Quick numbers: monthly take home on $800k in CA is $33k (40% tax rate!). Monthly payment on $3M according to Zillow is about $20k a month. That leaves $13k for all family expenses and saving for retirement. In Seattle, our credit card statement averages $8k, so I wouldn’t feel great about that margin.

> Even as Summit County adds waves of remote workers, it has experienced net negative migration since 2020

You click the link of that data and it talks about how the local government is desperate to attract workers and full time residents, rather than having mostly vacant second homes.

I presume they want to attract wealthy workers, given the choice, since that spending will be local and help the economy. So, assuming they get what they want, which is an influx of wealthy, employed people, it’s entirely clear what will happen to real estate prices. I’m sorry, but no one ever said you’re entitled to only having to play guitar in coffee shops to support themselves. That’s what college kids do, who don’t own houses.

And in an irony of ironies, you crawl through my post history and take objection to me expressing my lack of empathy for those that make repeated bad financial decisions, but you're not only defending that very sentiment, but taking it a step further: enjoyment of the suffering. I hope you find peace, brother. I'm going to put my robes back on and get those last few meditation hours in now.

A much more credible threat are humans that get other humans excited, and take damaging action. Yudkowsky said that an international coalition banning AI development, and enforcing it on countries that do not comply (regardless of whether they were part of the agreement) was among the only options left for humanity to save itself. He clarified this meant a willingness to engage in a hot war with a nuclear power to ensure enforcement. I find this sort of thinking a far bigger threat than continuing development on large language models.

To more directly answer your question, I find the following scenarios equally, or more, plausible to Yudkowsky's sound viruses or whatever. 1/ we are no closer to understanding real intelligence as we were 50 years ago, and we won't create an AGI without fundamental breakthroughs, therefore any action taken now on current technology is a waste of time and potential economic value; 2/ we can build something with human-like intelligence, but additional intelligence gains are constrained by the physical world (e.g., like needing to run physical experiments), and therefore the rapid gain of something like "super-intelligence" is not possible, even if human-level intelligence is. 3/ We jointly develop tech to augment our own intelligence with AI systems, so we'll have the same super-human intelligence as autonomous AI systems. 4/ If there are advanced AGIs, there will be a large diversity of them and will at the least compete with and constrain one another.

But, again, these are wild speculations just like the others, and I think the real message is: no one knows anything, and we shouldn't be taking all these voices seriously just because they have some clout in some AI-relevant field, because what's being discussed is far outside the realm of real-life AI systems.

For example, we don't understand fundamentals like these: - "intelligence", how it relates to computing, what its connections/dependencies to interacting with the physical world are, its limits...etc. - emergence, and in particular: an understanding of how optimizing one task can lead to emergent ability on other tasks - deep learning--what the limits and capabilities are. It's not at all clear that "general intelligence" even exists in the optimization space the parameters operate in.

It's pure speculation on behalf of those like Hinton and Ilya. The only thing we really know is that LLMs have had surprising ability to perform on tasks they weren't explicitly trained for, and even this amount of "emergent ability" is under debate. Like much of deep learning, that's an empirical result, but we have no framework for really understanding it. Extrapolating to doom and gloom scenarios is outrageous.

And I’m so tired of this “transformers are just GNNs” nonsense that Petar has been pushing (who happens to have invented GATs and has a vested interest in overstating their importance). Transformers are GNNs in only the most trivial way: if you make the graph fully connected and allow everything to interact with everything else. I.e., not really a graph problem. Not to mention that the use of positional encodings breaks the very symmetry that GNNs were designed to preserve. In practice, no one is using GNN tooling to build transformers. You don’t see PyTorch geometric or DGL in any of the code bases. In fact, you see the opposite: people exploring transformers to replace GNNs in graph problems and getting SOTA results.

It reminds me of people that are into Bayesian methods always swooping in after some method has success and saying, “yes, but this is just a special case of a Bayesian method we’ve been talking about all along!” Yes, sure, but GATs have had 6 years to move the needle, and they’re no where to be found within modern AI systems that this thread is about.