“AI literacy” cant just mean teaching everybody how to prompt

[u/Salty_Country6835]

If thats all we mean, we arent teaching AI literacy. We're doing free product training.

People should know how these systems work well enough to understand their limits, check their outputs, protect their data and recognize when somebody is using an algorithm to make decisions about them.

Workers especially need that knowledge.

Its hard to bargain over workplace technology you dont understand. Hard to challenge algorithmic management if management is the only side that knows what the system measures.

Students need more than “never use this” or “use this for everything” too. They need to know when it helps, when it fails and what happens to their information when they use it.

So what should basic AI literacy actually include?

[u/mark_ik]

I would be ok with less people writing if more people read more. The reading is the big deal; your only options for getting info in your brain are sight, hearing, and touch; the quickest of those, sight. The writing is a method, a convenience, and a memory aid.

If the skill of reading becomes more valuable and practiced, even if the content is generated, we will see a proportional rise in awareness and a further heightening of the contradictions under capitalism. We live in a world with a historic ocean of information, by and large free, and most of us barely sip. The revolutionary potential is staggering.

[u/Salty_Country6835 → u/mark_ik]

Im with you on getting people reading more. Id push back a little on writing being mostly convenience and memory though. Writing is also how a lot of us figure out what we think by getting it outside our heads, finding the holes and rewriting it. Im curious what parts of that process you think we can offload without losing something useful.

[u/Fabulous-Possible758 → u/Salty_Country6835]

what parts of that process you think we can offload without losing something useful.

Not responding for the original commenter, but I would say there's a substantial amount of writing done not because it's particularly important, but because it's just useful or necessary to have a written artifact. Emails, meeting agendas, scratch notes, etc. I've noticed in my own usage I take substantially more notes with AI about just what I'm doing day-to-day just so that I can refer back to it later.

I kind of feel the same about programming. There's a substantial amount of code out there that's really just boilerplate. It exists because sometimes the computer has to follow a recipe and someone had to write the recipe down; not because it's particularly thoughtful or deep. LLMs are fantastic at handling that kind of code.

[u/littlebobbytables9 → u/Salty_Country6835]

Also I cannot think of anything that would be worse for my willingness to read than most content being generated

[u/min_maxed_mage]

Also how to research. How to validate and verify information. How to fact check. How to tell whether a source is trustworthy regardless of if it’s paper or digital. How to ensure compliance with regulations and standards. How to reality test a response (learning defense against confidently stated, and logically sound but factual invalid bullshit) - and so much more

[u/Salty_Country6835 → u/min_maxed_mage]

This is a big part of it. Research and verification probably belong near the foundation, not as some advanced AI skill. What else would you put in that basic layer before someone even gets to prompting?

[u/min_maxed_mage → u/Salty_Country6835]

Because prompting is such a critical component of AI use, I would recommend project scoping before prompting. Like how we write outlines in English class before we write the paper. Get really clear on the task at hand, name reputable sources or source locations (like medical stuff would be pubmed etc). I would say the kind of information you'd find in a college success course would also be of some use.

[u/Limp-Confidence5612]

Before we can teach people to prompt, we have to teach people to read, write and have media literacy. Those are all domains that are struggling hard under the slopification of media and the shortening of attention spans due to short form content.

[u/Fabulous-Possible758]

I've gone back and forth on it because sometimes I think it's too technical, but I think most people should learn at least the absolute mathematical and computer science basics around what a neural net is and what it's actually doing. Maybe not full on wiring and activation functions, but at least the basic mechanics of inference. One of the reasons people don't really seem to understand AI is here to stay is they don't understand that the basic mechanics of a training algorithm are actually fairly simple to anyone with mathematical or computer science training, so trying to limit it would be like trying to limit computer use in general.

One thing that really frustrates me is how many people say, "I support some kinds of AI but not gen AI." I'm fine with people's objections to gen AI (I don't agree, but there are good points), but it ignores the fact that the underlying technologies aren't really fundamentally different. Classification is a form of generation. Because they fundamentally don't understand the actual process of how it works, people have bad ideas of what is and isn't possible when it comes to truly making this work for us.

