> 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?
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.
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.
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.
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.
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.
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.
Congratulations to everyone who celebrates Father's Day.
And for those who had a particularly difficult childhood, for those who may still struggle with the trauma of abuse and neglect, here are some resources you may find useful:
* "Adult children of emotionally immature parents" by Lindsay Gibson.
Isn't that due to a lack of leadership, rather than a problem with teamwork itself? Somebody has to be ultimately in charge and willing to put a stop to endless debate.
Wishing you could block some HN users? (Perhaps me!)
Try Martin Tournoij's script: https://gist.github.com/arp242/3159b9cbbbe148c786e1ca10adaaea61