[HN Gopher] AI in my plasma physics research didn't go the way I...
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AI in my plasma physics research didn't go the way I expected
Author : qianli_cs
Score : 328 points
Date : 2025-05-20 04:57 UTC (18 hours ago)
(HTM) web link (www.understandingai.org)
(TXT) w3m dump (www.understandingai.org)
| nicoco wrote:
| I am not a AI booster at all, but the fact that negative results
| are not published and that everyone is overselling their stuff in
| research papers is unfortunately not limited to AI. This is just
| a consequence of the way scientists are evaluated and of the
| scientific publishing industry, which basically suffers from the
| same shit than traditional media does (craving for audience).
|
| Anyway, winter is coming, innit?
| croes wrote:
| But AI makes it easier to write convincing looking papers
| moravak1984 wrote:
| Sure, it's not. But often on AI papers one sees remarks that
| actually mean: "...and if you throw in one zillion GPUs and
| make them run until the end of time you get {magic_benchmark}".
| Or "if you evaluate this very smart algo in our super-secret,
| real-life dataset that we claim is available on request, but
| we'd ghost you if you dare to ask, then you will see this chart
| that shows how smart we are".
|
| Sure, it is often flag-planting, but when these papers come
| from big corps, you cannot "just ignore them and keep on" even
| when there are obvious flaws/issues.
|
| It's a race over resources, as a (former) researcher on a low-
| budget university, we just cannot compete. We are coerced to
| believe whatever figure is passed on in the literature as
| "benchmark", without possibility of replication.
| nicoco wrote:
| I agree with that. Classically used "AI benchmarks" need to
| be questioned. In my field, these guys have dropped a bomb,
| and no one seem to care:
| https://hal.science/hal-04715638/document
| baxtr wrote:
| Can you give brief summary why this paper is a breakthrough
| for an outsider of the field?
| mzl wrote:
| Checking it shortly (I haven't seen the paper before)
| this seems to be a very good analysis of how results are
| reported specifically for medical imaging benchmarks.
|
| As is often the case with statistics, selecting just a
| single number to report (whatever that number is) will
| hide a lot of different behaviours. Here, they show that
| just using the mean is a bad way to report data as the
| confidence intervals (reconstructed by the methods in the
| paper in most cases) show that the models can't really be
| distinguished based on their mean.
| amarcheschi wrote:
| Hell, I was asked to use confidence interval as well as
| average values for by bs thesis when doing ml benchmarks
| and scientist publishing results in medical fields aren't
| doing it...
|
| How can something like that happen? I mean, i had a
| supervisor tell me "add the confidence interval to the
| results as well", and explained me why. I guess that at
| nobody ever told them? Or they didn't care? Or it's just
| a honest mistake
| stogot wrote:
| Is it because it's word-of-mouth and not written down in
| some NSF (or other organization) guidance? Thiss seems to
| be the issue
| amarcheschi wrote:
| That might be, but couldn't a paper be asked to include
| that to be published? It looks like an important
| information
| nicoco wrote:
| I don't think it qualifies as a breakthrough. In short:
|
| 1. Segmentation is a very classical in medical image
| processing. 2. Everyday there are papers claiming that
| they beat the state of the art 3. This paper says that
| most of the time, the state of the art has not been beat
| because they actually are in the margin of error.
| aleph_minus_one wrote:
| > It's a race over resources, as a (former) researcher on a
| low-budget university, we just cannot compete. We are coerced
| to believe whatever figure is passed on in the literature as
| "benchmark", without possibility of replication.
|
| The central purpose of university research has basically
| always been that researchers work on hard, foundational
| topics that are more long-term so that industry is hardly
| willing to do them. On the other hand, these topics are very
| important, that is why the respective country is willing to
| finance this foundational research.
|
| Thus, if you are at a university, once your research topic
| becomes an arms race with industry, you simply work either at
| the wrong place (university instead of industry) or on a
| "wrong" topic in the respective research area (look for some
| much more long-term, experimental topics that, if you are
| right, might change the whole research area in, say, 15
| years, instead of some high resource-intensive, minor
| improvements to existing models).
| KurSix wrote:
| AI just happens to be the current hype magnet, so the cracks
| show more clearly
| asoneth wrote:
| I published my first papers a little over fifteen years ago on
| practical applications for AI before switching domains.
| Recently I've been sucked back in.
|
| I agree it's a problem across all of science, but AI seems to
| attract more than it's fair share of researchers seeking fame
| and fortune. Exaggerated claims and cherry-picking data seem
| even more extreme in my limited experience, and even
| responsible researchers end up exaggerating a bit to try and
| compete.
| BenFranklin100 wrote:
| The author is a Princeton PhD grad working in physics. Funding
| for this type of work usually comes from the NSF. NSF is under
| attack by DOGE, and Trump has proposed slashing the NSF budget by
| 55%.
|
| A reason used to justify these massive cuts is that AI will soon
| replace traditional research. This post demonstrates this
| assumption is likely false.
