Are you saying that we should allow a Uber to do whatever they want because of scale they are operating? It’s Uber choice to operate at scale. No one forced them to.
This is pure revenue extraction from American tech. The EU now makes more in fines from American companies than it does in taxes from its own tech industry. It is clearly impossible to fill these roles with humans without breaking the economic case for Uber, which is obviously the goal. Europe is so fucked man.
> Sorry how else are they supposed to do it at Uber scale?
The people at Uber are ubersmart, so they can figure it out. If they can't, they don't know how to run their business in line with the law and should be fired.
Figure out how many humans are required to make the decisions, then hire that many humans and have them do it.
I swear this is like a restaurant chain complaining that they shouldn't be required to follow foodservice regulations because of how much money it costs them at scale.
What an unusual situation, a big company that has to manage infraction reports from their employees. I don't know... how does Starbucks, McDonalds, Volkswagen or Carrefour do? Do they have, maybe, managers or a human resources department? I'm just talking out loud.
Just how many cases are there? Surely not actually that great number that some employees can not manually go over the details. Maybe cut the developer and executive pay by say 20% and you can easily cover cost of those employees.
Yeah, I find the notion that human judgement is superior to algorithmic judgement to be pretty suspect… look at Jens Ludwig’s work on judges, it’s not like involving a human improves outcomes somehow
For large corporates and other entities of any size, the threat of the core of your infrastructure getting suddenly disabled because of something like this is going to be untenable. I predict the pressures for on-prem, offline access (whether by licensing weights or getting them in a restricted setting like TEE/CC) will be overwhelming and one the players will fill the need.
This is precisely why I expect that Chinese open models are going to win in the long run. The capability difference isn't dramatic in the grand scheme of things, but the fact that you can run your own is a huge selling point. Even if you rent an open model from a Chinese company, you can switch to on prem if they decided to yank access or change terms in the way you don't like. It might be a pain, but it wouldn't be existential. On the other hand, if you become dependent on a closed model and it gets yanked then you're in a world of hurt.
And infrastructure dominance is really the big picture here. Chinese models are going to become the standard setters because they're going to be what people are using. That means more research, more tooling, and a whole ecosystem developing around them.
After this action, I have no doubt that this administration will try to ban Chinese models. Of course, doing so will be futile, we'll figure out ways to get around it, but now I'm pretty sure they're going to try.
I'm waiting for that to happen as well since the price difference makes it very difficult for companies like Anthropic and OpenAI to compete. And we already have precedent for this with stuff like EVs, phones, and so on. As soon as Chinese companies start making a product that's more popular, they get banned on some national security pretext.
The tricky part with banning Chinese models is that they're open. It'll be easy to ban access to service providers, but preventing people from running these models on prem is going to be really tough. Like are they going to go after Cursor for example given that their model is based on Kimi?
I very much agree it's going to be a futile endeavour in the end. It kind of reminds me of the time Microsoft tried to get Linux and open source banned when Linux started encroaching on Windows server market. This is going to end the same way.
I'm going to guess they'll go after sites like Huggingface that host downloads. I suspect we'll be torrenting Chinese models in the not-too-distant future. Or we'll have to geo-spoof with VPN to download from other countries.
It is almost certain that the CCP will impose constraints on access to their models at some point too. But Trump is doing it to extort cash from Anthropic, and China will be doing it to leverage political and economic concessions.
Remember that there are degrees of banning. Slower tokens, dumber models, token caps, KYC for each model consumer, hurting specific companies that are not capitulating in a deal with a Chinese company, etc.
A big difference with open models is that anybody can run and tune them any way they like. The real difference in philosophy is that Americans companies treat the model as the product, while Chinese companies see models at infrastructure you build products on top of. You amortize the cost of deploying it at scale by sharing knowledge and iterating quickly to bring the cost down.
I see absolutely no reason why CPC would choose to kneecap themselves the way the USG just did. Keeping open access to the models means that the whole world will be using Chinese based AI stack going forward. Only a government run by absolute imbeciles would do what the US did.
Even if everyone uses Chinese open weight models at somw point, how do you make money creating them?
This is just typical Chinese behavior. Flood the market with cheap or free stuff and wait for your competitors to die off. Then you have a monopoly. (Maybe you were implying that would happen, dunno)
I mean you could ask the exact same question about other foundational tech like Linux. And the real question here is what stops Americans companies from producing things cheaply and at scale the way Chinese companies do. You frame it as some nefarious tactic, but the reality is that they're just more efficient and American companies are unable to compete with that.
The Chinese government grossly subsidized many industries to undercut their competitors and form monopolies, such as solar panels, electric cars, battery cells, and steel and aluminum manufacturing, not to mention rare earths. Yes, they are more efficient, I don't disagree with you there.
