I worked in tech for ~8 years and am now finishing up a PhD. One thing that stood out to me is how that in most scientific fields review papers tend to receive more citations than empirical work. This post looks at citation patterns and argues that scientific progress depends just as much on abstraction and synthesis as it does on empirical tests.
When I worked at arXiv I looked at usage statistics that we didn't make public because we didn't want people to get the wrong idea.
One thing we knew is exactly that: the most viewed papers were review papers. You read a lot of them on the road to a PhD.
Another strange thing about review papers is that they escape the usual standards for evaluation in science. That is, as an outsider I can appoint myself to write a review paper without doing any research in the field, and it's possible I could do a very good job. One of the fun things I did in grad school was make a bibliography and short review of papers on the phenomenon of "Giant Magnetoresistance" at the request of the experimentalist on my committee.
A few months ago, OpenAI shared some data about how with 700 million users, 1 million people per week show signs of mental distress in their chats [1]. OpenAI is aware of the problem [2], not doing enough, and they shouldn't be hiding data. (There is also a great NYT Magazine piece about a person who fell into AI Psychosis [3].)
The links in other comments to Less Wrong posts attempting to dissuade people from thinking that they have "awoken their instance of ChatGPT into consciousness", or that they've made some breakthrough in "AI Alignment" without doing any real math (etc.) suggest that ChatGPT and other LLMs have a problem of reinforcing patterns of grandiose and narcissistic thinking. The problem is multiplied by the fact that it is all too easy for us (as a species) to collectively engage in motivated social cognition.
Bill Hicks had a line about how if you were high on drugs and thought you could fly, maybe try taking off from the ground rather than jumping out of a window. Unfortunately, people who are engaging in motivated social cognition (also called identity protective cognition) and are convinced that they are having a divine revelation are not the kind of people who want to be correct and who are therefore open to feedback. Because one could "simply" ask a different LLM to neutrally evaluate the conversation / conversational snippets. I've found Gemini to be useful for a second or even third opinion. But this means that one would be happy to be told that one is wrong.
It's probably an artifact of how I use it (I turn off any kind of history or "remembering" of past conversations), but when I started becoming really impressed by tools like claude/chatgpt/etc. was the first time I was chasing down some dumb idea I had for work, convinced I was right, and it finally gently told me I was wrong (in its own way). That is exactly what I want these things to do, but it seems like most users do not want to be told they are wrong, and the companies are not very incentivized to encourage these tools to behave that way.
I have identified very few instances where something like chatGPT just randomly started praising me (outside of the whole "you're absolutely correct to push back on this" kind of thing). I guess leading questions probably have something to do with this.
In one recent thread about StackOverflow dying, some people theorized that the success of LLMs and thus failing of SO could mostly be attributed to the amount of sycophancy of LLMs.
I tend to agree more and more. People need to be told when their ideas are wrong, if they like it or not.
SO was/is a great site for getting information if (and only if) you properly phrase your question. Oftentimes, if you had an X/Y problem, you would quickly get corrected.
God help you if you had an X/Y Problem Problem. Or if English wasn't your first language.
I suspect the popularity is also boosted by the last two; it will happily tell you the best way to do whatever cursed thing you're trying to do, while still not judging over English skills.
It became technically incorrect. You couldn't dislodge old, upvoted yet now incorrect answers. Fast moving things were answered by a bunch of useless people. etc.
Combine this with the completely dysfunctional social dynamics and it's amazing SO has lasted as long as this.
The technically incorrect issue is downstream of their rigid policies.
Yes, answers which were accepted go Python 2 may require code changes to run on Python 3. Yes, APIs
One of the big issues is that accepted answers grow stale over time, similar to bitrot of the web. But also, SO is very strict about redirecting close copies of previously answered questions to one of the oldest copies of the question. This policy means that the question asker is frustrated when their question is closed and linked to an old answer, which may or may not answer their new question.
But the underlying issue is that SO search is the lifeblood of the app, but the UX is garbage. 100% of searches show a captcha when you are logged out. The keyword matching is tolerable, but not great. Sometimes Google dorking with `site:stackoverflow.com` is better than using SO search.
Ultimately, the UX of LLM chatbots are better than SO. It’s possible that SO could use a chatbot interface to replace their search and improve usability by 10x…
SO is officially dead according to the graph of number of questions posted per month.