| surfingdino wrote:
| When politicians get involved in research scientific proof and
| reason aren't always winning.
| adastra22 wrote:
| Politicians have been involved in research for over a
| century.
| toolslive wrote:
| Didn't a politician invent the internet ?
| TypingOutBugs wrote:
| Who?
| sundarurfriend wrote:
| Al Gore played a big role in getting political (and hence
| economic) support for the expansion of the Internet.
|
| https://en.wikipedia.org/wiki/Al_Gore_and_information_tec
| hno... :
|
| > Al Gore, a strong and knowledgeable proponent of the
| Internet, promoted legislation that resulted in President
| George H.W Bush signing the High Performance Computing
| and Communication Act of 1991. This Act allocated $600
| million
|
| > In the early 1990s the Internet was big news ... In the
| fall of 1990, there were just 313,000 computers on the
| Internet; by 1996, there were close to 10 million. The
| networking idea became politicized during the 1992
| Clinton-Gore election campaign, where the rhetoric of the
| information highway captured the public imagination.
|
| Your parent comment is either joining in on the ridicule
| side or at least in misquoting:
|
| > Gore became the subject of controversy and ridicule
| when his statement, "I took the initiative in creating
| the Internet", was widely quoted out of context. It was
| often misquoted by comedians and figures in American
| popular media who framed this statement as a claim that
| Gore believed he had personally invented the
| Internet.[54] Gore's actual words were widely reaffirmed
| by notable Internet pioneers, such as Vint Cerf and Bob
| Kahn, who stated, "No one in public life has been more
| intellectually engaged in helping to create the climate
| for a thriving Internet than the Vice President."
| eschaton wrote:
| That's a meme created and spread by pseudo-journalist
| Declan McCullagh specifically to tar Al Gore in the lead-up
| to the 2000 election.
|
| Specifically, Gore said in an interview that he "took the
| initiative in creating the Internet" by introducing the
| bill to allow commercial traffic on ARPAnet, which
| McCullagh twisted in an article to "Al Gote claimed he
| invented the Internet" in order to to smear him.
| sundarurfriend wrote:
| Given how close that election turned out to be, this
| smear campaign likely changed the presidency, and given
| George WMD Bush's actions, changed the course of the
| world for the worse in many ways. (For those who were too
| young or not yet born at the time, these jokes were
| MASSIVE to the extent that became largely Al Gore was
| known for, for years after. So it's not much of an
| exaggeration to say they had a material impact on his
| perception and hence the votes.)
|
| Al Gore understood technology, the internet, was a
| champion for the environment, and it's unbelievable today
| that he came that close to presidency (and then lost).
| When people say "we live in the bad timeline", one of the
| closest good timelines is probably one where this
| election went differently.
| surfingdino wrote:
| Nope. Most of the early core research was done by RAND
| Corporation.
| thrdbndndn wrote:
| Kinda off-topic, but about the cutting itself:
|
| I used to work in academia and was involved in NSF-funded
| programs, so I have mixed feelings about this. Waste and
| inefficiency are rampant. BTW I'm not talking about failed
| projects or research that were dimmed "not important", but
| things like buying million-dollar equipment just to use up the
| budget, which then sits idle.
|
| That said, slashing NSF funding by 50% won't fix any of that.
| It'll just shrink both the waste and the genuinely valuable
| research proportionally. So it's indeed a serious blow to real
| scientific progress.
|
| I don't really have a point, just want to put it here. Also to
| be fair this kind of inefficiency isn't unique to academia;
| it's common anywhere public money is involved.
| nottorp wrote:
| > but things like buying million-dollar equipment just to use
| up the budget, which then sits idle.
|
| That's not specific to the US, and it's because of a perverse
| incentive coming from those who assign the funds.
|
| If you don't use them they cut your funding for the next
| cycle.
| thrdbndndn wrote:
| This is a common saying, and I'm sure there's some truth to
| it. But IMHO, the main reason is that PIs just want to use
| the money.
|
| Because... why not? It's free money. Having the newest
| shiny equipment, at the very least, boosts your group's
| reputation. Not to mention that straight-up corruption
| (pocketing funds for personal gain) is not unheard of.
| dgb23 wrote:
| Even considering these inefficiencies, which are certainly to
| be taken seriously, there aren't a lot of things that have
| such a high ROI as research and education.
| Upvoter33 wrote:
| Yeah companies never waste money.
|
| I'm tired of all the complaining about waste and overhead in
| academics. Companies waste money all the time...
| BenFranklin100 wrote:
| Research is a human endeavor and as all human endeavors it
| can be improved. But having personally known NSF funded
| researchers in the past, I can confidently say these are some
| of the most dedicated, hard-working people you will ever
| meet. Most of these people work 60-70 hrs a week for half the
| salary they could get in industry.
