Even if it was (a lot of similar designs are due to common inspirations such as folk tales, but also a lot are direct parodies, and parodies can be made legally distinct) (but also, the Pokemon team itself rejected the copyright suit suggested by the public in their own public statement a while ago) the lawsuit is over parents, not copyright. Patents implies technological theft. Or bogus patent claims, more often than not. (Patenting math should be illegal, for instance.)

I don’t believe I’ve read either. I’ll put those on my list. I’d love to be proven wrong. Usually I’m more into light hearted things, which doesn’t necessarily seem to be Steinbeck’s wheelhouse (I can be sad in real life, after all), but that doesn’t mean I can’t enjoy more down to earth or down right depressing works.

You know what. I haven’t read him in a very long time. I’ll get back to you. Do you have a favorite? (I’m vetoing The Red Pony.)

Edit: Guessing The Grapes of Wrath. I remember not liking it as a kid. But at least it wasn’t The Red Pony.

The one that sticks out for me is On The Sidewalk Bleeding. Wasn’t even the most effed up thing we read in 9th grade, just had the most memorable name.

I still hate Steinbeck as an author. Don’t think that would have changed if I had read any of his works later in life.

Of Mice and Men? But I think it was a rabbit usually.

Not necessarily the case, but if it’s affecting your life so strongly, you might want to get checked by a medical professional.

Long COVID can destroy your life. Depression can destroy your life. Iron deficiency can ruin your life. A lot of things you might just think is just being tired may actually have a cause. Especially if simple fixes like “touch grass” style clichés do nothing for you.

It’s not always the answer, but it’s good to rule out in that case.

Our cats use it to beg for treats. Very rarely do I see them on it and not meowing for attention.

It’s valid, and it sucks. If you can even do $5, it’s worth it. But the world is absolutely against you right now. A lot of older folk don’t quite get how bad it’s gotten.

However, saving a dollar today is worth more than saving two dollars ten years from now. And having an emergency fund might actually save your life.

Hopefully something happens to shake up housing. These prices are absolutely criminal.

If I hadn’t saved, I probably would be dead right now. The US doesn’t really do healthcare or mental care, and I no longer can sustain myself. Long COVID is a bitch and doctors usually ignore it.

But if you’re banking on never having an emergency, go for it. There’s a balance to hit, at least in less developed countries like the US.

1. Done. Rewritten a few times. Fleshed out a bit.
2. Learning the game engine real fast, as I haven’t used Godot before. But yes, that’s the plan. I have a minimal game loop I want to hit as the first target. And it’s not too much farther than the tutorial result I’m looking at + the main hook gameplay element of the game.
3. Bounced the idea at least off people and they sound willing to jump into this.

And of course that’s where the trail ends until it’s vetted enough to move forward.

Nice to see it kind of laid out. Still don’t know how to get past the hurtle of my brain no longer working, but maybe I can still do it… Just slowly.

There are games I want to make. I caught long COVID and barely had energy for my job. I decided now that I got laid off for having an invisible disability, I can learn how to make games while I can’t get a new one, but I’m having issues thinking long enough to learn… I’ve almost started my game and that’s where I’m stuck.

That’s LLM bull. The model already knows hangman; it’s in the training data. It can introduce variations on the data, especially in response to your stimuli, but it doesn’t reinvent that way. If you want to see how it can go astray ask it about stuff you know very well, and watch how it’s responses devolve. Better yet, gaslight it. It’s very easy to convince LLMs that they’re wrong because they’re usually trained for yes-manning and non confrontation.

Now don’t get me wrong, LLMs are wicked neat, but they don’t come up with new ideas, but they can be pushed towards new concepts, even when they don’t grasp them. They’re really good at sounding sure of themselves, and can easily get people to “learn” new “facts” from them, even when completely wrong. Always look up their sources, (which Bard (Google’s) can natively get for you in its UI) but enjoy their new ideas for the sake of inspiration. They’re neat toys, which can be used to provide natural language interfaces to expert systems. They aren’t expert systems.

But also, and more importantly, that’s not zero-shot learning. Neat little anecdote from a conversation with them though. Which model are you using?

No. AI and, what you’re more likely to be referring to, machine learning has had applications for decades. Basic work was used back into the '60s, mostly for quick things, and 1D data analysis was useful long before images (voice and stuff like biometrics). But there are many more types of AI. Bayesian networks (still in the learned category) were huge breakthroughs and still see a lot of use today. Decision trees, Markov chains, and first order logic are the most common video games AI and usually rely on expert tuning rather than learned results.

AI is a huge field that’s been around longer than you expected, and permeates a lot of tech. Image stuff is just the hot application since it’s deep learning based buff that started around 2009 with a bunch of papers that helped get actual beneficial learning in deeper models (I always thought it started roughly with Deep Boltzmann Machines, but there’s a lot of work in that era that chipped away at the problem). The real revolution was general purpose GPU programming getting to a state where these breakthroughs weren’t just theoretical.

Before that, we already used a lot of computer vision, and other techniques, learned and unlearned, for a lot of applications. Most of them would probably bore you, but there are a lot of safety critical anomaly detectors.

This actually is a symptom from the sort of “beneficial” overfit in Deep Learning. As someone whose research is in low data, long tails, and few shot learning, there’s a few things that smaller networks did better in generalization, and one thing they particularly did better (without explicit training for it) is gauging uncertainty. This uncertainty is sometimes referred to as calibration. Calibrating deep networks can yield decent probabilities that can be used to show uncertainty.

There are other tricks for this. My favorite strategies prep the network for learning new things. Large margin training and the like are a good thing to look into. Having space in the output semantic space (the layer immediately before the output or earlier for encoder decoder style networks) allows for larger regions for distinct unknown values to be separated from the known ones, which helps inherently calibrate the network.

Which end? The main story is just a narrative device, in fact you shouldn’t really obey the narrator at all. Calling any end “The End” doesn’t make sense in the context of the game, really. Unless you just broke out of the mind control facility three times then called it quits? That end is supposed to be non enticing so that you try literally anything else before putting it down. I think the going insane end sticks with me the most. Although the game dev commentary in the recent release is fun.

Well drat. I suppose this post is going to fade away soon.

Just kitten.

Science is pushing the bounds of human knowledge. Science is only science if it propagates, otherwise it’s just someone’s discovery. Science has to be built upon, even if it’s disproven, that means it was documented well enough to be built upon. That’s not to say everything that’s disproven is science, because crackpot theories don’t often push the bounds of human knowledge.

I hope the brilliant students get their knowledge out there. (But that is unfortunately hard in academia. Despite us living in what should be a post knowledge scarcity society, we clearly aren’t.)

This is why the machine learning community will go through ArXiv for pretty much everything. We value open and honest communication and abhor knowledge being locked down. This is why he views things this way. Because he’s involved in a community that values real science.

ArXiv is free and all modern science should be open. There were reasons for publications in the past, since knowledge dissemination was hard, and they facilitated it. Now the publications just gatekeep.

This is a fair question. But also, we’re talking about one of the most influential minds in deep learning. If anything he’s selling himself short. He’s definitely not first author on most of them, but I would give all my limbs to work in his lab.