> Replicating a paper is just as valuable scientifically as publishing it, but how many careers advance through replication?
A lot. In fields where knowledge is incrementally building on previous work the reason the whole field hasn't collapsed from the replication crisis is that usually the results that are really high impact are replicated in as an initial step in new research building on it. It's almost never the focus of the paper but you'll often find a quick mention in methods/supplemental of some previous work that was verified to be valid by a replication of a key technique etc. you'll have crisis where old tools are found to be problematic and findings end up revisited etc. Plus fields like clinical research where there's an awful lot of focus on replicating findings using staged clinical trials with increasing statistical power to determine if new interventions work - that's driven by regulatory requirements grounded in good science and a lot of people make careers in just that.
Not an expert by any means but the assumption here as I understand it is that the arxiv worthy PDF would not be acceptable or meaningful for impossible to understand proofs. And the lean proof would be meaningless unless the specific expression being proven is human understandable as the direct translation of the question the human is asking in formal form. So proving the negation is not a thing but if you make a subtle mistake in translating the statement you want to prove then obviously the QI is going to be proving the wrong thing. And otherwise you're relying on the correctness of lean as a system and on identifying/preventing if the proof is adversarially exploiting bugs in lean to falsely prove things.
Can't speak for op for but me no - it was just ingrained as something disrespectful to do to books. Sometimes you've got a taboo like eating with your left hand that has a pretty clear reason for coming into existence (you use your left hand to clean up in the bathroom and before hand soap in the era of everyone dying from cholera might as well keep those functions as physically separated as possible) but this is more just something you would be disrespectful to do
I'm so confused by the point you're trying to make. There's a lot of rhetoric about Georgia and an investigation about how the country list is 1:1 with some mysterious list from 1996 - but like - it's an export control list? Yeah, Washington approved the sale of missiles to Georgia. That's how that list works. Washington has to approve it. Openai is not Washington. They're perfectly reasonably erring on the side of caution and potentially over-complying with export controls. And if they then have to get approval from the feds to export to Georgia - well - our current administration has provided many reasons to over comply with trade related controls and not exactly been a champion of encouraging cross country trade right now. Idk what you expect from OpenAI or why you think it would matter at all that Georgia is a democracy or an ally of the US or is closely aligned with us. Ask Canada and NATO how much that's counted for with this administration.
no the answer is `npx ccusage` or any of the other of the trillion ways to see how many tokens you're getting and what the current subsidization rates are.
no the $200/mo subs are definitely infinitely cheaper. If you're stuck paying enterprise API prices though that's not the case. So for personal or business premium plan use there's no competition but api rate/enterprise there is. Still a ton of spend happening on e.g. bedrock and via api.
I don't think you need the scare quotes. product lifecycles can take years and the actual neo demand was actually pretty insane. They were using it to soak up demand for binned a18 chips and it would have been irresponsible to forecast that they'd have the demand they did when there's another perfectly reasonable universe where 8 gigs was too much of a compromise and it flopped.
this article's also about enterprise demand specifically. That's a bit surprising to me as well frankly. I'd have thought the primary market for mac studios would be hobbyists/enthusiasts with a bunch of disposable income who are willing to pay 18k for a 512 gb machine to run glm 3.5 flash or 9k to run deepseek v4 flash locally. It's competing with a $200/mo subscription or renting server gpu time for open source models during a memory shortage - and idk if it's going to be powerful enough to train or fine tune so it's really just inference. seems reasonable to be surprised
i come from a ultrabook with 2 gigs of ram and the neo takes 2 vscode instances, one antigravity and firefox while keeping telegram and whatsapp on the background. that device can be used as a workhorse, pagination is very good.
can even run ios/android emulators but then you have to only have that project open, which is a non-issue for me. and the battery literally keeps working all day. good screen, good keyboard, good touchpad, operative system is close to linux. if youre thinking about getting one for programming and you're scared about the 8 gigs, hope i gave someone some light about it.
If the latest apple hardware could run Linux I would be buying a good amount. But I just hate the idea of being at the whims of macOS timelines. But I guess when you’re selling more than you can produce it doesn’t really matter.
if you're one of those enterprises it's perfectly reasonable to assume you're more likely to go have your procurement and legal teams negotiate with google for one of those weird boxes to run gemini on prem (https://cloud.google.com/blog/products/ai-machine-learning/r...) or other enterprise-y nonsense vs buying consumer hardware to run a chinese large language model in your network. I do not envy the person at a company having to get approval model by model because of weird open source licensing terms dealing with the "how do we know chinese models are safe" question (hopefully they're at least getting asked in the context of hooking it into an agent harness so there's some sort of plausible risk that necessitates the conversation - i can very much imagine it getting shut down to even run in a sandbox because chinese model + people being scared after the huggingface stuff). There's a lot of reasons to assume enterprise wouldn't be interested, and it's very very cool that they are IMO. Anthropic cut claude code rate limits - they dont seem to be see open source llms as a market threat (rhtetoric to the white house aside) yet but enterprises being willing to run consumer hardware to run local models could make things a bit more tangible
Wouldn't enterprises investing in their own hardware be more likely to buy standard 4-GPU or 8-GPU servers? Apple machines aren't really designed for server racks.