Google+SO was my LLM between 2007-2015. Then the site got saturated. All questions were answered. Git, C# Python, SQL, C++, Ruby, PHP, most popular topics got "solved". The site reached singularity. That is when they should have frozen it as the encyclopedia of software.
Then duplicates, one-offs, homeworks started to destroy it. I think earth society collectively got dumber and entitled. Decline of research and intelligence put into online questions is a good measure of this.
> People need to be told when their ideas are wrong, if they like it or not.
This is one of those societal type of problems rather than a technological one. I waffle on the degree of responsibility technology should have (especially privately owned ones) in trying to correct societal wrongs. There is definitely a line somewhere, I just don’t pretend to know where it is. You can definitely go too far one way or another - look at social media for an example
It all has to do with specific filler words you use when prompting, especially chatGPT. If you use words that suggest a heavy (and I mean you really have to make the LLM know you're questioning), then it will question to an extent as you imply. If you look at the chats that they do have from this incident, he phrased his prompts as more convincing rather than questioning (i.e "Shes doing this because of this!") So chatGPT roleplays and goes along with the delusion.
Most people will just talk to LLMs like they are a person, even though LLMs won't understand the difference in complex social language and reasoning. It's almost like robots aren't people!
Companies want the money and continual engagement. People getting addicted to AI, as trusted advisor or friend, is money in their pockets. Just like having people addicted to gambling or alcohol, it's all big business.
It's becoming even more apparent, that there is a line between using AI as a tool to accomplish a task versus excessively relying on it for psychological reasons.
> A few months ago, OpenAI shared some data about how with 700 million users, 1 million people per week show signs of mental distress in their chats
Considering that the global prevalence of mental health issues in the population is one in seven[1], that would make OpenAI users about 100 times more 'sane' than the general population.
Either ChatGPT miraculously selects for an unusually healthy user base - or "showing signs of mental distress in chat logs" is not the same thing as being mentally ill, let alone harmed by the tool.
Having a mental health issue is not at all the same thing as "showing signs of mental distress" in any particular "chat". Many forms of mental illness wouldn't show up in dialogue normally; when it would, it doesn't necessarily show up all the time. And then there's the matter of detecting it in the transcript.
I don't know the full details, but 700M users and 1 million per a week, means up to 52M per year though I imagine a lot of them show up multiple weeks.
You also don't take into account that the userbase itself is shifting.
That being said: Those of us who grew up when the internet was still young remember alt.suicide.holiday, and when you could buy books explaining relatively painless methods on amazon. People are depressed. It's a result of the way we choose to live as a civilization. Some don't make the cut. We should start accepting that. In fact, forcing people to live on in a world that is unsuited for happiness might constitute cruel and unusual punishment.
Maybe, just maybe, we should fix the fucked up world we created instead? Shunning the modern culture of individualism would be a great first step, followed by promoting communal culture. Live exactly how we evolved to live for hundreds of thousands of years.
> Because one could "simply" ask a different LLM to neutrally evaluate the conversation / conversational snippets.
The problem is using LLMs beyond a limited scope, which is free ideas but not reliable reasoning or, goodness forbid, decision-making.
Maybe the model for LLMs is a very good, sociopathic sophist or liar. They know a lot of 'facts', true or false, and are can con you out of your car keys (or house or job). Sometimes you catch them at a lie and their dishonesty becomes transparent. They have good ideas, though their usefulness only enhances their con jobs. (They also tell everything you say with others.)
Would you rely on them for something of any importance? Simply ask a human.
Fair question. While I'm not an expert on AI Alignment, I'd be surprised if any AI alignment approach did not involve real math at some point, given that all machine learning algorithms are inherently mathematical-computational in nature.
Like I would imagine one has to know things like how various reward functions work, what happens in the modern variants of attention mechanisms, how different back-propagation strategies affect the overall result etc. in order to come up with (and effectively leverage) reinforcement learning with human feedback.
I did a little searching, here's a 2025 review I found by entering "AI Alignment" into Google Scholar, and it has at least one serious looking mathematical equation: https://dl.acm.org/doi/full/10.1145/3770749 (section 2.2). This being said, maybe you have examples of historical breakthroughs in AI Alignment that didn't involve doing / understanding the mathematical concepts I mentioned in the previous paragraph?
