Very much smart people : microblogmemes

[–] scrubbles@poptalk.scrubbles.tech 311 points 2 weeks ago (10 children)

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

[–] waigl@lemmy.world 94 points 2 weeks ago* (last edited 2 weeks ago) (5 children)

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.

[–] notabot@piefed.social 62 points 2 weeks ago (1 children)

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.

[–] GreenMartian@lemmy.dbzer0.com 25 points 2 weeks ago (2 children)

But, more importantly, who is going on in there?

[–] Klear@quokk.au 11 points 2 weeks ago (3 children)

And how is it going in there?

[–] GreenMartian@lemmy.dbzer0.com 23 points 2 weeks ago

Not bad. How's it going with you?

[–] jqubed@lemmy.world 8 points 2 weeks ago (1 children)

That’s what we’re trying to find out! We’re trying to find out who killed him, and where, and with what! Tim Curry in Clue shouting the above text

[–] TropicalDingdong@lemmy.world 8 points 2 weeks ago

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[–] sp3ctr4l@lemmy.dbzer0.com 24 points 2 weeks ago* (last edited 2 weeks ago) (2 children)

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.

[–] SkyeStarfall@lemmy.blahaj.zone 8 points 2 weeks ago* (last edited 2 weeks ago) (3 children)

Il'll give you the point regarding LLMs.. but conventional neural networks? Nah. They've been used for a reason, and generally been very successful where other methods have failed. And there very much are investments into stuff with real brains or analog brain-like structures.. it's just that it's far more difficult, especially as have very little idea on how real brains work.

A big issue regarding digitally emulating real brain structures is that it's very computationally expensive. Real brains work using chemistry, after all. Not something that's easy to simulate. Though there is research in this are, but that research is mostly to understand brains more, not for any practical purpose, from what I know. But also, this won't solve the black box problem.

Neural networks are great at what they do, being a sort of universal statistics optimization process (to a degree, no free lunch etc.). They solved problems that failed to be solved before, that now are considered mundane. Like, would anyone really think it would be possible to have your phone be able to detect what it was you took a picture of 15 years ago? That was considered to be practically impossible. Take this xkcd from a decade ago, for example https://xkcd.com/1425/

In addition, there are avenues that are being explored such as "Explainable AI" and so on. The field is more varied and interesting than most people realize. And, yes, genuinely useful. And not every neural network is a massive large scale one, many are small-scale and specialized.

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[–] limelight79@lemmy.world 14 points 2 weeks ago* (last edited 2 weeks ago) (2 children)

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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[–] catch22@programming.dev 10 points 2 weeks ago (1 children)

Feature Visualization How neural networks build up their understanding of images

https://distill.pub/2017/feature-visualization/

[–] mrmacduggan@lemmy.ml 16 points 2 weeks ago

This method is definitely a great way to achieve some degree of explainability for images, but it is based on the assumption that nearby pixels will have correllated meanings. When AI is making connections between far-away features, or worse, in a feature space that cannot be readily visualized like images can, it can be very hard to decouple the nonlinear outputs into singular linear features. While AI explainability has come a long way in the last few years, the decision-making processes of AI are so different from human thought that even when it can "show its work" by showing which neurons contributed to the final result, it doesn't necessarily make any intuitive sense to us.

For example, an image-identification AI might identify subtle lens blur data to determine the brand of camera that took a photograph, and then use that data to make an educated guess about which country the image was taken in. It's a valid path of reasoning. But it would take a lot of effort for a human analyst to notice that the AI is using this process to slightly improve its chances of getting the image identification correct, and there are millions of such derived features that combine in unexpected ways, some logical and some irrationally overfitting to the training data.

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[–] expr@programming.dev 52 points 2 weeks ago (5 children)

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.

[–] scrubbles@poptalk.scrubbles.tech 20 points 2 weeks ago (2 children)

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

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[–] JandroDelSol@lemmy.world 43 points 2 weeks ago (3 children)

business majors are the worst i swear to god

[–] SexualPolytope@lemmy.sdf.org 37 points 2 weeks ago (1 children)

They are literally what's causing the fall of our society.

[–] Dogiedog64@lemmy.world 9 points 2 weeks ago

Objectively, per Ed Zitron.

