Serious ask:

I need a crash-course in AI.

Context: my line manager has been asked to evaluate the use of AI at work. He's come to me to ask if I want to help, as he knows I hate it (and he does too), but I'm...vaguely aware...that not all things that are called "AI" are equal.

(like, the "AI" of NPCs in a game is not the same as the "AI" used to create the type of image we generally call "slop", right?)

We want to make sure we're armed with decent knowledge, because we don't want people to say "Oh, ignore them, they're just haters" if we're talking about something that maybe isn't the "bad" kind of AI (if such a thing exists - I don't know enough to be confident right now)

At the moment, *to the best of my currently limited knowledge*, our AI usage is pretty much limited to people using Gemini to create emails and transcripts of meetings.

(I hope that makes sense)

@neonsnake The world of AI is much broader than Generative AI. If there's a use for directed neural nets, machine learning, computer vision etc then be open to it. One way of shutting down being a forced sloperator is to use other AI where appropriate to "tick the box"

@ingram Thank you

Would you be able to explain "neural nets, machine learning, computer vision" in VERY layman's terms to me?

And would it be possible for me - not very techy - to recognise them vs LLMs?

@neonsnake I'm not an expert, or even moderately skilled in the areas but have been to a course...

Neural networks have "weights" been neurons and the different methods have different ways of determining the weights. The undirected nets are basically self guided with a set of inputs and known outputs. The computer figures out based on probability. This is how a computer knows if the photo is a dog or a cat etc, or if cells are cancerous or normal.

(1/3)

@neonsnake In my opinion AI tools are best suited for interpolation working within the known things. Gen AI extrapolates and that's where the hallucinations happen.

The AI tool I've found most useful is audio transcription. I run the Whisper model on my low end Nvidia GPU and it turns rough audio into surprisingly good text.

Image classification and computer vision to identify good/bad products on a manufacturing line is another good use.

(2/3)

@neonsnake It's technical but the WP page on Neural Nets is good. https://en.wikipedia.org/wiki/Types_of_artificial_neural_networks?wprov=sfla1 (3/3)
Types of artificial neural networks - Wikipedia

@ingram "Image classification and computer vision to identify good/bad products on a manufacturing line is another good use."

That makes sense to me - and is also something roughly relevant to what I actually do for a living, so I can picture it a lot easier than some other examples!

Audio transcription makes sense to me as well - am I understanding correctly that "Whisper" is run completely locally?

(not sure if this helps, but it feels a bit like when I used to use Photoshop to remove an unwanted object in a photo, and all of the "clever" stuff was done on my own PC? Is that an accurate analogy?)

@neonsnake Yes, you can run Whisper locally. I use pinokio to run it, and it's pretty much point and click. I think it runs on CPU alone, but Nvidia GPU makes it faster.
https://github.com/pinokiofactory/whisper-webui
GitHub - pinokiofactory/whisper-webui: Pinokio Installer for Whisper-Webui

Pinokio Installer for Whisper-Webui. Contribute to pinokiofactory/whisper-webui development by creating an account on GitHub.

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@neonsnake @ingram Wiki has a good overview. Machine learning is the automated statistical analysis of existing data that allows you to perform tasks on new data without explicit programming. eg: by looking at x-rays of people with and without cancer being able to look at new x-rays and determine if the person has cancer or not. ML has been done for decades and there are many techniques.

https://en.wikipedia.org/wiki/Machine_learning

Machine learning - Wikipedia

@neonsnake @ingram Neural networks are a type of ML which roughly simulates how the nerves in a brain works, using clever tricks to establish the correct "connections" between nerves. Machine vision is a broad range of techniques used to analyse images and extract meaning from them, typically via neural networks. LLMs are _very_ large neural networks that allow you generate text that is a likely response based on an input prompt. "Spicy autocomplete".
@neonsnake @ingram So LLMs and chatbots are a subset of ML. The question is what are you trying to achieve and how might machine learning help you.

@bjn @ingram

"Machine learning is the automated statistical analysis of existing data that allows you to perform tasks on new data without explicit programming"

Crikey!

@neonsnake @ingram ML can be anything from very simple curve fitting that allows you to estimate an output for a new input value, to the insane complexity of an LLM. The wiki article is a nice overview.

@bjn @ingram

Sorry, but I don't know what "curve fitting" means in this context. I'm not able to use this in a conversation in the context of my original post.

Linear Regression in Machine learning - GeeksforGeeks

Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.

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@bjn @ingram I dont know what that means.
@neonsnake @ingram Read the link I provided.
@bjn @ingram how much do you know about supply vs demand and how it manifests itself in promo cycles?

@bjn

Can you let me know please, as this is really, really important to the context within which I asked the question, as per my original post?

@ingram

@neonsnake @ingram It’s not a thing I have more than a passing understanding of sorry. My background is elsewhere. I’m not at all sure what background reading to suggest.

@bjn

Of course. No worries.

@ingram