But they aren't making any money releasing open weight models, and no, I don't believe they are like Linus Torvalds with some grand vision of free as is freedom AI models, but rather doing more of the same.
The US government has also grossly subsidized many industries. Tesla literally wouldn't exist without subsidies. However, the whole premise behind capitalism and markets was that this system was inherently more efficient than state planning. Now we see that narrative is false because China's planned system is winning across the board.
And that's the beauty of a planned system, you don't have to focus solely on short term profit there. If Chinese government sees this tech as foundational, which they do, they can just keep pouring money into it because it provides general benefits for the country.
This is exactly what happened with high speed rail incidentally. People in the west kept talking about how unprofitable it is, while China understood that creating this infrastructure would create a huge economic boost for the country by allowing goods and people to move more easily. Sure enough, China continued to invest in high speed rail, and now a lot of regions that used to be hard to get to are wired into the economy, and China is seeing substantial development in these regions.
Yes, I agree. I'm not saying China is bad doing this, it's just this is what they are doing IMO.
Spending huge amounts of money to gain a foothold in AI because it helps their country is not a bad thing at all.
But my point is that the day that the USA's AI industry collapses in on itself, Chinese companies are going to also stop open sourcing their models, too.
It may sound like I'm kicking China for this, I'm not. It's just a practical take. You can't make money spending 10s or 100s of millions of dollars just to then give away everything for free and expect to make money long term. Do you agree?
I actually expect they will keep making the models open and we'll likely converge on one or two alternatives because there's really not much difference between them at the end of the day. Everybody curating their own model is a huge duplication of effort with no clear benefit. There's a reason everybody doesn't roll their own operating systems for example.
It all comes back to what I said earlier. If you treat the model as the product, then it makes sense to keep it closed. You have some secret sauce that nobody else has, and you sell it. But the reality is that nobody has a magic formula that's significantly better than what other people can figure out. You might get an advantage for a few months tops, and then other models start catching up.
And this creates involution where you just have a race to the bottom where nobody makes any money. On the other hand, if you treat models as infrastructure, and everybody contributes to the same pool of knowledge, then you amortize the cost of making a better model. The money comes from actual products that can genuinely differentiate themselves. Companies are going to seek niches they can dominate where they do a specific thing really well. That's a much more realistic path towards long term sustainability.
And that's why I expect models are going to become infrastructure akin to Linux in the long run. They're just not where profit is.
The difference is that making Linux doesn't require a lot of money to do. Most of it is written by people for free in their spare time. They do it because they like to solve complex puzzles and be recognized within the opensource community.
OTOH, training a model requires a lot of hardware and energy to do, and the money has to come from somewhere.
Do you think that China's government is going to pay for it and release it openly to the world for the purpose of goodwill towards China, or some other reason? What would it be? Or would some other groups do it?
Linux gets a lot of funding and corporate development done actually. And training models is also getting cheaper every year. There are also projects like Petals which allow you to train models in a distributed fashion https://github.com/bigscience-workshop/petals
And yeah, I do think China's government is going to continue subsidizing this tech because this tech is being used all over the place in China now. Meanwhile, the models aren't developed by the government, they're developed by individual countries that get subsidies. As I've explained above, converging on common infrastructure is going to save resources for all these companies. And continuing to work in the open with the rest of the world means getting the benefit of having a global community of researchers helping advance this tech forward.
It's not just altruism or clout. American companies working on closed models have to foot the bill for all the research, and they're limited to the brainpower within the company. And they're competing with Chinese which have much bigger research community contributing to developing their models.
If the model itself is not the product, then American companies find themselves in a situation where they're spending a ton of resources on something that's not their core business.
Thinking that on prem models will be a halfway decent solution against what can be served out of a data center is a fools take... One that is more common than it should be on here...
The point is not to be as good as the multi-trillion parameter model you can host in across 72 GPUs (or whatever).
I'm running a 248B model on a paltry amount of hardware and getting plenty of good use out of it.
Sure, the most demanding tasks will demand the best models (and always will). There's still less demanding tasks for other models.
I think some people are fooling themselves that coding of all tasks is always going to requires the biggest models ever. Again, maybe some coding tasks will, but the majority of business CRUD apps probably don't. Same goes for virtually any other type of task. The biggest models are really only useful for the most complex tasks.
If you wouldn't mind, could you explain a bit what the 248B model is good for, and where it breaks down and you need something better? I hear this take often, but it is always a fleeting remark so I have no idea what the 'useful' looks like - at all.
To answer this and my sibling, it's DeepSeek V4 Flash at native FP4 quantization, on two Nvidia DGX Sparks. Which is a bit of kit but still paltry relative to the data centre. ~40 TPS generation, ~2000 TPS prompt processing, which makes it feel approximately as fast as typical APIs.