In the context of the above article, I think it's possible that some people are talking to ChatGPT on a buzzword level end up thinking that alignment can be solved via "fractal recursion of human in the loop validation sessions" for example. It seems like a modern incarnation of people thinking they can trisect the angle: https://www.ufv.ca/media/faculty/gregschlitt/information/Wha...
> maybe you have examples of historical breakthroughs in AI Alignment that didn't involve doing / understanding the mathematical concepts I mentioned in the previous paragraph?
Multi agentic systems appear to have strong potential. Will that work out? I don’t know. But I know the potential there.
Per the article, this seems even better than the headline would suggest:
> Histotripsy generally seems to stimulate an immune response, helping the body attack cancer cells that weren’t targeted directly by ultrasound. The mechanical destruction of tumors likely leaves behind recognizable traces of cancer proteins that help the immune system learn to identify and destroy similar cells elsewhere in the body, explains Wood. Researchers are now exploring ways to pair histotripsy with immunotherapy to amplify that effect.
Not only do people leave the US but stay in Academia, plenty of people leave the research pipeline after receiving years and years of highly specialized, expert training. As an American who used to work in Tech and is currently getting a PhD, the geographic constraints on the (top tier) academic job market are more severe than people outside of Academia typically realize. It's a shame, because if it were the norm that science could happen by university-trained experts but in non-university institutions, we could a) fix the leaky pipeline, and b) see greater scientific progress.
What I mean is that if you don't like the company you work for in, say, SF, you can switch companies without having to switch houses. In Academia... it's akin to going to conservatory for classical music: you have to travel to where the orchestral openings are. This is a bit of a legacy problem from Wilhelm von Humboldt's idea to combine teaching and research, which led to the modern university system.
I'm far from the first person to say this, btw. Convergent Research's "Focused Research Organization" concept as well as The Arc and Astera Institutes are a few recent examples of people trying to provide escape routes from having to deal the large degree of "institutional tech/systems debt" in university contexts. For a great essay on why this is necessary, see "A Vision of Meta-science" (highly recommended if you are interested) [1].
The good news is that people are starting to come around to the idea that the scientific ecosystem would benefit from more diversity in the shape, size, and form of science-generating institutions.=The NSF just announced a new program to fund such "independent research organizations." I think this could give people who want to go into the sciences as a second career and who have a bit of an entrepreneurial tendency a new kind of Job opportunity [2]. We talk about Founders all of the time in Tech, we should probably have some equivalent in the best possible sense of the term, in the Sciences.
Maybe I'm just a sci-fi nerd who loves innovation, but this is so cool!
Clearly, this product is not intended for the mass market, and may find purchase with people who have tennis elbow and who can afford it, etc. <insert other critiques about practicality and applicability here>. But still, when was the last time someone tried to re-invent something as basic as a knife?
I thought I recognised the name - Bourbon Moth is the guy who faked a video of oily rags self-igniting to make a video to advertise fireproof bins for woodworking.
Writer Jack London's mansion,The Wolf House, that he was building up in Sonoma county was destroyed by a fire that investigators later attributed to the spontaneous combustion of oil-soaked rags in the dining room...
Uh, really? I haven't been following him for a while, so I don't absolutely know if you're wrong, but I absolutely can see him joking about it and maybe even taking it too far.
Never heard of either of these Youtubers but I've seen tons of cans of stains and such with warnings about potential self-ignition if left on rags in the wrong conditions...
Can you explain what you mean about 'not needing to be solved'? There are versions of that kind of critique that would seem, at least on the surface, to better apply to finance or flash trading.
I ask because scaling an system that a substantially chunk of the population finds incredibly useful, including for the more efficient production of public goods (scientific research, for example) does seem like a problem that a) needs to be solved from a business point of view, and b) should be solved from a civic-minded point of view.
I think the problem I see with this type of response is that it doesn't take into context the waste of resources involved. If the 700M users per week is legitimate then my question to you is: how many of those invocations are worth the cost of resources that are spent, in the name of things that are truly productive?
And if AI was truly the holy grail that it's being sold as then there wouldn't be 700M users per week wasting all of these resources as heavily as we are because generative AI would have already solved for something better. It really does seem like these platforms are, and won't be, anywhere as useful as they're continuously claimed to be.
