For posterity: English Wikipedia is deletionist, so your burden of proof is entirely backwards. I know this because I quit English WP over it; the sibling replies are from current editors who have fully internalized it. English WP’s notability bar is very high and not moved by quantity of sources; it also has suffered from many cranks over the years, and we should not legitimize cranks merely because they publish on ArXiv.
We can read between the lines for ourselves. From OpenAI’s announcement of Stargate in January, the only equity-holder who has built datacenters is Oracle, and the only other technology partner who has built datacenters is Microsoft. They claim that OpenAI will be operationally responsible, but OpenAI doesn’t have a team dedicated to building out and staffing datacenters. In related reporting, Microsoft relaxed its exclusive rights to OpenAI’s infrastructure specifically for Oracle and Stargate. As for the motives, I’ll highlight Ed’s reporting:
The Oracle/Stargate situation was a direct result — according to reporting from The Information — of OpenAI becoming frustrated with Microsoft for not providing it with servers fast enough, including an allotment of 300,000 of NVIDIA’s GB200 chips by the end of 2025.
Why is Microsoft canceling a Gigawatt of data center capacity while telling everybody that it didn’t have enough data centers to handle demand for its AI products? I suppose there’s one way of looking at it: that Microsoft may currently have a capacity issue, but soon won’t, meaning that further expansion is unnecessary.
This is precisely it. Internally, Microsoft’s SREs perform multiple levels of capacity planning, so that a product might individually be growing and requiring more resources over the next few months, but a department might be overall shrinking and using less capacity over the next few years. A datacenter requires at least 4yrs of construction before its capacity is available (usually more like 5yrs) which is too long of a horizon for any individual product…unless, of course, your product is ChatGPT and it requires a datacenter’s worth of resources. Even if OpenAI were siloed from Microsoft or Azure, they would still know that OpenAI is among their neediest customers and include them in planning.
Source: Scuttlebutt from other SREs, mostly. An analogous situation happened with Google’s App Engine product: App Engine’s biggest users impacted App Engine’s internal capacity planning at the product level, which impacted datacenter planning because App Engine was mostly built from one big footprint in one little Oklahoma datacenter.
Conclusion: Microsoft’s going to drop OpenAI as a customer. Oracle’s going to pick up the responsibility. Microsoft knows that there’s no money to be made here, and is eager to see how expensive that lesson will be for Oracle; Oracle is fairly new to the business of running a public cloud and likely thinks they can offer a better platform than Azure, especially when fueled by delicious Arabian oil-fund money. Folks may want to close OpenAI accounts if they don’t want Oracle billing them someday.
Reading through the docket, he is entitled to a hearing for relief and has a modicum of standing due to the threat of deportation from the USA to China; it’s not unreasonable to go to federal court. The judge was fairly courteous in referring him to the Pro Se Project a week ago. I’m a little jealous of how detached he is from reality; from 36(a) of the Amended Complaint:
The Plaintiff asserts that completing a Ph.D. in Health Services Research significantly increases earning potential. The average salary for individuals with such a Ph.D. is $120,000 annually, compared to $30,000 annually in China, where Plaintiff’s visa cancellation forces him to seek employment. Over an estimated 30-year working career, this represents a lifetime income loss of $2,700,000.
He really went up to the judge and said, “your honor, my future career is dependent on how well I prompt ChatGPT, but statistically I should be paid more if I have a second doctorate,” and the judge patted him on his head and gave him a lollipop for being so precocious.
Well, how do you feel about robotics?
On one hand, I fully agree with you. AI is a rebranding of cybernetics, and both fields are fundamentally inseparable from robotics. The goal of robotics is to create artificial slaves who will labor without wages or solidarity. We’re all ethically obliged to question the way that robots affect our lives.
