineering will disappear, but it could change a lot, potentially. More understanding the needs of the end consumer, more writing specifications, less raw coding and debugging. Maybe fewer programmers will be needed, and that would not be the end of the world.

Because only people who work for Anthropic use AI tools? What is the reasoning here? The guy is a mathematician, after all.

I am just saying that people have been making these sorts of shallow dismissive comments about people who use different tooling since forever. I remember a college professor snided at students who didn't know how to use vi.

In the same way that most people are no longer proficient in assembly, but it has not spelled doom for software engineering, some folks are now using AI tools, and it will probably be fine. The day to day work flow of a typical programmer will probably be different ten or twenty years from now, and we will all move on, as usual.

The existence of a "do anything" IDE causes humans to forgo learning the multitude of tools we already have. I suspect there will be no next generation of software engineers.

People said stuff like that in the 90s. I was there.

Indeed, the incentive is the opposite: hiding the fact that they used an AI would boost their own personal brand.

The actual quote is (=emphasis= mine) :

they haven't made new IMAX =film= projectors in about 50 years

Digital projectors have been the norm for a while now.

Can we at least agree that it does not appear in ABOK when it was published in the 40s?

You would be surprised. New useful knots are still being found. E.g. The Zeppelin bend is exceptionally good and was invented in the 60s. The icicle hitch is from the 90s. Some fishing knots are less than 20 years old.

Then you don't. But don't expect people who live in urban centers to cater to your needs when you don't live there.

Tell me about it! It is endless!

No one here actually cares about folding laundry. I can demonstrate it by pointing out at the absence of posts on that subject.

...but when an affordable robot that folds laundry becomes available, people here pay attention.

You asked for counterexamples, so I gave you a few. Did I misinterpret your question?

They absolutely still exist

That is precisely the point. They still exist, but it is a far less common occupation than it once was.

You are assuming that the amount of work is finite, so more productive people implies fewer people are required. Is that the case though? Has it ever been?

How many horse farriers have you met? How many coopers, blacksmiths, or shoemakers?

All these apps rely on the data provided by OpenStreetMap (OSM). It is a Wikipedia-like project where anybody can contribute edits.

https://www.openstreetmap.org/

Thank you. I like the comparison of "meditation" with "sport": it is not all the same, even if there are commonalities between some disciplines.

It is rare to see laypeople discuss some of the different types and which one may be best suited for a particular goal.

If the goal is simply relieving stress, performing some sport outdoors —especially team sports— is probably more effective than any meditation, for most people.

The privacy policy indicates that they track you and share your data to ad networks like Meta. Yikes.

I understand the frustration of these unmet expectations. There are good technical reasons why each of these things don't work the way you would like them to. E.g. adding preemption to GPUs is doable, but it is not cheap, and simply killing the task that is hogging the GPU is often the more practical and expeditious way to go.

That is largely what drivers do: work around hardware bugs. It's the industry's dirty secret.

To be fair: the hardware is enormously complex, and the drivers much less so.

How do you determine that the bugs you run into are located in the Nvidia drivers and libraries?

Way back when I wrote the OpenCL driver at Qualcomm, we would frequently get bug reports from customers complaining about our code. During my tenure, every single one of them was root-caused as an application bug. Unsurprisingly, considering that our code was backed by an extensive test suite and their code wasn't.

Not to say that our code was perfect, of course. But people have a tendency to blame GPU drivers when the problem often lies elsewhere.

"I am fine, thank you. How are you?"

Some folks are not going to like what I am about to say, but what I do is write down as much information that I think may be relevant as possible, trying to avoid leading the witness with any of my preconceived ideas of what may be going on. At the end, I encourage them to ask me questions to get a more complete picture of what may be going on.

After a couple of rounds of that, a picture will start to emerge. The AI will make a few XYZ hypotheses of what may be going on, some of which will make more sense to you than others. This is when you can start searching some of those terms in places like pubmed.ncbi.nlm.nih.gov, including for example like diagnostic criteria for XYZ.

One of the ways I often use these AIs, not just in the context of finding possible diagnoses, is requesting them to make the case for and against hypothesis XYZ based on the data you have personally collected. Again, it's not about fully buying every thing that comes out of them, but it can help you consider angles or possibilities that did not occur to you, or that you had previously accepted/discarded without sufficient evidence. Think of them as that quirky acquaintance that knows a little bit about everything but sometimes misremembers, rather than as a god-like oracle.

And don't do all this in a single session/context. Start a new context every now and then, because otherwise it tends to go in circles as these AIs are biased towards agreeing with whatever it is you said most recently. Intentionally challenge yourself, re-evaluate the existing data from other perspectives.

Sometimes what you learn is not pleasant, but as more data becomes available, you learn to accept it. Good luck.

Last week I went to a highly-specialized tertiary clinic about further treatment for a rare medical condition that I was diagnosed and treated for as a child. The two very specialized doctors I met there confirmed a diagnostic mistake that a specialist had made ten years ago. The only reason I pursued a second opinion, ten years later, was because Google Gemini had explained to me that the specialist ten years ago had performed the wrong type of test for my condition.

Do these LLMs make mistakes? They sure do, I see it all the time. But they can also help people make breakthroughs.

And this isn't the only time that Gemini has helped me diagnose long-term health issues, either.

I am not advocating to trust anything they say blindly, but they can be a great place to form new hypotheses and learn the right terms to look for when you are unfamiliar with a subject.

For people who are already in academia and want to be more academic? I don't get it.

This notion that Masters are for academia is so contrary to my experience altogether. They are not even associated with research, in my experience. What I have seen is:

* BSc: General professionals.

* MSc: More specialized professionals.

* PhD: Baseline for research, but more often than not just doing even more specialized work than folks with MSc.

* Postdocs: Actual researchers, including but not limited to academic careers.

What are PhDs for, then?

A Master's degree is hardly academia, is it? A PhD is the starting point for a career in research, and even that does not necessarily imply academia, either.

A Master's thesis is not for somebody aiming for academia. It is a requirement for graduation. Rather different from a PhD.

You don't need billions of parameters for that, precisely because the risk of being stuck at a local minimum decreases exponentially with the number of parameters. Right?

There has been an extension of sexism over the past few decades, where ever increasing swaths of people find it socially acceptable to treat a large cohort of the population as a uniform evil to be mitigated.

And it is not limited to a few loud voices in extremist online communities. You will find it percolating onto real-life policies as well.

We have gotten to the point where trying to address problems that primarily affect certain cohorts has become taboo because it clashes with the commonly-accepted narrative that they are a privileged group, regardless of what any data shows.