Just like Tesla FSD, we keep hearing about a "breakaway" model and the broken record of AGI. Instead of getting anything exceptionally better we seem to be getting models tuned for benchmarks and only marginal improvements.
I really try to limit what I'm using an LLM for these days. And not simply because of the resource pigs they are, but because it's also often a time sink. I spent an hour today testing out GPT-5 and asking it about a specific problem I was solving for using only 2 well documented technologies. After that hour it had hallucinated about a half dozen assumptions that were completely incorrect. One so obvious that I couldn't understand how it had gotten it so wrong. This particular technology, by default, consumes raw SSE. But GPT-5, even after telling it that it was wrong, continued to give me examples that were in a lot of ways worse and kept resorting to telling me to validate my server responses were JSON formatted in a particularly odd way.
Instead of continuing to waste my time correcting the model I just went back to reading the docs and GitHub issues to figure out the problem I was solving for. And that led me down a dark chain of thought: so what happens when the "teaching" mode rethinks history, or math fundamentals?
I'm sure a lot of people think ChatGPT is incredibly useful. And a lot of people are bought into not wanting to miss the boat, especially those who don't have any clue to how it works and what it takes to execute any given prompt. I actually think LLMs have a trajectory that will be similar to social media. The curve is different and I, hopefully, don't think we've seen the most useful aspects of it come to fruition as of yet. But I do think that if OpenAI is serving 700M users per week then, once again, we are the product. Because if AI could actually displace workers en masse today you wouldn't have access to it for $20/month. And they wouldn't offer it to you at 50% off for the next 3 months when you go to hit the cancel button. In fact, if it could do most of the things executives are claiming then you wouldn't have access to it at all. But, again, the users are the product - in very much the same way social media played into.
Finally, I'd surmise that of those 700M weekly users less than 10% of those sessions are being used for anything productive that you've mentioned and I'd place a high wager that the 10% is wildly conservative. I could be wrong, but again - we'd know about that if it were the actual truth.
> If the 700M users per week is legitimate then my question to you is: how many of those invocations are worth the cost of resources that are spent, in the name of things that are truly productive?
Is everything you spend resources on truly productive?
Who determines whether something is worth it? Is price/willingness of both parties to transact not an important factor?
I don't think ChatGPT can do most things I do. But it does eliminate drudgery.
I don't believe everything in my world is as efficient as it could be. But I genuinely think about the costs involved [0]. When doing automations that are perfectly handled by deterministic systems why would I put the outcomes of those in the hands of a non-deterministic one? And at that cost differential?
We know a few things: LLMs are not efficient, LLMs are consuming more water than traditional compute, we know the providers know but they haven't shared any tangible metrics, and the build process involves, also, an exceptional amount of time, wattage and water.
For me it's: if you have access to a supercomputer do you use it to tell you a joke or work on a life saving medicine?
We didn't have these tools 5 years ago. 5 years ago you dealt with said "drudgery". On the other hand you then say it can't do "most things I do". It seems as though the lines of fatalism and paradox are in full force for a lot of the arguments around AI.
I think the real kicker for me this week (and it changes week-over-week, which is at least entertaining) is when Paul Graham told his Twitter feed [1] a "hotshot" programmer is writing 10k LOC that are not "bug-filled crap" in 12 hours. That's 14 LOC per minute. Compared to industry norms of 50-150 LOC per 8 hour day. Apparently,this "hot-shot" is not "naive", though, implying that it's most definitely legit.
> When doing automations that are perfectly handled by deterministic systems why would I put the outcomes of those in the hands of a non-deterministic one?
The stuff I'm punting isn't stuff I can automate. It's stuff like, "build me a quick command line tool to model passes from this set of possible orbits" or "convert this bulleted list to a course articulation in the format preferred by the University of California" or "Tell me the 5 worst sentences in this draft and give me proposed fixes."
Human assistants that I would punt this stuff to also consume a lot of wattage and power. ;)
> We didn't have these tools 5 years ago. 5 years ago you dealt with said "drudgery". On the other hand you then say it can't do "most things I do".
I'm not sure why you think this is paradoxical.
I probably eliminate 20-30% of tasks at this point with AI. Honestly, it probably does these tasks better than I would (not better than I could, but you can't give maximum effort on everything). As a result, I get 30-40% more done, and a bigger proportion of it is higher value work.
