No, that problem was not generated by Airbnb. There’s growing demand and, because of regulation, not enough is built every year. For example, according to INE, 250k new families are formed in Spain (more than 500k people) and only 100k new houses/flats are built and the yearly deficit has been accumulating for 12 years. That is the real issue and blaming corporations is just the politicians’ easy path to deflect blame, which unfortunately too many citizens eagerly buy into.
Even if it were true that the problem of housing affordability was not affected by Airbnb (it's not, at best it only exacerbated an existing problem), that would not mean it didn't create other problems for cities. Having tourists concentrated in places that are not designed for it, where a hotel license would never have been issued; the problem of too many tourist accommodations, causing an overflow of tourism; problems for neighbors with parties and similar nuissances; problems with untaxed income from the smaller owners; and probably others I'm forgetting.
I know the Spanish case. The hotel lobby does not only push for airbnb restrictions but also for banning new hotels (so that they can push prices up). For example famously there are no new airbnb or hotel licenses since more than 10 years and obviously the problem has only worsened. Regardless, the problem is in general still a demand that grows roughly at 2.5x the supply growth rate.
Are the boulevards, museums, parks etc growing as well? If not, why should the number of hotels grow? A city generally has a limited capacity for tourism (one which is not primarily related to the number of hotel rooms) and once that is reached, it becomes detrimental to increase tourist accommodations. I don't know if this is the specific case in one specific city that you have in mind, but the principle is there - expanding tourist accommodations is not a good in and of itself. Supply is naturally constrained, regardless of demand, for many real goods, and this is one of them.
Additionally, you keep ignoring the fact that even if new housing supply would be very important, Airbnb is still a drain. If demand was outpacing supply 2.5x before Airbnb, and it's outpacing it 3x now, that is still Airbnb making a bad problem worse.
Externalize all the problems, but its "not cheating!" - its just making us all pay for your growth while you take advantage of the current structure of society and generally making things worse.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
What a cool idea. How does it work? AFAIK The human brain at least does sparse backprop and has SOME neural circuits that feed-backward, so how do you manage it without anything?
Thanks! I have other ideas, following Jeff Hawkins's Thousand Brains Project, but in this one I'm trying to get to cortical columns from the other side, from "standard" deep neural networks.
The short version: each layer trains itself independently using Hinton's Forward-Forward algorithm. Instead of propagating error gradients backward through the whole network, each layer has its own local objective: "real data should produce high activation norms, corrupted data should produce low ones." Gradients never cross layer boundaries. The human brain is massively parallel and part of that is not using backprop, so I'm trying to use that as inspiration.
You're right that the brain has backward-projecting circuits. But those are mostly thought to carry contextual/modulatory signals, not error gradients in the backprop sense. I'm handling cross-layer communication through attention residuals (each layer dynamically selects which prior layers to attend to) and Hopfield memory banks (per-layer associative memory written via Hebbian outer products, no gradients needed).
The part I'm most excited about is "sleep". During chat, user feedback drives reward-modulated Hebbian writes to the memory banks (instant, no gradients, like hippocampal episodic memory). Then a /sleep command consolidates those into weights by generating "dreams" from the bank-colored model and training on them with FF + distillation. No stored text needed, only the Hopfield state. The model literally dreams its memories into its weights.
Still early, training a 100M param model on TinyStories right now, loss is coming down but I don't have eval numbers yet.
The idea is that the brain uses what the authors refer to as "feedback alignment" rather than backprop. Even if it turns out not to be literally true of the brain, the idea is interesting for AI.
I also love the idea of grafting on the memory banks. It reminds me of early work on DNC's (Differentiable Neural Computer's). I tried to franken-bolt a DNC onto an LLM a few years back and mostly just earned myself headaches. :)
It's fun to see all the wild and wacky stuff other folks like myself are tinkering with in the lab.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
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