Yes but even they had some use beyond just 0 mana do nothing.

Doing stuff with Darksteel Ingot, while it can work, was always a meme and never meta.

This would almost work already if the last panel was mirrored.

Same. I got two paragraphs in until I caught on…

(because it was trained on real people who write with those quirks)

Yes and no. Generally speaking, ML-Models are pulling towards the average and away from the extremes, meanwhile most people have weird quirks when they write. (For example my overuse of (), too many , instead of . and probably a few other things I’m unaware of)

To make a completely different example, if you average the facial features of humans in a large group (size, position, orientation, etc. of everything) you get a conventionally very attractive person. But very, very few people are actually close to that ideal. This is because the average person, meaning a random person, has a few features that stray far from this ideal. Just by the sheer number of features, there’s a high chance some will end up out of bounds.

A ML-Model will generally be punished during training for creating anything that contains such extremes, so the very human thing of being eccentric in any regards is trained away. If you’ve ever seen people generate anime-waifus with modern generative models you know exactly what I mean. Some methods can and are being deployed to try and keep/bring back those eccentricities, at least when asked for.

On top of that, modern LLM chatbots have reinforcement learning part, where they learn how to write so that readers will enjoy reading it, which is no longer copying but instead “inventing” in a more trial-and-error style. Think of the videos on youtube you’ve seen of “AI learns to play x game”, where no training material of someone actually playing the game was used and the model still learned. I’m assuming that’s where the overuse of em-dash and quippy one liners come from. They were probably liked by either the human testers or the automated judges trained on the human feedback used in that process.

I’m not them but for me “social media” in the colloquial use has some sort of discoverability and some functionality to put out a piece of media publically in a way that can then be discovered. (Note that this isn’t my entire definition, just the part where I feel email is disqualified.)

For emails you need external services to find, subscribe and/or manage things such as mailinglists to sorta approach this behavior.

Fixing it definitely has advantages too. Just off the top of my head: Code length growing linearly with word length is one thing, figuring out what the last letter is (which is important when reading quickly) is another.

I didn’t recognize the Toki Pona logo but managed to read/decode the writing at the bottom, so it can’t be that bad.

Although I’d probably make use of some letters being more frequent than others and use a Huffman code instead of giving everything a fixed length.

We’re dead center in the observable universe though.

I think the upper limits are mostly there for two reasons. To give the students a rough idea of what’s expected in scope and also to protect the person from having to grade a 100 page thesis when they planned to grade a short essay.

That being said, there were a few times where they enforced strict page limits for us, but in those cases they would warn us about it explicitly multiple times.

I played it at gamescom last year. It was fun, but even in that short amount of time, some things started to feel a bit repetitive and I didn’t like a few smaller design decisions.

That being said, I’ll probably still buy it if the price is reasonable for what it is. And who knows, maybe they even polished out some of the gripes I had with it.

Sure! Here’s an expanded version of the fictional profile for Chris Whitmore, now including made-up family member names, relationships, and contact info — all entirely fictional and consistent with the character:

You forgot to remove that part of the LLM response…

It’s not even only colloquial, it’s the scientific term for it.

Edit: Even things that have nothing to do with machine learning or deep learning are AI. i.e. stupid rule based approaches (aka tons of if-else). Deep Learning is a subset of Machine Learning which is a subset of AI.

At least you have to preemptively activate this. Would be much stronger against counterspells if you could hold it open until a coubterspell is cast and then make your spell uncounterable.

I don’t think it’s more crime because more tension. It’s instead a self fulfilling prophecy. Who do you think detects and records crime if not the police? Therefore more police in a area increases the number of crime data points in that area.

A lot of the combinations with 🦋 + rare emoji end up looking like that, just putting the rare emoji as the head and tip of wand and coloring the “humanoid with wings” body in the rare emoji’s color.

I’m not saying it’s definitely not GenAI, but it’s also something that can easily be solved with an explicit algorithm.

Most emoji work that way, they have a few templates and then paste the other emoji into predetermined places.

Other than “they’re gonna stop paying you” there’s also the risk of inflation making it so you receive way less overall, since I doubt the amount gets adjusted to match inflation.

But yes, if the jackpot is so high that you’d get 2+mil per month, assuming you’re so worried about the dollar being worthless soon, you can still take the 2mil/mo and diversify. After a year you should already have plenty money to live comfortably for the rest of your life.

One field it impacts is radio astronomy. We can already see Musk’s satellites mess with it (unintentionally) and it’s probably only going to get worse from here.

It didn’t play the animation for me (only the comments made me realize it was meant to be animated).

Him just standing there NOT dancing made this so much more funny and relatable to me.

Assuming each user will always encrypt to the same value, this still loses to statistical attacks.

As a simple example, users are e.g. more likely to vote on threads they comment in. With data reaching back far enough, people who exhibit “normal” behavior will be identified with high certainty.

In my experience, it is good at simple to medium complexity regex. For the harder ones it starts being quite useless though, at best providing a decent starting point to begin debugging from.