And, AI sometimes helps me with stuff that I -can't- do, like making a good illustration of something. It doesn't surpass top humans at this stuff, but it surpasses me and probably even where I can get to with reasonable effort.
It is absolutely impossible that human assistants being given those tasks would use even remotely within the same order of magnitude the power that LLM’s use.
I am not an anti-LLM’er here but having models that are this power hungry and this generalisable makes no sense economically in the long term. Why would the model that you use to build a command tool have to be able to produce poetry? You’re paying a premium for seldom used flexibility.
Either the power drain will have to come down, prices at the consumer margin significantly up or the whole thing comes crashing down like a house of cards.
> It is absolutely impossible that human assistants being given those tasks would use even remotely within the same order of magnitude the power that LLM’s use.
A human eats 2000 kilocalories of food per day.
Thus, sitting around for an hour to do a task takes 350kJ of food energy. Depending on what people eat, it's 350kJ to 7000kJ of fossil fuel energy in to get that much food energy. In the West, we eat a lot of meat, so expect the high end of this range.
The low end-- 350kJ-- is enough to answer 100-200 ChatGPT requests. It's generous, too, because humans also have an amortized share of sleep and non-working time, other energy inputs/uses to keep them alive, eat fancier food, use energy for recreation, drive to work, etc.
Shoot, just lighting their part of the room they sit in is probably 90kJ.
> I am not an anti-LLM’er here but having models that are this power hungry and this generalisable makes no sense economically in the long term. Why would the model that you use to build a command tool have to be able to produce poetry? You’re paying a premium for seldom used flexibility.
Modern Mixture-of-Experts (MoE) models don't activate the parameters/do the math related to poetry, but just light up a portion of the model that the router expects to be most useful.
Of course, we've found that broader training for LLMs increases their usefulness even on loosely related tasks.
> Either the power drain will have to come down, prices at the consumer margin significantly up
I think we all expect some mixture of these: LLM usefulness goes up, LLM cost goes up, LLM efficiency goes up.
Reading your two comments in conjunction - I find your take reasonable, so I apologise for jumping the gun and going knee first in my previous comment. It was early where I was, but should be no excuse.
I feel like if you're going to go down the route of the energy consumption needed to sustain the entire human organism, you have to do that on the other side as well - as the actual activation cost of human neurons and articulating fingers to operate a keyboard won't be in that range - but you went for the low ball so I'm not going to argue that, as you didn't argue some of the other stuff that sustains humans.
But I will argue the wider implication of your comment that a like-for-like comparison is easy - it's not, so leaving it in the neuron activation space energy cost would probably be simpler to calculate, and there you'd arrive at a smaller ChatGPT ratio. More like 10-20, as opposed to 100-200. I will concede to you that economies of scale mean that there's an energy efficiency in sustaining a ChatGPT workforce compared to a human workforce, if we really want to go full dystopian, but that there's also outsized energy inefficiency in needing the industry and using the materials to construct a ChatGPT workforce large enough to sustain the economies of scale, compared to humans which we kind of have and are stuck with.
There is a wider point that ChatGPT is less autonomous than an assistant, as no matter the tenure with it, you'll not give it the level of autonomy that a human assistant would have as it would self correct to a level where you'd be comfortable with that. So you need a human at the wheel, which will spend some of that human brain power and finger articulation, so you have to add that to the scale of the ChatGPT workflow energy cost.
Having said all that - you make a good point with MoE - but the router activation is inefficient; and the experts are still outsized to the processing required to do the task at hand - but what I argue is that this will get better with further distillation, specialisation and better routing however only for economically viable task pathways. I think we agree on this, reading between the lines.
I would argue though (but this is an assumption, I haven't seen data on neuron activation at task level) that for writing a command-line tool, the neurons still have to activate in a sufficiently large manner to parse a natural language input, abstract it and construct formal language output that will pass the parsers. So you would be spending a higher range of energy than for an average Chat GPT task
In the end - you seem to agree with me that the current unit economics are unsustainable, and we'll need three processes to make them sustainable - cost going up, efficiency going up and usefulness going up. Unless usefulness goes up radically (which it won't due to scaling limitations of LLM's), full autonomy won't be possible, so the value of the additional labour will need to be very marginal to a human, which - given the scaling laws of GPU's - doesn't seem likely.