[–] scrubbles@poptalk.scrubbles.tech 24 points 2 weeks ago (1 children)

Didn't you know? Being adept at business immediately makes you an expert in many science and engineering fields!

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[–] GreenShimada@lemmy.world 32 points 2 weeks ago (1 children)

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."

[–] scrubbles@poptalk.scrubbles.tech 8 points 2 weeks ago

Absolutely agree there

[–] skisnow@lemmy.ca 21 points 2 weeks ago (1 children)

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

[–] scrubbles@poptalk.scrubbles.tech 10 points 2 weeks ago

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!

[–] FauxLiving@lemmy.world 10 points 2 weeks ago (3 children)

Hell, I’ve been training models and using ML directly for a decade and I barely know what’s going on in there.

Outside of low dimensional toy models, I don’t think we’re capable of understanding what’s happening. Even in academia, work on the ability to reliably understand trained networks is still in its infancy.

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[–] breg@discuss.tchncs.de 147 points 2 weeks ago

[–] brucethemoose@lemmy.world 129 points 2 weeks ago (1 children)

It was the same with crypto TBH. It was a neat niche research interest until pyramid schemers with euphemisms for titles got involved.

[–] UnderpantsWeevil@lemmy.world 44 points 2 weeks ago (4 children)

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.

[–] brucethemoose@lemmy.world 20 points 2 weeks ago* (last edited 2 weeks ago)

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.

[–] FauxLiving@lemmy.world 12 points 2 weeks ago (6 children)

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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[–] killeronthecorner@lemmy.world 75 points 2 weeks ago (1 children)

This image is clearly of my hands with an elastic band at the back of class two decades ago

[–] cows_are_underrated@feddit.org 15 points 2 weeks ago* (last edited 2 weeks ago) (1 children)

Yeah but why am I arguing with them?

[–] killeronthecorner@lemmy.world 10 points 2 weeks ago

Maybe it's because they were stretching.

[–] Infernal_pizza@lemmy.dbzer0.com 53 points 2 weeks ago (7 children)

OK but what actually is this image?

[–] SatyrSack@lemmy.sdf.org 95 points 2 weeks ago (2 children)

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)

[–] CookieOfFortune@lemmy.world 11 points 2 weeks ago (3 children)

Wouldn’t a bot recognize this though?

[–] SatyrSack@lemmy.sdf.org 47 points 2 weeks ago

A bot might, but this post is pointing out how common it is for people who consider themselves AI experts to not recognize this diagram that is basically part of AI 101

[–] driving_crooner@lemmy.eco.br 15 points 2 weeks ago (1 children)

They're not saying that the bots are asking what the image is, but users (may be bots or not) that sell themselves as AI/ML experts.

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[–] Asetru@feddit.org 57 points 2 weeks ago

Illustration of a neural network.

[–] Gladaed@feddit.org 33 points 2 weeks ago (3 children)

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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[–] iAvicenna@lemmy.world 48 points 2 weeks ago (3 children)

Wait till you talk to LinkedIn people interested in Quantum Physics

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[–] kSPvhmTOlwvMd7Y7E@lemmy.world 32 points 2 weeks ago (3 children)

Hot take : Adding "Prompt expert" to a resume is like adding "professional Googler"

[–] echodot@feddit.uk 18 points 2 weeks ago (1 children)

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.

[–] hansolo@lemmy.today 16 points 2 weeks ago (4 children)

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.

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[–] AdrianTheFrog@lemmy.world 17 points 2 weeks ago

Probably bc they forgot the bias nodes

(/s but really I don't understand why no one ever includes them in these diagrams)

[–] sandywarhole@lemmy.zip 16 points 2 weeks ago (1 children)

isn't this the Trial of the Sekhemas in PoE2?

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[–] Danquebec@sh.itjust.works 14 points 2 weeks ago

Even I know what this is and I don't have a background in AI/ML.

[–] zr0@lemmy.dbzer0.com 13 points 2 weeks ago

Same as if you’d ask a crypto bro how a blockchain actually works. All those self proclaimed Data Scientists who were able to use pytorch once successfully by following a tutorial, just don’t want to die.

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