this post was submitted on 22 Sep 2025
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The majority of "AI Experts" online that I've seen are business majors.
Then a ton of junior/mid software engineers who have use the OpenAI API.
Finally are the very very few technical people who have interacted with models directly, maybe even trained some models. Coded directly against them. And even then I don't think many of them truly understand what's going on in there.
Hell, I've been training models and using ML directly for a decade and I barely know what's going on in there. Don't worry I get the image, just calling out how frighteningly few actually understand it, yet so many swear they know AI super well
And even then I don’t think many of them truly understand what’s going on in there.
That's just the thing about neural networks: Nobody actually understands what's going on there. We've put an abstraction layer over how we do things that we know we will never be able to pierce.
I'd argue we know exactly what's going on in there, we just don't necessarily, know for any particular model why it's going on in there.
I have a masters degree in statistics. This comment reminded me of a fellow statistics grad student that could not explain what a p-value was. I have no idea how he qualified for a graduate level statistics program without knowing what a p-value was, but he was there. I'm not saying I'm God's gift to statistics, but a p-value is a pretty basic concept in statistics.
Next semester, he was gone. Transferred to another school and changed to major in Artificial Intelligence.
I wonder how he's doing...
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Ding ding ding.
It all became basically magic, blind trial and error roughly ten years ago, with AlexNet.
After AlexNet, everything became increasingly more and more black box and opaque to even the actual PhD level people crafting and testing these things.
Since then, it has basically been 'throw all existing information of any kind at the model' to train it better, and then a bunch of basically slapdash optimization attempts which work for largely 'i dont know' reasons.
Meanwhile, we could be pouring even 1% of the money going toward LLMs snd convolutional network derived models... into other paradigms, such as maybe trying to actually emulate real brains and real neuronal networks... but nope, everyone is piling into basically one approach.
Thats not to say research on other paradigms is nonexistent, but it is barely existant in comparison.
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I’ve given up attending AI conferences, events and meetups in my city for this exact reason. Show up for a talk called something like “Advances in AI” or “Inside AI” by a supposed guru from an AI company, get a 3 hour PowerPoint telling you to stop making PowerPoints by hand and start using ChatGPT to do it, concluding with a sales pitch for their 2-day course on how to get rich creating Kindle ebooks en masse
Even the dev oriented ones are painfully like this too. Why would you make your own when you subscribe to ours instead? Just sign away all of your data and call this API which will probably change in a month, you'll be so happy!
Yeah, I've trained a number of models (as part of actual CS research, before all of this LLM bullshit), and while I certainly understand the concepts behind training neural networks, I couldn't tell you the first thing about what a model I trained is doing. That's the whole thing about the black box approach.
Also why it's so absurd when "AI" gurus claim they "fixed" an issue in their model that resulted in output they didn't want.
No, no you didn't.
Love this because I completely agree. "We fixed it and it no longer does the bad thing". Uh no, incorrect, unless you literally went through your entire dataset and stripped out every single occurrence of the thing and retrained it, then no there is no way that you 100% "fixed" it
business majors are the worst i swear to god
Didn't you know? Being adept at business immediately makes you an expert in many science and engineering fields!
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I have personally told coworkers that if they train a custom GPT, they should put "AI expert" on their resume as it's more than 99% of people have done - and 99% of those people didn't do anything more than tricked ChatGPT into doing something naughty once a year ago and now consider themselves "prompt engineers."
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Hot take : Adding "Prompt expert" to a resume is like adding "professional Googler"
There used to be some skill involved in getting search engines to give you the right results, these days not so much but originally you did have to inject the right kind of search terms and a lot of people couldn't work that out.
Many years ago back before Google became so dominant I had a co-worker who could not get her head around the idea that you didn't in fact have to ask a search engine in the form of a question with a question mark on the end. It used to be somewhat of a skill.
This is actually very true. I did always object to knowing that Boolean operators work in Google coming to be called "Dorking." I amassed a sizeable MP3 collection in the early oughts thanks to searching ".mp3" and finding people's public folders filled with their CD rips. Just out there, freely hanging the internet wind.
These days SEO optimization has rendered Google itself borderline useless, and IIRC they removed some operators from use at some point. I have to use DDG, Brave and Leta searching Google if I want to find anything that's not just a URL for an obvious thing. And half the time none of that works anyway and I can't even find things I've found previously.
You actually do "file:mp3", this is how found most of my course literature without ending up in a bunch off spam sites.
I used Filetype:mp3 but I've noticed it really doesn't really work anymore.
SEO optimization
Man they should really incorporate optimization into the initialism
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I'd trust the latter any day.
the latter just means IT expert
It was the same with crypto TBH. It was a neat niche research interest until pyramid schemers with euphemisms for titles got involved.
With crypto, it was largely MLM scammers who started pumping it (futily, for the most part) until Ross Ulrich and the Silk Road leveraged it for black market sales.
Then Bitcoin, specifically, took off as a means of subverting bank regulations on financial transactions. This encouraged more big-ticket speculators to enter the market, leading to the JP Morgan sponsorship of Etherium (NFTs were a big part of this scam).
There's a whole historical pedigree to each major crypto offering. Solana, for instance, is tied up in Howard Lutnick's play at crypto through Cantor Fitzgerald.
Interesting.
I guess AI isn't so dissimilar, with major 'sects' having major billionaire/corporate backers, sometimes aiming for specific niches.
Anthropic was rather infamously funded by FTX. Deepseek came from a quant trading (and to my memory, crypto mining) firm, and there's loose evidence the Chinese govt is 'helping' all its firms with data (or that they're sharing it with each other under the table, somehow). Many say Zuckerberg open-sourced llama to 'poison the well' over OpenAI going closed.
Silk Road and other black market vendors existed well before the scams started. You could mail order drugs online when bitcoin was under $1, the first bubble pushed the price to $30 before crashing to sub-$1 again. THEN the scams and market manipulation took off.
Later people forked the project to create new chains in order to run rug pulls and other modern crypto scams.
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Even I know what this is and I don't have a background in AI/ML.
This image is clearly of my hands with an elastic band at the back of class two decades ago
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OK but what actually is this image?
Basic model of a neural net. The post is implying that you're arguing with bots.
https://en.wikipedia.org/wiki/Neural_network_(machine_learning)
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Illustration of a neural network.
The simplest neural network (simplified). You input a set of properties(first column). Then you weightedly add all of them a number of times(with DIFFERENT weights)(first set of lines). Then you apply a non-linearity to it, e.g. 0 if negative, keep the same otherwise(not shown).
You repeat this with potentially different numbers of outputs any number of times.
Then do this again, but so that your number of outputs is the dimension of your desired output. E.g. 2 if you want the sum of the inputs and their product computed(which is a fun exercise!). You may want to skip the non-linearity here or do something special™
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isn't this the Trial of the Sekhemas in PoE2?
As a data scientist who also plays POE2, I laughed at this a lot longer than I should have
Probably bc they forgot the bias nodes
(/s but really I don't understand why no one ever includes them in these diagrams)
Something to do with Large Language Models?
It's a neural network diagram
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