Meanwhile - we're telling the masses at large to get on with the programme, without considering that maybe for some classes of tasks it just won't be economically viable; which creates lock in and might be difficult disentangle in the future.
All because we must maintain the vibes that this technology is more powerful than it actually is. And that frustrates me, because there's plenty pathways where it's obvious it will be viable, and instead of doubling down on those, we insist on generalisability.
> There is a wider point that ChatGPT is less autonomous than an assistant, as no matter the tenure with it, you'll not give it the level of autonomy that a human assistant would have as it would self correct to a level where you'd be comfortable with that.
IDK. I didn't give human entry level employees that much autonomy. ChatGPT runs off and does things for a minute or two consuming thousands and thousands of tokens, which is a lot like letting someone junior spin for several hours.
Indeed, the cost is so low -- better to let it "see its vision through" than to interrupt it. A lot of the reason why I'd manage junior employees closely are to A) contain costs, and B) prevent discouragement. Neither of those apply here.
(And, you know -- getting the thing back while I remember exactly what I asked and still have some context to rapidly interpret the result-- this is qualitatively different from getting back work from a junior employee hours later).
> that maybe for some classes of tasks it just won't be economically viable;
Running an LLM is expensive. But it's expensive in the sense "serving a human costs about the same as a long distance phone call in the 90's." And the vast majority of businesses did not worry about what they were expending on long distance too much.
And the cost can be expected to decrease, even though the price will go up from "free." I don't expect it will go up too high; some players will have advantages from scale and special sauce to make things more efficient, but it's looking like the barriers to entry are not that substantial.
The unit economics is fine. Inference cost has reduced several orders of magnitude over the last couple years. It's pretty cheap.
Open AI reportedly had a loss of $5B last year. That's really small for a service with hundreds of millions of users (most of which are free and not monetized in any way). That means Open AI could easily turn a profit with ads, however they may choose to implement it.
> so what happens when the "teaching" mode rethinks history, or math fundamentals?
The person attempting to learn either (hopefully) figures out the AI model was wrong, or sadly learns the wrong material. The level of impact is probably quite relative to how useful the knowledge is one's life.
The good or bad news, depending on how you look at it, is that humans are already great at rewriting history and believing wrong facts, so I am not entirely sure an LLM can do that much worse.
Maybe ChatGPT might just kill of the ignorant like it already has? GPT already told a user to combine bleach and vinegar, which produces chlorine gas. [1]
The only solution to those people starving to death is to kill the people that benefit from them starving to death. It's a solved problem, the solution isn't palatable. No one is starving to death because of a lack of engineering prowess.
> The only solution to those people starving to death is to kill the people that benefit from them starving to death.
There are solutions other than "to kill the people that benefit", such as what have existed for many years, including but not limited to:
- Efforts such as the recently emasculated USAID[0].
- Humanitarian NGO's[1] such as the World Central Kitchen[2]
and the Red Cross[3].
- The will of those who could help to help those in need[4].
Note that none of the aforementioned require executions nor engineering prowess.
Figuring out how to align misaligned incentives is an engineering problem. Obviously I disavow what you said, I reject all forms of advocacy of violence.
>>> People are starving to death and the world's brightest engineers are ...
>> This is a political will, empathy, and leadership problem. Not an engineering problem.
> Those problems might be more tractable if all of our best and brightest were working on them.
The ability to produce enough food for those in need already exists, so that problem is theoretically solved. Granted, logistics engineering[0] is a real thing and would benefit from "our best and brightest."
What is lacking most recently, based on empirical observation, is a commitment to benefiting those in need without expectation of remuneration. Or, in other words, empathetic acts of kindness.
Which is a "people problem" (a.k.a. the trio I previously identified).
Famine in the modern world is almost entirely caused by dysfunctional governments and/or armed conflicts. Engineers have basically nothing to do with either of those.
This sort of "there are bad things in the world, therefore focusing on anything else is bad" thinking is generally misguided.
Famine is mostly political but engineers (not all of them) definitely have to do with it. If you’re building powerful AI for corporations that are then involved with the political entities that caused the famine, then you can’t claim to basically have nothing to do with it.
You can disagree all you want but the exact wording used in original comment that I responded to was
> Engineers have basically nothing to do with either of those.