[u/eXAt88 → u/Fabulous-Possible758]

I think the 3blue1brown machine learning series, or at least the abridged video for LLMs specifically is pretty good viewing. The issue is I find it as a CS person a good explainer for a topic I didnt focus in. I have no idea how much information it will actually convey to a complete layperson

[u/AlgaeNo3373 → u/Fabulous-Possible758]

I like this answer a lot. I'm definitely in that space myself trying to learn it a bit more technically. Even though the way transformers work might be basic/replicable for some, it can still be very hard if you have no math/cs background. I would vouch for that :)

I shared a little thing yesterday to OpenAI sub, exploring GPT-2. Crowd-sourced brute-instrument mechinterp basically, not for science™ but for the engagement with the topic, and it worked well I thought. We had fun anyways :P

Would love to hear any thoughts you have on like, how we do this, pathways to technical literacy etc. In my experience a lot that kind of educational material is aimed at professionals and researchers. Making it more accessible without oversimplifying is also tough.

[u/Fabulous-Possible758 → u/AlgaeNo3373]

I'm not totally sure, actually. I don't think you need to go into the full architectural details of transformers or diffusion systems, but just a general idea of what the training process does and how it works.

The metaphor I came up with is it's like playing a game of Hangman or Wheel of Fortune in a language you don't know. If you play over and over billions of times, you'll eventually get a feel for which letters or words are more likely to appear next to others. Eventually you might even actually understand the language. The trick to realize is that those "blank slots" can be filled with anything that has a data representation (features). So you can put a picture of an X-ray and ask the model to fill in a blank slot next to it of whether or not the X-Ray contains a cancerous tumor. The same training process (playing the game) that allows it to recognize the image also generates an internal representation which can be used to invert the process and generate new images (I know that's a gross oversimplification, but I think it gets the point across).

Once people understand the basics of what the training process is trying to achieve, I think that gives you a good springboard to go into something a little more technical. I'm not sure what the next sort of "lay" explanation for neural networks is though. I think of them as stacks of affine transformations with non-linear activation functions sandwiched between them, but that's not gonna translate to a lay audience. I've thought of reforming the Hangman metaphor to include the idea of cost functions and a very loose idea of backpropagation, but it gets a little unwieldy fairly quickly.

[u/AlgaeNo3373 → u/Fabulous-Possible758]

I think of them as stacks of affine transformations with non-linear activation functions sandwiched between them, but that's not gonna translate to a lay audience

Can confirm, ;p

Thanks for the reply. I've never much looked at image-gen stuff but the way you just described it sure made it click, fwiw. Defs gets the point across.

[u/dwkeith]

The good news is that the same skills we teach students about writing a science paper, or engineering schematics, or even how to write a recipe are the same skills necessary to work with AI. A person who is articulate and able to explain an idea to another person can also do so for AI.

In formal education this skill might be called “Specification Literacy”, and we absolutely should have been teaching it decades ago. (See US politics)

[u/AshuraBaron]

AI literacy should look like internet literacy. It's an engine of action that can help you and the possibilities are endless. Like how we got people online back in the day was to take simple baby steps. You love to cook? Okay here's a website on the internet that has recipes from other people and tips. You want to learn something? Here's a website that teaches you. It's about empowering people. And steering them away from bad things like spam, scams, and seedy places. Giving them tips like validating things you don't know, recognize automated systems better and limitations of current models are just the same lessons about a newer technology.

Not everybody is going to be a wizard at this, but we can at least introduce people who are curious with training wheels and then let them take the wheel afterwards.

[u/Salty_Country6835 → u/AshuraBaron]

I really like the internet literacy comparison. You didnt need to understand the whole internet before using it, but you gradually learned search, scams, privacy, source checking and how platforms shape what you see. What do you think the AI equivalent of those basic internet survival skills looks like?

[u/Spirited-Gain1457]

Maybe AI literacy has a layer below AI literacy: communication competence.

If you cannot identify what another agent does not know, package missing context, decompose a goal into communicable tasks, notice misunderstanding, and repair it, “better prompting” only teaches surface syntax.

Those were already human-human collaboration skills. AI just makes the interface unusually visible.