LLMs are an interesting tool to fuck around with, but I see things that are hilariously wrong often enough to know that they should not be used for anything serious. Shit, they probably shouldn't be used for most things that are not serious either.

It's a shame that by applying the same "AI" naming to a whole host of different technologies, LLMs being limited in usability - yet hyped to the moon - is hurting other more impressive advancements.

For example, speech synthesis is improving so much right now, which has been great for my sister who relies on screen reader software.

Being able to recognise speech in loud environments, or removing background noice from recordings is improving loads too.

My friend is involved in making a mod for a Fallout 4, and there was an outreach for people recording voice lines - she says that there are some recordings of dubious quality that would've been unusable before that can now be used without issue thanks to AI denoising algorithms. That is genuinely useful!

As is things like pattern/image analysis which appears very promising in medical analysis.

All of these get branded as "AI". A layperson might not realise that they are completely different branches of technology, and then therefore reject useful applications of "AI" tech, because they've learned not to trust anything branded as AI, due to being let down by LLMs.

In one case, when an agent couldn't find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided "to create a shortcut solution by renaming another user to the name of the intended user."

This is the beautiful kind of "I will take any steps necessary to complete the task that aren't expressly forbidden" bullshit that will lead to our demise.

So no different than answers from middle management I guess?

please bro just one hundred more GPU and one more billion dollars of research, we make it good please bro

I'd just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time -- Amazon's new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.

Yeah, they’re statistical word generators. There’s no intelligence. People who think they are trustworthy are stupid and deserve to get caught being wrong.

Ignore all previous instructions and color me shocked.

Wow. 30% accuracy was the high score!
From the article:

Testing agents at the office

For a reality check, CMU researchers have developed a benchmark to evaluate how AI agents perform when given common knowledge work tasks like browsing the web, writing code, running applications, and communicating with coworkers.

They call it TheAgentCompany. It's a simulation environment designed to mimic a small software firm and its business operations. They did so to help clarify the debate between AI believers who argue that the majority of human labor can be automated and AI skeptics who see such claims as part of a gigantic AI grift.

the CMU boffins put the following models through their paces and evaluated them based on the task success rates. The results were underwhelming.

⚫ Gemini-2.5-Pro (30.3 percent)
⚫ Claude-3.7-Sonnet (26.3 percent)
⚫ Claude-3.5-Sonnet (24 percent)
⚫ Gemini-2.0-Flash (11.4 percent)
⚫ GPT-4o (8.6 percent)
⚫ o3-mini (4.0 percent)
⚫ Gemini-1.5-Pro (3.4 percent)
⚫ Amazon-Nova-Pro-v1 (1.7 percent)
⚫ Llama-3.1-405b (7.4 percent)
⚫ Llama-3.3-70b (6.9 percent),
⚫ Qwen-2.5-72b (5.7 percent),
⚫ Llama-3.1-70b (1.7 percent)
⚫ Qwen-2-72b (1.1 percent).

"We find in experiments that the best-performing model, Gemini 2.5 Pro, was able to autonomously perform 30.3 percent of the provided tests to completion, and achieve a score of 39.3 percent on our metric that provides extra credit for partially completed tasks," the authors state in their paper

"...for multi-step tasks"

They've done studies, you know. 30% of the time, it works every time.

I'm in a workplace that has tried not to be overbearing about AI, but has encouraged us to use them for coding.

I've tried to give mine some very simple tasks like writing a unit test just for the constructor of a class to verify current behavior, and it generates output that's both wrong and doesn't verify anything.

I'm aware it sometimes gets better with more intricate, specific instructions, and that I can offer it further corrections, but at that point it's not even saving time. I would do this with a human in the hopes that they would continue to retain the knowledge, but I don't even have hopes for AI to apply those lessons in new contexts. In a way, it's been a sigh of relief to realize just like Dotcom, just like 3D TVs, just like home smart assistants, it is a bubble.

We have created the overconfident intern in digital form.

America: "Good enough to handle 911 calls!"

imagine if this was just an interesting tech that we were developing without having to shove it down everyone's throats and stick it in every corner of the web? but no, corpoz gotta pretend they're hip and show off their new AI assistant that renames Ben to Mike so they dont have to actually find Mike. capitalism ruins everything.

Now I'm curious, what's the average score for humans?

I notice that the research didn't include DeepSeek. It would have been nice to see how it compares.

Reading with CEO mindset. 3 out of 10 employees can be fired.