The logic here is “If A is actively working to develop capabilities for B, which B offers up to C who then uses it to do D, then A cannot claim to have nothing to do with D.”
the existence of poor hungry people feeds the fear of becoming poor and hungry which drives those brightest engineers. I.e. the things work as intended, unfortunately.
They won’t be honest and explain it to you but I will. Takes like the one you’re responding to are from loathsome pessimistic anti-llm people that are so far detached from reality they can just confidently assert things that have no bearing on truth or evidence. It’s a coping mechanism and it’s basically a prolific mental illness at this point
And what does that make you? A "loathsome clueless pro-llm zealot detached from reality"? LLMs are essentially next word predictors marketed as oracles. And people use them as that. And that's killing them. Because LLMs don't actually "know", they don't "know that they don't know", and won't tell you they are inadequate when they are. And that's a problem left completely unsolved. At the core of very legitimate concerns about the proliferation of LLMs. If someone here sounds irrational and "coping", it very much appears to be you.
Side note: The organization that maintains Lean is a "Focused Research Organization", which is a new model for running a science/discovery based nonprofit. This might be useful knowledge for founder types who are interested in research. For more information, see: https://www.convergentresearch.org
The concept trying new science orgs is noble, but this is the typical Schmidt BS of saying every previous academic consortia is totally incompetent and I'm the only one that can inject the magic sauce of focus and coordination.
Unfortunately being noble or self righteous or whatever emotion you choose has nothing to do with it. If there is a pool of grant money available only to “Focused Research Organizations,” and you want some of it for your work, then you open one and do your work under that umbrella. Academic institutions themselves do this all the time. It looks politically and morally sketchy, and maybe it often is, but it’s the way it works.
To me, it seems like coming up with something more coordinated than a consortium and more flexible than a single lab or a research corporation funded by multiple universities makes sense.
It's probably a narrow set of problems with the right set of constraints and scale for this to be a win.
Having an organization maintain a software tool seems pretty unsurprising. There’s a well-defined problem with easily visible deliverables, relatively little research risk, and small organizations routinely maintain software tools all the time. Whereas broader research is full of risk and requires funders be enormously patient and willing to fund crazy ideas that don’t make sense.
Hmm. I don't know very much about Lean, and it definitely feels smaller in scope and coordination risk than the kinds of things that would generally benefit from this.
(OTOH, within the community they're effectively trying to build a massive, modern Principia Mathematica, so maybe they would...)
> Whereas broader research is full of risk and requires funders be enormously patient and willing to fund crazy ideas that don’t make sense.
Yah. I'm not a researcher, but I keep ending up tangentially involved in research communities. I've seen university labs, loose research networks, loose consortia funding research centers, FFRDC, etc.
What I’ve noticed is that a lot of these consortia or networks struggle to deliver anything cohesive. There's too many stakeholders, limited bandwidth, and nobody quite empowered to say “we’re building this.”
In the cases where there’s a clearly scoped, tractable problem that’s bigger than what a single lab can handle, and a group of stakeholders agrees it’s worth a visionary push, something like an FRO might make a lot of sense.
This is an incredibly bad take on a hard social problem which is hard for reasons that are well understood.
Scientific research is often not immediately applicable, but can still be valuable. The number of people that can tell you if it's valuable are small, and as our scientific knowledge improves, the number of people who know what's going on shrinks and shrinks.
Separately, it's possible to spend many years researching something, and have very little to show for it. The scientists in that situation also want some kind of assurance that they will be able to pay their bills.
Between the high rate of failure, and conflicts of interest, and inscrutability of the research topics. It's very hard to efficiently fund science, and all the current ways of doing it are far from optimal. There is waste, there is grift, there is politics. Any improvement here is welcome, and decreasing the dollar cost per scientific discovery is more important than the research itself in any single field.
