Ekky

You thought journalism had reached rock bottom already? Watch this:

Oh, no no, I was talking about the down votes that you’ve been receiving. My bad!

Doesn’t matter, it’s fun to see how far you can bend definitions (and I did go rather far with that one).

Also, I’m not sure where those down votes are coming from. Showerthoughts don’t need to be fully logical, taking all edge cases into account, etc. After all, it’s just a showerthought to discuss and joke/awe about.

In that case, you’d never see anything through anything but a “screen” because of the lens in your eye. (Using the definition of “screen” to be a “barrier”, which in this case provokes a transformation, or projection, of light.)

I think i read that fighter pilots need to be able to identify a plane in one frame at 300 fps, and that the theoretical limit of the eye is 1000+ fps.

Though, whether the brain can manage to process the data at 1000+ fps is questionable.

Huh, I’m not sure they are comparable.

Didn’t USB A and USB B use a master-slave relationship in which the male would (generally) always be the slave, whereas USB C uses agreement and discussion to decide the master and slave roles regardless of connector gender.

Please do correct me if I’m wrong. Also, do we say “agent” now instead of “slave”, or what is the new term?

I’m with you here, Neptune’s definition seems to overspecify the extract from Oxford they presented.

If we boil stereotyping down to its core components, then it appears to simply be an instance of correlation using subjective and non-complete data: “This individual exerts traits a, b, and c, which means they are highly likely to also exert traits x, y, and z.”

Or: “This individual is operating a car (unique trait/type of person), therefore their visibility and attention capacity are likely reduced or under strain (overgeneralization as driving might come natural to them, and fixed as I might assume that no one is a natural).”

^This is, of course, an oversimplification, as I’m going purely by Neptune’s words and my own understanding, and have not looked up additional sources.

And then links to a similar sounding but ultimately totally unrelated site.

I’m pretty sure it’s more like

Junior dev: Got all the nice addons, RGB lighting, only uses dark theme, got all the stickers, works from either a café or moms basement.

VS Senior dev: Works on company standard issue hardware, barely customizes visuals (but got a script which makes a cup of coffee on the shared machine in exactly 2 minutes and 30 seconds), works in shared office, has old rolling cabinet with unknown artifacts last touched 10+ years ago.

Obviously this is an overgeneralization and not a catch-all, you might even say that it’s “programmer humor”.

Jup, most definitely!

I’d much rather have just one unhinged uncle at St. Martin’s Day than having everybody come off as the unhinged uncle by lack of supervision of the LLMs talking in your place, making it seem like being unhinged is normal and thereby creating artificial peer pressure in a truly wicked exercise of laziness.

People who rely too heavily on autocorrect do already now cause misunderstandings by writing something they did not intend to.

I had a friend during uni who was dyslectic, and while the words in his messages were written proper you still had to guess the context from the randomly thrown together words he presented you with.

Now that we can correct not only a single word or roughly the structure of a sentence, but instead fabricate whole paragraphs and articles by providing a single sentence, I imagine we will see a stark increase in low-quality content, accidental false information, and easily preventable misunderstandings - More than we already have.

AI: “Can I copy your work?”

Phil: “Just don’t make it obvious.”

AI:

Agreed for induction, but I’d mich rather use one or two minutes more cleaning the knobs than having to almost cook my finger on this 60-90 degree Celcius hot conventional stove’s touch surface to change the plate from step 7 to 4 for 10 FUKKEN SECONDS! OUCH!

Having to restart it 2-3 times during cooking because it got confused (pan moved slightly to the side) is also rather annoying.

Edit & tl:dr: Touch works decent on induction, just please keep it far away from any conventional stoves.

Oh right, I do actually have track, volume, and “take call” on the wheel. I think I did use them once, but it just never stuck since they felt awkward to use.

I’m more concerned about fog lights, emergency lights, and Window heating, as law usually requires you to be able to use them if conditions require it.

Same, I’ve got an Opel Corsa from 2016, so it’s pretty much brand new.

The only things in the wheel are the speed control, wipers, and default lights.

For everything else required for driving, such as fog lights, emergency lights, front and back Window heating, AC, radio, and of course the shift stick, I’ll need to remove a hand from the wheel.

Luckily for me, the Touchscreen in the middle only handles less important things like navigation and external music sources.

Do you latch your sheets below your matress’ corners? They’re usually meant to fully cover the top and all sides, except for those with no elastic.

Or, perhaps the elastic has gotten too lash, so they slip out from below and ride up the sides?

I don’t have a proper solution for washing and drying, except putting them in a washing bag so they don’t eat all the other laundry.

Neural nets are a technology which is part of the umbrella term “machine learning”. Deep learning is also a term which is part of machine learning, just more specialized towards large NN models.

You can absolutely train NNs on your own machine, after all, that’s what I did for my masters before Chatgpt and all that, defining the layers myself, and also what I do right now with CNNs. That said, LLMs do tend to become so large that anyone without a super computer can at most fine tune them.

“Decision tree stuff” would be regular AI, which can be turned into ML by adding a “learning method” like a KNN or neural net, genetic algorithm, etc., which isn’t much more than a more complex decision tree where decision thresholds (weights) were automatically estimated by analysis of a dataset. More complex learning methods are even capable of fine tuning themselves during operation (LLMs, KNN, etc.), as you stated.

One big difference from other learning methods and to NN based methods, is that NN likes to add non-weighted layers which, instead of making decisions, transform the data to allow for a more diverse decision process.

EDIT: Some corrections, now that I’m fully awake.

While very similar in structure and function, the NN is indeed no decision tree. It functions much the same as one, as is a basic requirement for most types of AI, but whereas every node in a decision tree has unique branches with their own unique nodes, all of a NN’s nodes are interconnected to all nodes of the following layer. This is also one of the strong points of a NN, as something that seemed outrageous to it a moment ago might have become much more plausible when looking at it from a different point of view, such as after a transformative layer.

Also, other learning methods usually don’t have layers, or, if one were to define “layer” as “one-shot decision process”, they pretty much only have a single or two layers. In contrast, the NN can theoretically have an infinite amount of layers, allowing for pretty much infinite complexity as long as the inputted data is not abstracted beyond reason.

At last, NN don’t back-propage by default, though they make it easy to enable such features given enough processing power and optionally enough bandwidth (in the case of chatGPT). LLMs are a little different, as I’m decently sure they implement back-propagation as part of the technologies definition, just like KNN.

This became a little longer than I had hoped, it’s just a fascinating topic. I hope you don’t mind that I went into more detail than necessary, it was mostly for the random passersby.

AI is a very broad term, ranging from physical AI (material and properties of a robotic grabbing tool) to AI (as seen in many games, or in a robotic arm to calculate path from current position to target position) and to MLAI (LLM, neural nets in general, KNN, etc.).

I guess it’s much the same as asking “are vehicles bad?”. I don’t know, are we talking horse carriages? Cars? Planes? Electric scooters? Skateboards?

Going back to your question, AI in general is not bad, though LLMs have become too popular too quick and have thus ended up being misunderstood and misused. So you can indeed say that LLMs are bad, at least when not used for their intended purposes.

It appears, that with the increase in popularity of machine learning, the percentage of people who properly source and sanitize their training data has steeply decreased.

As you stated, a MLAI can only be as good as the data it was trained on, and is usually way worse. The popularity and application of MLAIs built with questionable practices scare me, though, at least their fuckups will keep me employed and likely more busy than ever.