Indeed. Also, companies with a genuine and socially at least somewhat useful business model prefer focusing on customers buying their actual products as the main revenue source. The state- and EU-driven funding ecosystem is so convoluted that one either mostly ignores it in favor of value-creating work, or specializes in exploiting it, unfortunately.
It may not be sloppiness. Consider the official statement as shared in this comment: https://news.ycombinator.com/reply?id=47789061.
The ministry of defense will issue an 'exception' that generally applies.
Presumably, revoking this exception is straightforward and much easier than passing a new law.
What would be a balanced perspective? Perhaps that oAI may now be another "boring" startup in that it is no longer primarily about moving the technology frontier, but about further scaling while keeping churn low, with margins (in the broader sense, i.e. for now prospective margins) becoming increasingly important?
Couldn't one interpret "magical systems thinking" as a fallacy that people may commit when applying systems thinking? More broadly, I find some of the comments here rather harsh, also considering that many observations in the article are intuitively true for anyone whose ever been exposed to bureaucracy on the meta-level.
> That's not as inspiring as it could be, but it's a lot better than the tech industry overall.
I regularly see both worlds. What I find more troubling in academia is that it is difficult to openly talk about how flawed the system is, that people make mistakes, that papers have mistakes (the own ones as well as others'). We know all software has bugs; but the code that a PhD student hacks together over night is assumed to be flawless (the more senior people rarely even glance at it), otherwise the paper is all wrong, and papers are set in stone...
So I genuinely struggle with the lack of a proper failure culture in academia, as it is designed as a system that is fundamentally geared against openly discussing failure.
It is well-known that top-conferences had and still have many problems. Some examples:
There used to be the problem with authors adding new co-authors after acceptance, aka "selling seats".
There is a debate about how many papers one should be allowed to submit, as some people with money and influence are heavily franchising.
It is unclear to what extent there is implicit and explicit reviewer collusion. Even double-blind reviews don't really solve the problem.
If we don't admit that there are fundamental problems that affect all of us (academics) and instead pretend it is only the lesser people who f things up, we'll all be screwed sooner or later.
This is an interesting perspective, and I assume your definition is the technically correct one. Still, many SEs receive substantial compensation in RSUs, direct stocks, shares in startups, et cetera. So also from this perspective, there are many non-working class SEs. Another aspect is that culturally, the perception has been that SEs don't necessarily sell their work by the hour, but instead sell knowledge that scales tremendously, in exchange for a comfortable upper middle-to-lower upper class life.
Well, for the time SEs are substantially better paid than working-class jobs, they are not the working class. For now, this applies at least to some regions, not only within the US. I agree in that I have at times felt some level of arrogance among some people taking up software engineering jobs, but IMO this just confirms the social class aspect of it. So there may have been some level of delusion to it, but at least temporarily it was, and partially still is, true.
The narrative reflects a broader cultural shift, from "we are all in this together" (pandemic) to "our organizations are bloated and people don't work hard enough" (already pre-LLM hype post-pandemic). The observation that less-skilled people can, with the help of LLMs, take the work of traditionally more-skilled people fits this narrative. In the end, it is about demoting some types of knowledge workers from the skilled class to the working class. Apparently, important people believe that this is a long-term sustainable narrative.
Is it really that LLM-based tools make developers so much more productive or rather that organizations have found out they can do with less -- and less privileged -- developers?
What I don't really see, especially not big tech-internally, are stories of teams that have become amazingly more productive. For now it feels we get some minor productivity improvements that probably do not off-set the invest and are barely enough to keep the narrative alive.
Why is it a terrible take? You first have to understand a community in order to reform it. You can't just say "I actually have no idea how to do research in this field, have not contributed anything substantial, have no money or soft power, but let me tell y'all how to do better science according to my subjective and very limited understanding."
You are introducing a straw-man argument here (that obviously ignores basic facts about population sizes of Sweden and China, so it's wrong on both meta- and object-level). But following that line of reasoning, Sweden is not an ethnostate because the Swedish population is ethnically diverse. E.g., more than a third of the population are either foreign-born or have at least one foreign-born parent. At the same time, the current borders of Sweden cover more than the traditional homeland of your preferred "Swedes". So apparently, you are actually arguing for a racist empire. Demographic reality is not your friend, though. Perhaps you will have more success some generations down the road. Try to sell the same story to the ethnically substantially different group of people who will consider themselves "Swedes" then.
In the ethno-nationalistic sense, Sweden is not a nation state. In its current reality, Sweden is ethnically diverse and it (partially) covers the homelands of several peoples/"nations" in the ethno-nationalistic sense. The latter issue could, e.g., be solved by giving up most of what the Swedes in the South call Norrland. This would deplete "Sweden" of natural resources and make it even more dependent on high-skilled immigration to ensure that at least the tech industry industry in the somewhat larger cities keeps running.
The historic reality begs to differ: Sweden covers large parts of the traditional homelands of non-Swedish people. Current reality differs as well, obviously. No matter how you twist it and turn it, the argument can only make sense for a hypothetical, geographically smaller Sweden that does not exist.
Based on practical experience, NPS is garbage because:
1) Even with stable mean and median, NPS tends to vary month over month, at least for my B2B settings where samples are probably much smaller than for B2C. Then, management goes nuts because of very subtle shifts in the distribution caused by NPS' arbitrary aggregation into promoters, neutrals, detractors. Of course, often investors are married to NPS, so educating management does not solve the problem.
