I'll admit blockchains (while they may have potential for the future) don't currently have much use in the real world, but saying the only people it helps are criminals is just that old flawed argument that undermines privacy which in turn only benefits oppressors

I didn't say the only people it helps is criminals. I said that's the only example I've seen in the real world. If you have more I'd be happy to hear about them.

Sending money to family in another country with crap financial infrastructure

I personally attribute this FOMO to so called AI influencers who love "shilling" AGI as something that's as true as 1 + 1 = 2

I don't get why people insist this is agi any more than a ship is artificial general swimming.

It doesn't matter if it's general, what matters is that its useful. And if you don't find it useful just remember a lot of people in the 00s didn't find google useful either since they already had the yellow pages.

I strongly suggest paying for a subscription to either openai or anthropic and learning quickly.

> learning quickly.

Learning what quickly exactly?

You don't even have to do that, just go to http://ChatGPT.com and type at it. you don't even need to make an account.

You get what you pay for, despite what everyone is saying the 4o gpt model is really bad for long form reasoning.

Buy the subscription and use the turbo4 model.

After that api credits so you get access to the playground and change the system prompt. It makes a huge difference if you don't want to chat for 10 minutes before you get the result you want.

Swimming and 'intelligenting' are definitely not in the same category.

AI is not and most likely will not be a "magic box that meets all your needs" in at least 1000 years unless something insane happens, AI does function approximation, when you scale it up it can do amazing things but you still need human creativity, you still need engineers because it can only output data not act on real-world stuff, you still need data that is generated by humans, you still need to train every model for your specific task, and tasks are infinite.

If we wanted to replace humans we'd have to fully figure out how the human brain works first, for now no one knows how the human brain truly works so how can we claim AI will replace all humans?

In my opinion, AGI is just an acronym pushed by people who have something to gain from mass hysteria, FOMO, and making people feel like "the end is near", AKA, AI influencers and founders who want investors to give them money.

Of course, this doesn't mean everyone who believes in AGI is one of the two, most were probably just fed this narrative and it sounded believable so they just went with it.

These are my two cents, sorry for the wall of text :P, but I just had to say something about what I perceive as an "AI bubble" that generates mass panic for most in order to benefit a few

Many of the day-to-day tasks that keeps people regularly employed are not creative tasks, but repetitive and predictable.

Sure, repetitive tasks may get replaced in the short time depending on the complexity, but, the way I see it, there will always be a human need, new jobs of higher complexity and creativity will be created, and maybe there will be less jobs overall, but predictable jobs being replaced always happen with new advances in technology, whether that's a good thing or not :)

> always be a human need

In what quantity though? In Willy Wonka, when Charlie's dad got laid off at the toothbrush factory and was later rehired as the sole toothbrush automation inspector, what happened to his colleagues? Bigger and better things? Unlikely, unless their kids also suddenly inherited chocolate factories.

It's a decimation of the workforce, and we're still climbing the population curve.

I'm looking into learning more about ML and how I can create my own models. I enjoy math, but when I read research papers the formulas sound like gibberish. I also like the idea of Kaggle competitions, do you think I need to have research-level math understanding of ML models to do well on Kaggle?

Yes, I believe you do need to have a good math understanding to understand why a model is behaving the way it is and develop intuition into where the problem might be but that comes with experience. If you're looking into ML research then absolutely you need to learn the math but if you intend to support ML as a programmer then there's plenty of space for you to do so

Thanks for the response! I'm torn between AI research and just implementing models with my own data, I do enjoy math but I can't seem to understand advanced math, that might change after I get an engineering degree though, or not ¯\_(ツ)_/¯

But for the time being, I'm just wondering if people who win Kaggle competitions implement their own algorithm, or do they just read a lot and try techniques that are already out there?

Good luck in your studies and plans!

I can't answer you about Kaggle competitions as I never was involved in any but I imagine the novelty would be in the way people encode the datasets and maybe the cost functions they implement but the models themselves are probably based on already established archs

Does anyone know why the blur effect always takes so much power? Is there not a way to use the GPU, or is the problem something else entirely?

You can tell CSS to use GPU by adding `transform: translate3d(0, 0, 0);`

Explanation of slow CSS filter performance is in this video: https://www.youtube.com/watch?v=oie6KqSPPlE