> another perfectly reasonable universe where 8 gigs was too much of a compromise and it flopped.
The target audience didn't evaluate that as a limitation - the majority of the market for Apple devices does trust that they will not produce and sell a computer incapable of support their use.
Professionals know there are tasks that a baseline computer cannot handle, and even common tasks that a more powerful computer does better, but those people weren't really the target demo for the laptop.
> The target audience didn't evaluate that as a limitation - the majority of the market for Apple devices does trust that they will not produce and sell a computer incapable of support their use.
Well, kinda the point. We know this to be fully true in retrospect (and many people correctly predicted it) but decent arguments existed against it at time of release.
>"the majority of the market for Apple devices does trust that they will not produce and sell a computer incapable of support their use"
the problem with that argument is that the vision pro exists, where they clearly overestimated demand, and where even among people with interest in VR and disposable income, the compromises on battery life and weight were actually too much to bear. Forecasting is just hard and you always need to be especially skeptical when you yourself are doing the forecasting for something you want to succeed. All those arguments for the neo line up in retrospect hindsight is always 2020
They also irritated plenty of devs, who aren't shelling out 3500 for what is in practical purposes a toy device, and then having Apple congratulate themselves the devs are the ones that should be happy they are allowed to play on Apple's kingdom in first place.
A lot of big enterprises struggle with ai adoption. Either they can't get models/tools approved fast enough, can't provision them in their internal network, or get slammed with exorbitant inference costs.
A big enterprise can drop one (or 4) of these on someone's desk and let them go nuts.
I think people just hate subscriptions so much they are willing to make an obviously worse financial choice to buy upfront.
I’d be willing to pay more to own vs subscribe, but the gap is currently far too large where buying a Mac Studio for AI is a straight up terrible investment.
I am also willing to overpay to not be reliant on cloud things, to have control of local hardware.
My biggest problem with buying a Mac Studio is that even in the case of the M5 Max models, I can’t think of any non-AI macOS applications in my creative life that have anywhere near that level of hardware requirements, and nor can I forecast that I would within its ordinary supported lifespan as a macOS machine. These machines left high-end stills and most video work behind generations ago, for example.
So while I would like a local machine that I can leave running in a way that I wouldn’t want to do with my old secondhand M1 Max laptop, it’s going to have to be something a little more pragmatic. Probably based around the Radeon R9700, since the AMD/Nvidia gap for LLMs is beginning to close, and even a single R9700 runs the main models I am interested in at speeds that are acceptably faster than what I have here.
I think the best move right now is to just wait a couple of years. There is room for the prices to drop dramatically. Buying the highest spec mac at the peak prices probably isn't a financially sensible move.
I tend to buy secondhand anyway, and yes, almost certainly it's best to wait.
At the moment, speccing out a local box more powerful than the machine I am using is largely an intellectual exercise, but it does have some value for understanding what a client might be able to use if they absolutely require on-premises inference, and I do have a couple who fall into that zone. So I am trying to keep up to date in principle, but the built machines don't get beyond the shopping cart.
The M5 Ultra has probably smoothed out the major issues of AI on Apple Silicon (prefill doesn't suck anymore) but the way Apple marketed that chip strongly suggests they are now fully aware of the strengths but also the limitations of their architecture and will get a lot more done.
It's properly put the (fairly equivalently priced) DGX Spark in the shade, though.
A lot. In fields where knowledge is incrementally building on previous work the reason the whole field hasn't collapsed from the replication crisis is that usually the results that are really high impact are replicated in as an initial step in new research building on it. It's almost never the focus of the paper but you'll often find a quick mention in methods/supplemental of some previous work that was verified to be valid by a replication of a key technique etc. you'll have crisis where old tools are found to be problematic and findings end up revisited etc. Plus fields like clinical research where there's an awful lot of focus on replicating findings using staged clinical trials with increasing statistical power to determine if new interventions work - that's driven by regulatory requirements grounded in good science and a lot of people make careers in just that.
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