On the other hand, machine learning (ML) isn’t going anywhere. In my oversimplification of history, ML was originally developed by Markov and Shannon to make chatbots and predict the weather; we still want to predict the weather, so even a complete death of the chatbot industry won’t kill ML. Similarly, some robotics and cybernetics research is still useful even when not applied to replacing humans; robotics is where we learned to apply kinematics, and cybernetics gave us the concept of a massive system that we only partially see and interact with, leading to systems theory.
Here’s the kicker: at the end of the day, most people will straight-up refuse to grok that robotics is about slavery. They’ll usually refuse to even examine the etymology, let alone the history of dozens of sci-fi authors exploring how robots are slaves or the reality today of robots serving humans in a variety of scenarios. They fundamentally don’t see that humans are aggressively chauvinist and exceptionalist in their conception of work and labor. It’s a painful and slow conversation just to get them to see the word robota.
Starting the week with yet another excellent sneer about Dan Gackle on HN. The original post is in reply to a common complaint: politics shouldn’t be flagged so quickly. First, the scene is set:
The story goes, at least a few people don’t like hearing about Musk so often, and so we need to let all news about the rapid strip-mining of our government and economy be flagged without question.
The capital class are set to receive trillions in tax breaks off the gutting of things like Medicaid and foreign aid to the poorest and most vulnerable people in the world. The CEO of YC and Paul Graham are cheer-leading the provably racist and inexperienced DOGE team. That dozens of stories about their incredibly damaging antics are being flagged on HN is purely for the good of us tech peasants, and nothing to do with the massive tax breaks for billionaires.
But this sneer goes above and beyond, accusing Gackle of steering the community’s politics through abuse of the opaque flagging mechanism and lack of moderator logs:
Remember, dang wants us all to know that these flags are for the good of the community, and by our own hand. All the flaggers of these stories that he’s seen are ‘legit’. No you can’t look at the logs.
And no, you can’t make a thread to discuss this without it getting flagged; how dare you even ask that. Now let Musk reverse Robin Hood those trillions in peace, and stop trying to rile up the tech-peasantry.
I’m not really surprised to see folks accusing the bartender of the Nazi Bar of being a member of the Nazi Party; it’s a reasonable conclusion given the shitty moderation over there. Edit: Restored original formatting in quote.
The sibling comment gives a wider perspective. I’m going to only respond narrowly on that final paragraph’s original point.
String theories arise naturally from thinking about objects vibrating in spacetime. As such, they’ve generally been included in tests of particle physics whenever feasible. The LHC tested and (statistically) falsified some string theories. String theorists also have a sort of self-regulating ratchet which excludes unphysical theories, most recently excluding swampland theories. Most money in particle physics is going towards nuclear power, colliders like LHC or Fermilab’s loops, or specialized detectors like SK (a giant tank of water) or LIGO (artfully-arranged laser beams) which mostly have to sit still and not be disturbed; in all cases, that money is going towards verification and operationalization of the Standard Model, and any non-standard theories are only coincidentally funded.
So just by double-checking the history, we see that some string theories have been falsified and that the Standard Model, not any string theory, is where most funding goes. Hossenfelder and Woit both know better, but knowing better doesn’t sell books. Gutmann doesn’t realize, I think.
It’s been frustrating to watch Gutmann slowly slide. He hasn’t slid that far yet, I suppose. Don’t discount his voice, but don’t let him be the only resource for you to learn about quantum computing; fundamentally, post-quantum concerns are a sort of hard read in one direction, and Gutmann has decided to try a hard read in the opposite direction.
Page 19, complaining about lattice-based algorithms, is hypocritical; lattice-based approaches are roughly as well-studied as classical cryptography (Feistel networks, RSA) and elliptic curves. Yes, we haven’t proven that lattice-based algorithms have the properties that we want, but we haven’t proven them for classical circuits or over elliptic curves, either, and we nonetheless use those today for TLS and SSH.
