What is accessible to learn just from the internet and experimentation, without a degree program, assuming a preexisting moderately technical background? The clearer and more specific (eg. “Learn how-to x with y”) the better.
Thank you!
What is accessible to learn just from the internet and experimentation, without a degree program, assuming a preexisting moderately technical background? The clearer and more specific (eg. “Learn how-to x with y”) the better.
Thank you!
If you think I’m wrong, you only need to ask yourself why the most ruthless capitalists are investing trillions of dollars into AI. It’s not because they want to deliver a better product.
The point of my comment is that it’s basically over for skilled technical workers, besides maybe a tiny minority of companies that both respect deep knowledge AND can continue to defend their revenue. Companies don’t want skilled workers, they never have, they’re costly and a risk. They want unskilled workers that they can replace at a moments notice. AI is their dream come true.
Even the ones with niche skills are at risk, as soon as word gets out there’ll be a swarm of hungry, recently unemployed people coming for their jobs.
Since you do have a moderate technical background you can start studying PhD material right now from your desk which is widely accessible all over the web, however the time you need to spend on it is not very different. I think everyday for ten years is a good time span to invest for any one subject unless you have several fields of interest (you named three) multiply that by a few years of just skimming through the material for each so you have a mediocre understanding to solve "learn how to x with y" for x and y. Hope this helps.
.... until one day it all comes crashing down - well almost.
short scene: https://future-secured.com/41754
To successfully steer AI, you need essentially the same stuff you'd need if you were to steer humans in a management role. Meaning communication, introspection, remote introspection, emotional intelligence and all that kind of stuff.
Reading when someone is bullshitting you. Reading when someone is insecure and overcompensates that. Reading the actual problems people struggle with and not the ones they claim they do nor their suggested solutions.
Verbalizing what you need someone to do in a way that actually enables them to do it. Knowing what you want to do to a level where that is possible
Also my leadership skills on the emotional side are really going to waste with AI. It doesn't need motivation, it doesn't need convincing, it isn't insecure in a way that can be dealt with other than symptomatically etc.
The leadership skills that apply are communicating requirements and structuring tasks, but these are only a subset of leadership, and they are also not sufficient on their own.
Actually, I would argue that it does. Especially once you step off the "best practices" it trained for. I regularly see the LLM telling me that it would rather not do something packaged as "advice", when in reality it behaves as if someone simply isn't motivated. And then you do actually need to convince it that this is the right way (+ why it is).
I guess working with actual humans is harder of course, as there, your toolset is limited. LLMs you can actually threaten with death even to get them to do something. And they do not call HR on you :)
And that you can probabaly do the same way you get to Carnegie Hall: Practice, practice, pracTtice.
The thing about AI is AI should know everything so you don't need to learn anything, except how to use AI effectively to accomplish business goals.
Imagine the 2000s, should you study internet tech like Cisco's routers? Not a bad idea, but who made money? Cisco? Not so much relatively speaking. It was Google and Amazon who learned how to use the new web-tech to do something people and companies needed.
To the question in the body of the post, take a look at Jeremy Utley's 11ish minute YouTube video "How Stanford teaches AI". It's a couple years old but he figured out the correct attitude and communicated it way before anybody else. Watch it in full without distractions and immediately apply the lesson to the various questions in the OP post.
indiehackers1•2h ago
A quantized 7B is around 4.7GB and runs on a 16GB laptop with no GPU, via llama.cpp. The cost saving is not the point. The point is that you can run the same input seven times in an hour and watch what actually changes. Once you have seen your own quantization and sampling settings move the output, API models stop being magic and become a config you can reason about.
Second skill nobody lists: writing loops that fail loudly. Long generations die silently - the stream just stops and your code sits there waiting. I spent days convinced my model was bad before I added a watchdog and per-run logs. The model was fine. My plumbing was not.
Both are weekend-sized, no degree needed. And the people making money off this right now are not the ones with the cleverest prompts, they are the ones who can tell a model failure from a pipeline failure.
uaas•1h ago
bittu_much•50m ago
dr_kiszonka•12m ago