1,704 karma · joined February 6, 2018
meet.hn/city/us-Conshohocken
Interests: Data Science, Art, AR/VR, AI/ML, Fintech, Gaming, Hacking, Hardware, Philosophy, Programming, Science, Space Tech, Web Development
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It would be straight forward to require companies over a certain size to issue and distribute some number of shares based on current pay to employees. Existing shareholders will have their stake diluted but are welcome to buy more shares on the market while employees are welcome to sell or hold. If the investors are actually good at allocating capital, they should have plenty of profits from previous investments to maintain their % ownership despite the share inflation. If they aren't, then they should find a different job.
Under this scheme, Bezos would still have become a billionaire, and we know this because Amazon and tech companies already do this exact thing by offering stock options!
And the neat thing is, when a company does a stock-buyback, this is literally the same giving a dividend of the profits to employees.
https://chatgpt.com/share/4298efbf-1c29-474a-b333-c6cc1a3ce3...
Yeah, it could be a spurious input though. My understanding is that quantum mechanics doesn't really matter at biological scale, and that kinda makes sense right? Like, if this whole claim about biology being reducible to the topology of the components of the network is true, then the first thing you'd do is try to evolve components that are robust to quantum noise or leverage it for some result (ie: one can imagine some binding site constructed in such a way that it requires a rare event that none-the-less actually has a very specific probability of occurring).
> and does not change the analysis of what the UAN computes? It seems difficult to think that what a UAN computes is not impacted by those lower level details (meaning specifically quantum effects, I'm not thinking of just alternate implementations).
What the UAN computes is impacted by those lower level details, but it is abstractable given enough simulation data.
ie, imagine if you had a perfect molecular scan of a modern CPU that detailed the position of every atom. While it would be neat to simulate it physically, for the purpose of analysis, you'd likely want to at least abstract it to the transistor level. The 'critical topology' is I guess, the highest possible level of abstraction before a CPU tester can tell your simulation from an atom-level simulation.
Now for CPUs, we designed that model first and then built the CPU. In biology, it evolved on the physical level, but still maps to a 'critical topology'.
I always hear that the brain doesn't do back-propagation the way that NNs are trained, but why are we so sure of that? This reads to me like a potential communication channel for doing backprop.
iirc browsers fuzz the precise timing of calls for exactly this reason already?
I learned 3d modeling in 3ds Max as a kid, but many years and a CS degree later, I ended up switching to Blender because the license cost of 3ds Max was absolutely mental for a hobbyist ($3,000ish?). This was after the Blender renaissance where they overhauled their UI and stuff. But it wasn't my first time trying to switch to Blender from 3ds Max. In terms of features and workflow, both are very similar (and I actually like some things in Blender more), but it's the little differences that end up bugging you in my experience. Like some alt-mode of a feature being missing. Or the camera handling differently from how you're used to.
I'm currently going through this again now, switching from Unity to Stride game engine. At a quick glance, they're extremely similar, but that's almost worse. It's like coming home to your house and finding that it was replaced with an identical house.
I can tell you that many people would choose the first.
I'm under the impression that the supply-chain for Ukrainian drones basically leads to China in the same way that it does for Russia. For a "small" regional conflict, this isn't a problem to Ukraine because there's no way China could or would restrict supply of their cheap drones. But for a large-scale conflict, it would be a problem for the US to not be able to source drone motors by the 10,000's.
The cost estimates for those are quite wide, but in terms of raw materials even those low-cost prices are kind of absurd right? $20,000 for a few motors, batteries, basically a modern smartphone and 20-40 pounds of explosive? The military expects that they will get a lot cheaper, which means you need to be able to counter them at least as cheaply.
> You cannot compete with China even if we annexed Mexico for cheap labor in order to manufacture high volume low profit drones.
DJI isn't making drones by hand, they have automated factories. But its only worth building an automated factory if you're selling at a massive scale. Banning DJI drones in the US lets you build a factory in the US that can eventually get costs down.
And it's also dumb to fund your opponent's war production lines.
1. Walmart opens location
2. Prices things to get a small margin
3. Competition is driven out because consumers don't want to go to multiple stores to get everything they need
4. Suppliers raise costs, inflation happens, market conditions change, etc. (it would even make sense for the supplier to raise prices if they have less independent customers, to account for the risk of only having 1 big customer)
5. Someone writes an article about how X is more expensive now at Walmart than it was at random shop, years ago.
Land is made desirable mostly from what productive use it has. ie: being near jobs. There's not really any hard work any one individual can do to make some random plot of land in Kansas more valuable than its current market price.
> participate in local government to help protect the value of that land.
This is a nice euphemism for blocking new construction and restricting housing supply.