Pages 28 and 29 are outright science denial and anti-intellectualism. By quoting Woit and Hossenfelder — who are sneerable in their own right for writing multiple anti-science books each — he is choosing anti-maths allies, which is not going to work for a subfield of maths like computer science or cryptography. In particular, p28 lies to the reader with a doubly-bogus analogy, claiming that both string theory and quantum computing are non-falsifiable and draw money away from other research. This sort of closing argument makes me doubt the entire premise.
Look, I get your perspective, but zooming out there is a context that nobody’s mentioning, and the thread deteriorated into name-calling instead of looking for insight.
In theory, a training pass needs one readthrough of the input data, and we know of existing systems that achieve that, from well-trodden n-gram models to the wholly-hypothetical large Lempel-Ziv models. Viewed that way, most modern training methods are extremely wasteful: Transformers, Mamba, RWKV, etc. are trading time for space to try to make relatively small models, and it’s an expensive tradeoff.
From that perspective, we should expect somebody to eventually demonstrate that the Transformers paradigm sucks. Mamba and RWKV are good examples of modifying old ideas about RNNs to take advantage of GPUs, but are still stuck in the idea that having a GPU perform lots of gradient descent is good. If you want to critique something, critique the gradient worship!
I swear, it’s like whenever Chinese folks do anything the rest of the blogosphere goes into panic. I’m not going to insult anybody directly but I’m so fucking tired of mathlessness.
Also, point of order: Meta open-sourced Llama so that their employees would stop using Bittorrent to leak it! Not to “keep the rabble quiet” but to appease their own developers.
West Coast of USA, late 2000s to early 2010s, yes, the thick squared dark eyeglass frames were popular. Every time I see photos of these folks, I’m reminded of a couple people I know IRL as well as folks I know professionally who still prefer the thicker frames. Personally, I’ve always needed a very heavy prescription, and so I’ve always looked for the thinnest frames, but it really was a trend a decade ago.
Somebody pointed out that HN’s management is partially to blame for the situation in general, on HN. Copying their comment here because it’s the sort of thing Dan might blank:
but I don’t want to get hellbanned by dang.
Who gives a fuck about HN. Consider the notion that dang is, in fact, partially to blame for this entire fiasco. He runs an easy-to-propagandize platform due how much control of information is exerted by upvotes/downvotes and unchecked flagging. It’s caused a very noticeable shift over the past decade among tech/SV/hacker voices – the dogmatic following of anything that Musk or Thiel shit out or say, this community laps it up without hesitation. Users on HN learn what sentiment on a given topic is rewarded and repeat it in exchange for upvotes.
I look forward to all of it burning down so we can, collectively, learn our lessons and realize that building platforms where discourse itself is gamified (hn, twitter, facebook, and reddit) is exactly what led us down this path today.
Elon is an Expert Beginner: he has become proficient in executing the basics of the craft by sheer repetition, but failed to develop meaningful generalizations.
The original Expert Beginner concept was defined here in terms of the Dreyfus model, but I think it’s compatible with Lee’s model as well. In your wording of Lee’s model, one becomes an Expert Beginner when their intuition is specialized for seeing the thing; they have seen so many punches that now everything looks like a punch and must be treated like a punch, but don’t worry, I’m a punch expert, I’ve seen so many punches, I definitely know what to do when punches are involved.
There’s a good insight from this armchair psychoanalysis. The typical narcissist is technically capable of performing the whole pretend-to-care-for-game-theoretic-reasons behavior, provided that there is an incentive for them. However, if Elon genuinely believes himself to be Christ or Buddha or Roy, then his abilities don’t matter, because he will never have the incentive to deflate his beliefs and face his own limitations and mortality. In short, Elon’s attitude can’t be adjusted and his mental health will never improve.
You may have heard that Catturd doesn’t have any fiber in his diet and was hospitalized for bowel blockage. (Best sneer I’ve seen so far: “can’t turd.”) Along similar lines, Srid isn’t taking his statins for high cholesterol caused by a carnivore diet.
