So when are they taking down celebjihad?

sudo

This is a masterpiece. You should publish.

I got lost on the way to grad school. Now I’m stuck in the terminal emulator.

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

Stupid results are better than no results.

But when you’re gifted, talented, and a perfectionist: your “no results” are fucken perfect.

Therefore, stupidity is better than antistupidity. QED.

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

stOcHaStIC-l33t-CasE FTW yizzo.

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?

No, this is incompetent management.

Senior engineers write enabling code/scaffolding, and review code, and mentor juniors. They also write feature code.

Lead engineers code and lead dev teams.

Principal engineers code, and talk about tech in meetings.

Senior Principal engineers, and distinguished technologists/fellows talk about tech, and maybe sometimes code.

Good managers go to meetings and shield the engineers from the stream of exec corporate bs. Infrequently they may rope any of the engineers in this chain in to explain the decisions that the engineers make along the way.

Bad managers bring engineers in to these meetings frequently.

Terrible managers make the engineering decisions and push those to the engineers.