I primarily use it with my own harness for coding. I'm not going to say it will compete with Opus in the most challenging domains, because it won't, but I will say that there's a reasonable likelihood that Opus is used for tasks that a model like Flash could comfortably handle at 1/100th the cost.
So far I've only seen it struggle at tasks that I myself would struggle with. Tasks that I can describe the shape of the solution for, it has a high success rate at implementing.
Useful is going to be different for everyone. I'm not working on the hardest problems, I don't need the best models.
In my experience they require much more hand holding and more specific directions with less possibilities to interpret a command in several ways. You do the planning, keep on eye on that they're producing and they do the legwork. It's not that their knowledge of Java or PHP or what have you is lacking, it's the long horizon planning that you have to do yourself. Technically they're good. You just have to do more thinking and more reviewing yourself. YMMV.
Depending on quantization I figure they need at least a p4 and likely a p5 EC2 (or similar instance in another provider) for a model with that many parameters. Maybe they are hosting on bare metal but I imagine not. Those instance types (assuming not using spot) are quite expensive to run.
It’s perfectly reasonable to believe that a law of marginal decreasing returns will kick in at some point (if it hasn’t already), and that what one point looked like an exponential may start looking like an s-curve.
I do not see how being experienced in engineering, or having higher studies in computer science and economics should make that view less common.
Some will take greater risks and win (or lose); others will play it safer and slowly accumulate wins (or be obsoleted).
Never mind the threat of letting these models write code that runs your business, or operate it agentically. Models trained by actors (corporate or nationstate) diametrically opposed to your interests.
Lots to take into account now, interesting time to be in business.
Or abstract i.e. openrouter, that reduces the risk vector to "all implementations have been simultaneously banned".
If a government entity bans a LLM provider due to a jailbreak concern, they can also ban an on-prem solution under the same guise. The jailbreak risk exists regardless of where it's hosted. You could defensibly argue the on-prem risk is higher since frontier model companies can justify safety spend due to their size, it's more difficult to combat bad actors if you're company is the only one using the model and you don't have economies of scale.
This is ignoring the fact that the government is the foundation of society (I know some will disagree with that, but the end result is just government with more steps).
Private models in a low trust society means the government will come and seize the models. Competitive business will only be allowed through cronyism.
The better option is to opt for high trust. Yes the Gman can rip your servers apart, but they know they'll face consequences, legal and political. Laws and regulations are the answer, not locking down into smaller fiefdoms.
You get high trust through social norms, not by more "laws and regulations". Social norms can't be imposed by fiat, they arise spontaneously, often for unclear reasons. That's why they're so fragile and precious. With Trump's destruction of social norms around the presidency and the federal government generally, the US is now just another country where bribery is the cost of doing business.
Great point. That is what all the Fortune 500 CEO's are frothing at the mouth about. Having LLM's replace their payroll. So yeah, they deserve to fail.
Personally knowing four people with GBM and not being an oncologist is exceptional and worrying. That feels like it should be raised to… someone. It is very possible something bad is happening and a commonality needs to be tracked down urgently.
It is indeed very strange, none of these people lived in the same place or got diagnosed at the same time (some 20 years ago). Most are fairly remote to me though... Like : a good friend's mom, or my wife's cousin's husband, etc.
I knew two people (one adult, one child) who lived in the same building and got diagnosed with leukemia (~14 per 100,000) shortly after each other, maybe within a few weeks.
It's anecdotal, of course, but I've always thought that there could have been a connection.
It could be birthday paradox, but it could potentially be enough to get someone to come check for contaminants.
Looks like benzene, some pesticides, and formaldehyde are the common workplace exposures that can trigger leukemia. But some of those can turn up near housing.
> benzene, some pesticides, and formaldehyde are the common workplace exposures that can trigger leukemia.
That rings a bell. I remember that someone mentioned a recent repainting of the building. The incident happened at least a decade ago, so I can't remember all details.
I just finished Silent Spring last fall and was shocked to learn they already knew DDT and other pesticides were causing leukemia back in 1960. I guess PR from the chemical companies is working, because I would have guessed 1979-1985.
We have a gaussian splat component (same format that it looks World lab uses) for A-Frame that should work in VR on Quest and Vision Pro in their respective Web browsers. Quest FPS might not be ideal yet
I was searching mostly under the News section of google although this seems to just be a Tiktok feature "restricted mode" that Tiktok may have accidentally enabled for some users?
Fun story I heard recently: apparently a bunch of pigs were placed on various small islands in the pacific in the 19th century so in case sailors were running out of food, they would have a known self-sustaining backup option.
Many of those pigs were completely isolated for over a hundred years and entirely missed out on the globalization of tons of infectious diseases.
