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.
Then why are you saying it's incorrectly formatted? I'm directly backing its premise.
Except, usage defines language. If it didn't, English wouldn't exist. Therefore, usage is correct when people understand and use it.
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.
He is. Just about anyone who works in computer vision based machine learning knows this man. He's insane and I would hire him on the spot, but there's no way a company I work for could afford what he's worth.
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.
In addition to Aezora's response, extrovert vs introvert being a description of your attitude to socializing is only a colloquial use of the term. I am a shy extrovert. I do not get social energy by being alone, like an introvert does, and I have problems talking with new people and even with friends prefer a back seat in the conversation.
Most people seem to fit into more clear buckets, if you believe the marketing, but that doesn't make the buckets the definition.
Our cats use it to beg for treats. Very rarely do I see them on it and not meowing for attention.