2) NPS varies unreasonably across cultures. We used to say, somewhat tongue-in-cheek, that NPS is a US-centric metric, where things are either amazing or awful (with little space in between). E.g., in northern/central Europe, an 8 can be pretty amazing.
Without knowing the specific context: I think this really is a good example of how errors should be disclosed.
We need to acknowledge that scientists/academics are human; even very competent mathematicians make mistakes and some of these mistakes appear in published papers. What we lack in many fields is a culture and process that allows (and ideally, encourages) one to disclose: "this was wrong, here is how I fixed it, or how it's actually correct". E.g., in the communities I know in Computer Science & AI, I rarely even see errata lists on personal webpages, not to speak of journals that provide a straightforward process for updates. I would even go so far to claim that the current culture, in which honest errors cannot be straightforwardly corrected, plays into the hands of the clearly dishonest "bad apples".
Science is, obviously, not a "monotonic" process in which every single paper adds to the truth; this is practically not even the case for mathematics, which is at least monotonic on object-level (but mistakes happen all the time). As a prominent example, consider this impressive list of Feynman errata: https://www.feynmanlectures.caltech.edu/info/flp_errata.html.
The Corchado issue is merely an extreme "black and white" case, whereas the overall problem is more nuanced. E.g., there is no clear line between an implicit citation cartel and a group of thought-leaders in a small community. And once you are a thought-leader in a small community, it's much easier to gain broader visibility, to obtain funding, and to influence behind-the-scenes decision making. In a way, Corchado is relatively harmless because everybody knows he is a clown. He does not have much to say in the European AI community. The real issues are in the grey areas (and the people thriving in them) that affect and taint everyone.
> performance doesn't matter for the staff to be paid.
That's not the case for the people who are in the key phase of establishing themselves as academics and who are generally not on permanent contracts. And for tenured staff, performance decides access to resources that may be as mundane as one's own research time.
Conversely, the problem is a mix of extremely shallow performance metrics and a paper-pusher system in which most people get bogged down by bureaucracy by the time they get tenure (or even earlier). This means that very few people have the peace of mind that allows them to focus on high-risk fundamental research. In European AI and CS, I would claim that the ones who manage to maintain focus essentially do this in their free time and get little reward for this; the alternative, i.e., building a strong franchise, is more rewarding in terms of performance metrics.
Note that "false positives" may appear in the Google Scholar results for such queries. For example, a high-profile AI researcher shared a screenshot of https://scholar.google.com/scholar?as_sdt=0%2C5&as_ylo=2023&... (on X/Twitter: https://twitter.com/MelMitchell1/status/1768422636944499133).
The second entry is an ironically written abstract of a talk and the author clearly (and consciously) plays with the cliché. Obviously, most of the fans of the person who shared the screenshot to thousands of followers will not notice this.
I would still assume that with apps like Snapchat, there are inherent problems on meta-level (from an E2EE view) because for the users, Snapchat is essentially a trusted third party, providing governance/management features. But it can well be that Snapchat does not provide E2EE; I cannot see any remarks about it on their webpage.
"WhatsApp _considers_ chats with businesses that use the WhatsApp Business app or manage and store customer messages themselves to be end-to-end encrypted."
And later, they write that in many cases, Meta can actually read the messages.
The article somewhat suggests that the guy used the airport's wifi. In this case and assuming that the wifi network supports government surveillance by design, end-to-end encryption does not help.
What I disagree with in the article is the _I never make mistakes_ attitude; it could be worse, but I still think it's good to discuss. The author writes that because they are "serious", they can essentially rebut all criticism "easily". We are all human and even excellent scientists make honest mistakes. Strong theorists can sometimes make "hard" math mistakes. In the best cases, peer review gives us some assurance that we at least did not make obvious mistakes that can be relatively easily spotted by other specialized researchers.
I think the _never make mistakes_ attitude is dangerous, because it means that researchers need to be very cautious when admitting honest mistakes and their own intellectual fallibility in order to not lose face.
If so, let's start with the former (god) and point us not only to the proof, but also to the theorem and the underlying definitions. There are many logicians with a scientific interest in god, religion, and related concepts. Why not be open minded, after all? But we should expect more rigor in this case.
A well-intended interpretation is: the parent comment advocates for a society where there is substantial language diversity even within smaller geographic regions.
This makes sense from many perspectives, because forcing people to speak the same language no matter the context is not only hostile to immigrants but also to native minorities. E.g., in the North of Scandinavia (where I reside), many people don't like that the language that has to be spoken is traditionally (less now than historically) dictated by the South.
Sure, but we are talking about research teams here, not about an ops or compliance team. Central research tends to be detached from the business units but does not provide any of the 'consistency' benefits. Central research makes sense if the objectives are outward-facing, not if one wants to have an effect on what happens in the software-building units. So I'd say that ideally/hopefully, the people of the RAI team will now be much closer to Meta's engineering reality.
Fully agree. Central functions of these types do not scale. Even with more mundane objectives, like operational excellence, organizations have learned that centralization leads to ivory tower nothing-burgers. Most of the resources should go to where the actual work gets done, as little as possible should be managed centrally (perhaps a few ops and thought leadership fluff folks...).