A while ago a group of them were picked up and brought to a special protected refuge in New Zealand, where they are being used for artificial organ research (https://nzeno.nz/) basically for the reasons you highlight.
Am I just crazy? Do others not wonder that if pigs and humans have remarkably similar biology, then this increases the chance that pigs and humans might also have similar mental features? (I'm not claiming we're equally intelligent, just that it's well known that pigs are [quite intelligent](https://en.wikipedia.org/wiki/Pig#Behavior):
> Pigs are highly intelligent animals,[64] on par with dogs,[65] and according to David DiSalvo's writing in Forbes, they are "widely considered the smartest domesticated animal in the world. Pigs have demonstrated the ability to move a cursor on a video screen with their snouts and understand what is happening onscreen, and have learned to distinguish between the scribbles they had seen before and those they were seeing for the first time."[66][a][70]
But forget about the capacity to process information. How do we expect to improve as a society, in terms of treating other humans in a non-disposable fashion, if we regularly treat animals in a disposable fashion, given that a standard tactic in feeling okay about mistreating humans involves reducing ("de-anthropomorphizing") humans (via comparisons, propaganda, statistics, etc.) to be like "other animals"?
Suppose aliens were to visit us one day, and sci-fi-magically were to give certain animals a voice and a say, and those animals advocated for giving humans what they got from them: putting humans on little islands, where they multiply, and then a few years later, animals/aliens/etc. come by to use them as transplant sources. Clearly, the chances of that happening are next to zero. However, equally clearly, if this were to happen, the fact that humanity is reduced to such a state would be horrific. Yet the animals are giving us tit-for-tat: does it only become horrific for party A, when party B is able to do the same in return?
Damn. My heart goes out to those pigs. Sleep peacefully, this world isn't a nice place anyway.
You should find a camp to join that can help coordinate tickets. (Whether Directed Group Sale or just having the network for overflow tickets.) Unless you’re prepared to spend $$$ and suffer through figuring out a lot to truly DIY it, it’ll be a much better experience to be part of a camp that knows what it is doing anyway.
Right, if our best regular conductors (used in your ohmmeter) are ~10^-8 and superconductivity is (by convention) less than 10^-11, one can see right away the simple regular methods won’t work and some cleverness is needed.
The conductors of your ohmmeter are not that important, though. You can work around that by using four-terminal sensing, and you can of course also calibrate your probes by directly touching them together. Even if your ohmmeter conductors have a resistance of several ohm, you could still get an accurate measurement if your tool has a high enough resolution.
A bigger issue is going to be sample size. A 1mm-diameter 1mm-long rod of silver has a resistance of about 20 μΩ (or 2e-5) at room temperature. That's already getting tricky to measure with lab-grade equipment without pushing insane currents through it, let alone anything even smaller. If you want to measure a 1m-diameter 1m-long silver rod (which would be 0.02μΩ or 2e-8) you could just push a few thousand amps through it and reliably measure that using a household multimeter in the mV range - but do that with a small sample and it'll evaporate.
> Even if your ohmmeter conductors have a resistance of several ohm, you could still get an accurate measurement if your tool has a high enough resolution.
Not that low in range though, you will end up seeing thermal noise that dwarfs your measurement.
> superconductivity is (by convention) less than 10^-11,
Ah, so you're saying that superconductivity is not actual zero resistance, but something close to it, and in fact only a factor of 1000x less resistive than the best conductor?
If that is so, this is something that I had previously thought would make a lot more sense to me.
But in that case it's not intuitive to me how SMES is possible with a 0% discharge rate. Shouldn't a significant fraction of the electrons looping around the coils be lost after many loops? (I know very little about electricity, as you can probably tell, never mind superconductors).
After running into these issues a few others and I wrote a typescript agent framework that I think significantly improves on LangChain in many ways: https://github.com/sciencecorp/buildabot/
It’s still very early days for software composing AI models and we almost certainly don’t have all the right metaphors yet. And I think there is a lot to be said for strong typing and simple, robust code!
I've played with langchain now for a couple weeks (with some of the llama-derivative local models and Oobadooba's native & openai apis + TextGen https://python.langchain.com/docs/modules/model_io/models/ll... ) and find it not-too-insanely-hard for an idiot like myself to figure out, though I'm just experimenting at this point with different models, esp. using tools, etc. I've found that some of the recommended prompts in the demos that, while perhaps working well with chatgpt/gpt4, need a lot of tweaking to work with with say WizardLM. But then I can get them working, so that's kinda neat.
I also played with huggingface's transformer agent (https://huggingface.co/docs/transformers/transformers_agents ) and thought it was a lot easier to useas far as the tools go, though is perhaps less capable for other things. I may go back to playing with that actually.
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