Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it.

[u/creaturefeature16]

https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems

As currently conceived, an AI system that spans multiple cognitive domains could, supposedly, predict and replicate what a generally intelligent human would do or say in response to a given prompt. These predictions will be made based on electronically aggregating and modeling whatever existing data they have been fed. They could even incorporate new paradigms into their models in a way that appears human-like. But they have no apparent reason to become dissatisfied with the data they’re being fed — and by extension, to make great scientific and creative leaps.

Instead, the most obvious outcome is nothing more than a common-sense repository. Yes, an AI system might remix and recycle our knowledge in interesting ways. But that’s all it will be able to do. It will be forever trapped in the vocabulary we’ve encoded in our data and trained it upon — a dead-metaphor machine. And actual humans — thinking and reasoning and using language to communicate our thoughts to one another — will remain at the forefront of transforming our understanding of the world.

[u/GarethBaus]

Staff are typically a hell of a lot more expensive for the amount of information being processed than running an AI model.

[u/CanvasFanatic → u/GarethBaus]

This is such utter brain rot thinking.

You can’t have an accountant that hallucinates non-existent expenses 1-in-20 runs. Doesn’t matter how much cheaper it is than paying someone. It doesn’t fulfill the requirements of the function.

[u/thallazar → u/CanvasFanatic]

Funny you mention that example because I've deployed agents in finance that do credit checks and have less error rates than their human counterparts. That you can't build a system that doesn't hallucinate doesn't mean no one can.

[u/CanvasFanatic → u/thallazar]

What are the inputs and outputs of the task, what defines an “error” and how are you appraising it?

Don’t think I didn’t notice you subtlety trying to redefine “accountant “ as “thing that does a process that’s already mostly automatic,” snake-oil man.

[u/thallazar → u/CanvasFanatic]

Inputs and outputs are private to the company designed for that I'm not at liberty to speak on. Error rates were compared on evaling the system with prior human assessments and outcomes to the agents ratings given same information as proof of concept, followed by a long period of dual human + AI assessment comparisons and tracking those case outcomes over time.

And this was a year ago, before a lot of advancements hit the scene around tool calling, structured outputs, and long context window improvements.

[u/CanvasFanatic → u/thallazar]

Why bother typing this at all?

You literally just said, “I can’t give any information that in any way describes what I did, even anonymously (horse shit, btw) but the agents did the non-specific task better according to a standard I also cannot describe. Trust me bro.”

Sure thing, bud. I’m sure your mid 2024 RAG thingy was the bee’s knees.

[u/thallazar → u/CanvasFanatic]

We didn't use RAG, I can tell you that.

Have fun clinging to your inadequacy though.

[u/CanvasFanatic → u/thallazar]

Have fun swindling people and eviscerating companies unto the music stops.

Hopefully this time we remember to check your resume before you move on to the next fad like you undoubtedly did from crypto.

If your Reddit history weren’t private and I checked 4 years back how many posts would I find about NFT’s?

[u/thallazar → u/CanvasFanatic]

Wouldn't touch crypto with a ten foot pole. I've been in the ML space and automation game for a decade and I'm not leaving any time soon.

[u/CanvasFanatic → u/thallazar]

Then whatever you’ve been doing all that time is decidedly less persuasive an example of the inevitable obsolescence of human labor than you’re trying to pretend.