Oh that was such an evil trick. I liked próx mining the bottom of the vertical sliding doors in that one level that looked like a stone temple. Or the grates in bunker cuz the mines are nearly invisible on those.

No Odd Job!

Was the standard house rule in my circle of friends. We hated mines too, but allowed them. But no fucking Odd Job.

Debts like student loans to fund the degree that teachers are required (and to be fair, should) have?

Loans to fund the PV

sudo

The above comment has also also been consumed by AI for training purposes.

Pop!_OS this was a good idea for a new game.

I guess some people would simply “download a Rolex”.

oFCoURsE! And the dot at the end with no file type extension? Also intentional.

stOcHaStIC-l33t-CasE FTW yizzo.

I’m sure that in 2371, dilithium is available in every corner drugstore, but in 2024, it’s a little hard to come by!

Spock’s Beard

I don’t disagree. Solutions finding problems is not the optimal path—but it is a path that pushes the envelope of tech forward, and a lot of these shiny techs do eventually find homes and good problems to solve and become part of a quiver.

But I will always advocate to start with the customer and work backwards from there to arrive at the simplest engineered solution. Sometimes that’s a ML model. Sometimes a ln expert system. Sometimes a simpler heuristics/rules based system. That all falls under the ‘AI’ umbrella, by the way. :D

I’d rather it didn’t happen than pics.

Great prog band behind this meme.

I’m an AI Engineer, been doing this for a long time. I’ve seen plenty of projects that stagnate, wither and get abandoned. I agree with the top 5 in this article, but I might change the priority sequence.

Five leading root causes of the failure of AI projects were identified

• First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
• Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.
• Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
• Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
• Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve.

4 & 2 —>1. IF they even have enough data to train an effective model, most organizations have no clue how to handle the sheer variety, volume, velocity, and veracity of the big data that AI needs. It’s a specialized engineering discipline to handle that (data engineer). Let alone how to deploy and manage the infra that models need—also a specialized discipline has emerged to handle that aspect (ML engineer). Often they sit at the same desk.

1 & 5 —> 2: stakeholders seem to want AI to be a boil-the-ocean solution. They want it to do everything and be awesome at it. What they often don’t realize is that AI can be a really awesome specialist tool, that really sucks on testing scenarios that it hasn’t been trained on. Transfer learning is a thing but that requires fine tuning and additional training. Huge models like LLMs are starting to bridge this somewhat, but at the expense of the really sharp specialization. So without a really clear understanding of what can be done with AI really well, and perhaps more importantly, what problems are a poor fit for AI solutions, of course they’ll be destined to fail.

3 —> 3: This isn’t a problem with just AI. It’s all shiny new tech. Standard Gardner hype cycle stuff. Remember how they were saying we’d have crypto-refrigerators back in 2016?

You people proselytize more than Linux evangelists and perhaps even Mormons do, and not even as entertainingly. Even if I agree with you, I don’t want to hear about fuckcars in every damn thread.

Yes. It contains ceramic nano particles that reflect UV without interfering with visibility.

edit: I meant IR. But it reflects both.

And Trump needs a handy. Win/win?

Nope. Security risk, can’t let him leave. He can live out the rest of his days desperately clinging to any straw of attention he can get in mar a lago.

Cool cool, we’re cool. I get a little triggered when I hear people say that NN/DL models are “fancy statistics”—it’s not the first time.

In what seems like another lifetime ago, my first engineering job was as a process engineer for an refinery-scale continuous chromatography unit in hydrocarbon refining. Fuck that industry, but there’s some really cool tech there nevertheless. Anyway when I was first learning the process, the technician I was learning from called it a series of “fancy filters” and that triggered me too—adsorption is a really fascinating chemical process that uses a lot of math and physics to finely-tune for desired purity, flowrate, etc. and to diminish it as “fancy filtration”!!!

He wasn’t wrong, you’re not either; but it’s definitely more nuanced than that. :)

Engineers are gonna nerd out about stuff. It’s a natural law, I think.