Some days I joke that there should be a set of Nobel prizes for making machines quieter. Categories could include: air-conditioning units and mini-fridges, construction and landscaping equipment, old university buildings, pump-housings, etc. The quality of life of many would be improved if we had quieter machines. It boggles my mind that a) in many hotel rooms one can hear a good deal of machine noise and neighbors' televisions, and b) that some sort of noise score (as calculated from DB meter measurements) isn't more widely available for things like apartment rentals, conference room bookings, etc.
what about a noise tax? my city has some electric buses and some ancient buses - the difference obviously is absolutely huge, but right now the financial incentives aren't there to upgrade the whole fleet
Noise from construction machines is actually a feature. They all have added backup beepers at this point as required per OSHA guidelines. Audible for well over a mile in normal conditions
Tesco delivery trucks have them here in Ireland; it's pretty good stuff. Still quite loud/noticeable when you're up close, while at the same time not being completely obnoxious to everyone in a kilometre radius.
They have to be loud enough to be heard through hearing protection. The amplitude is a feature.
It's a "solved problem" in the sense that nuclear energy is a solved problem. There's no mandate to actually see widespread roll out of anything that may be a better solution.
There's a construction site near me at present. There is always 1 machine in reverse, at all times. The utility of having a backup beeper or any noise making device on that site is thus zero. It is the single largest source of noise pollution, larger than the roadway
>The utility of having a backup beeper or any noise making device on that site is thus zero.
This strikes me as an odd take, maybe from someone who has never worked on a construction site.
Our auditory sense is more than just a binary “present/not present” detection. We can sense distance and direction. Just because there is a backup beeper somewhere on site does not mean there is no value to any other auditory signal.
Think about when you’re in a congested city. There’s probably a lot of ambient car noise, including horns, in the background. That doesn’t mean you’re unable to react to a honking car in your immediate vicinity.
You just believe you can sense the direction of loud noises in urban environments. Our nervous system has no "404 not found" for positional awareness. Even after severe head trauma, you have a sense of position for everything. It's so wrong as to be useless, but you have it.
Ask anyone who's been at a shooting in a city. Everyone gives a different answer for where the shooter was at. It's such a severe issue the US Army has microphone arrays they equip urban combat vehicles with. Even with bullets actually bouncing off the armor the troops cannot accurately locate the direction of the shooter(s).
As the other poster mentioned, the characteristics of sound matter. That’s why the report of a firearm is a bad example.
But there are more commonplace examples. Older phone ringtones are often hard for people to locate, but nearly everybody can pinpoint the sound of a dropped coin. Sound perception is more complex than just perception of pressure levels. To the point above, you wouldn’t confuse a car honking in front of you with one behind you even in the presence of ambiguous ambient noise.
I'm not talking about the report of a firearm. I'm talking about the physical impact of the bullet on the armored vehicle you are in.
Also I have no idea what you mean by "but nearly everybody can pinpoint the sound of a dropped coin". What sound does a coin make when it is dropped on a busy street?
> They have to be loud enough to be heard through hearing protection.
It's kind of a nit-pick, but this is not really true.
Very approximately, you will perceive a sound if it is above your threshold of hearing, and also not masked by other sounds.
If you're wearing the best ear defenders which attenuate all sounds by about 30dB, and you assume your threshold of hearing is 10dBSPL (conservative), any sound above 40dBSPL is above the threshold of hearing. That's the level of a quiet conversation.
And because your ear defenders attenuate all sounds, masking is not really affected -- the sounds which would be masking the reversing beepers are also quieter.
There are nuances of course (hearing damage, and all the complicated effects that wearing ear defenders cause), but none of them are to the point that loud reversing noises are required because of hearing protection -- they are required to be heard over all the other loud noises on a construction site.
> The utility of having a backup beeper or any noise making device on that site is thus zero.
The inverse square law says otherwise; on site the distances will be much more apparent.
Exactly. That’s where the other comment about a “noise tax”, or enact fines for exceeding limits, are probably necessary to shift the calculus.
Japan is a good case study [1]. If nothing else, it’s fun to look at the charts showing noise reductions—not just in aggregate, but for each contributing input (e.g. engine, intake, exhaust, tires, cooling)—for both passenger vehicles and heavy equipment. Unfortunately, in the US, we have a few obstacles to legislation like this, least of which being public apathy as majority of voters who are not exposed to high sound levels daily.
“Japan's primary legislation governing noise regulation is the Environmental Noise Regulation Act, first introduced in 1986 and subsequently amended in 1999. This act sets different noise limits for different times of the day, with the maximum allowable noise level during the day set at 55 decibels and reduced to 45 decibels at night to prevent disturbances to those who are sleeping. Violators of these standards are subject to penalties.”