Meta: I’m kind of pissed that Catturd is WP notable but laughing my ass off at the page for carnivore diets. Life takes and gives.
Yeah, as somebody in the USA, I think that both you and @[email protected] are pearl-clutching over laboratory conditions while ignoring the other, more serious safety problems being addressed; the presentation was not exaggerating when they were talking about the lifesaving impact of gender-affirming therapy. Last thread, you sheepishly admitted that part of the synthesis is complicated by criminalization and over-regulation; this thread, I’d like a sheepish admission that about a third of the USA (by population) suffers from restrictions on their reproductive rights.
Like, yes, you shouldn’t brew your own high-proof alcohol at home, because you can go blind from methanol poisoning. But also, there was a time in the USA when high-proof alcohol was over-regulated, and it incentivized a lot of people to homebrew.
Today’s “Luigi isn’t sexy” poster is Thomas Ptacek. The funniest example is probably this reply on the orange site:
That’s an extrapolation from a poll, not literally 50 million people…
A cryptographer not believing in statistical analysis! I can’t stop giggling, sorry.
John “Animats” Nagle choosing the most racist angle possible to respond to problems in education. The topic is giftedness and yet Nagle needs to start with “Ashkenazi Jews”.
I’m imagining no fewer than three fictional versions of Eris/Discord laughing at this orange-site fool:
Meanwhile I cannot turn my living room LED lights on or off because I control them through discord.
I wasn’t going to explain my downvote, but it’s been a few days and apparently everybody here is thinking about MRAs when there’s more at stake.
I see Nixon in Trump: somebody who starts and prolongs wars for their own political gain. Of my three uncles who qualified to go to Vietnam, one was permanently disabled during basic training, one didn’t come back home, and one fell apart before I was born. I had to “voluntarily” register as a potential servicemember in order to access various standard government services as a young man in the 2000s, while the USA was invading Iraq and Afghanistan. Under a sufficiently fascist government, the USA has shown itself capable of sending its men to death. This system is explicitly misandrist; only men are required to register and only my uncles suffered this hate.
Misandry isn’t equal and opposite to misogyny. Our society was never obligated to hate men and women in ways that are nicely symmetric and amenable to analysis; indeed, critical theory suggests that society deliberately structures itself to obfuscate its hate.
Here’s some food for thought; ha ha, only serious. What if none of this is new?
If this is a dealbreaker today, then it should have been a dealbreaker over a decade ago, when Google first rolled out Knowledge panels, which were also often inaccurate and unhelpful.
If this isn’t acceptable from Google, then it shouldn’t be acceptable from DuckDuckGo, which has the same page-one results including an AI summary and panels, nor any other search engines. If summaries are unacceptable from Gemini, which has handily topped the leaderboards for weeks, then it’s not acceptable using models from any other vendor, including Alibaba, High-Flyer, Meta, Microsoft, or Twitter.
If fake, hallucinated, confabulated, or synthetic search results are ruining the Web today, then they were ruining the Web over two decades ago and have not lessened since. The economic incentives and actors have shifted slightly, but the overall goal of fraudulent clicks still underlies the presentation.
If machine learning isn’t acceptable in collating search results today, then search engines would not exist. The issue is sheer data; ever since about 1991, before the Web existed, there has been too much data available on the Internet to search exhaustively and quickly. The problem is recursive: when a user queries a popular search engine, their results are populated by multiple different searchers using different techniques to learn what is relevant, because no one search strategy works at scale for most users asking most things.
I’m not saying this to defend Google but to steer y’all away from uncanny-valley reactionism. The search-engine business model was always odious, but we were willing to tolerate it because it was very inaccurate and easy to game, like a silly automaton which obeys simple rules. Now we are approaching the ability to conduct automated reference interviews and suddenly we have an “oops, all AI!” moment as if it weren’t always generative AI from the beginning.