|
| Also, industry is far from perfect too. Think of high-profile
| failures like the Apple car or Meta's Metaverse, both of
| which wasted literally tens of billions of dollars, many
| times the entire annual NSF budget. Let's not hold scientists
| to standards applied no where else.
| rhubarbtree wrote:
| I think this is mostly just a repeat of the problems of academia
| - no longer truth-seeking, instead focused on citations and
| careerism. AI is just a.n.other topic where that is happening.
| geremiiah wrote:
| I don't want to generalize because I do not know how widespread
| this pattern is, but my job has me hopping between a few HPC
| centers around Germany, and a pattern I notice is that, a lot
| of these places are chuck full of reject physicists, and a lot
| of the AI funding that gets distributed gets gobbled up by
| these people and the consequence of which is a lot of these
| ML4Science projects. I personally think it is a bit of a shame,
| because HPC centers are not there to only serve physicists, and
| especially with AI funding we in Germany should be doing more
| AI-core research.
| ktallett wrote:
| HPCs are usually in Collab with universities for specific
| science research. Using up their resources is hopping on the
| bandwagon to damage another industry.an industry (AI) which
| is neither new nor anywhere close to being anything more than
| an personal assistant at the moment. Not even a great one at
| that.
| shusaku wrote:
| > a pattern I notice is that, a lot of these places are chuck
| full of reject physicists
|
| Utter nonsense, these are some of the smartest people in the
| world who do incredibly valuable science.
| barrenko wrote:
| Seriously don't understand what "no longer" does here.
| raesene9 wrote:
| Interesting article. There is always risk that a new hot
| technique will get more attention that it ultimately warrants.
|
| For me the key quote in the article is
|
| "Most scientists aren't trying to mislead anyone, but because
| they face strong incentives to present favorable results, there's
| still a risk that you'll be misled."
|
| Understanding people's incentives is often very useful when
| you're looking at what they're saying.
| ktallett wrote:
| There are those who have realised they can make a lot of cash
| from it and also get funding by using the term AI. But at the
| end of the day what software doesn't have some machine learning
| built in. It's nothing new, nor is the current implementations
| particularly extraordinary or accurate.
| asdff wrote:
| Plenty of software has zero ML. But either way not all ML is
| the same. There are many different algorithms each with their
| own tradeoffs. AI as it is presently marketed however usually
| means one type of AI, large language model, which also has
| tradeoffs, and is a bit new to the scene compared to say
| markov chains whose history starts in the early 1900s.
| pawanjswal wrote:
| Appreciate the honesty. AI isn't magic, and it's refreshing to
| see someone actually say it out loud.
| omneity wrote:
| The article initially appears to suggest that all AI in science
| (or at least the author's field) is hype. But their gripe seems
| to be specific to an architecture named PINN that seems to be
| overhyped, as they mention in the end how they end up using other
| DL models to successfully compute PDEs faster than traditional
| numerical methods.
| hyttioaoa wrote:
| He published a whole paper providing a systematic analysis of a
| wide range of models. There's a whole section on that. So it's
| not specific to PINN.
| nottorp wrote:
| Replace PINN with any "AI" solution for anything and you'll
| still find it overhyped.
|
| The only realistic evaluations of "AI" so far are those that
| admit it's only useful for experts to skip some boring work.
| And triple check the output after.
| geremiiah wrote:
| It's more widespread than PINNs. PINNs have been widely known
| to be rubbish a long time ago. But the general failure of using
| ML for physics problems is much more widespread.
|
| Where ML generally shines is either when you have relatively
| lots of experimental data with respect to a fairly narrow
| domain. This is the case for machine learned interatomic
| potentials MLIPs which have been a thing since the '90s. Also
| potentially the case for weather modelling (but I do not want
| to comment about that). Or when you have absolute insane
| amounts of data, and you train a really huge model. This is
| what we refer to as AI. This is basically why Alphafold is
| successful, and Alphafold still fails to produce good results
| when you query it on inputs that are far from any data points
| in its training data.
|
| But most ML for physics problems tend to be somewhere in
| between. Lacking experimental data and working with not enough
| simulation data because it is so expensive to produce. And also
| training models that are not large enough, because inference
| would be too slow, anyway, if they were too big. And then
| expecting these models to learn a very wide range of physics.
|
| And then everyone jumps in on the hype train, because it is so
| easy to give it a shot. And everyone gets the same dud results.
| But then they publish anyway. And if the lab/PI is famous
| enough or if they formulate the problem in a way that is unique
| and looks sciency or mathy, they might even get their paper in
| a good journal/conference and get lots of citations. But in the
| end, they still only end up with the same results as everyone
| else: replicates the training data to some extent, somebody
| else should work on the generalizability problem.
| kumarvvr wrote:
| Are complex math problems just solvable by LLMs, as a stream of
| language tokens?
|
| I mean, there ought to be an element of abstract thought,
| abstract reasoning, abstract inter-linking of concepts, etc, to
| enable mathematicians to solve complex math theorems and
| problems.
|
| What am I missing?
| geremiiah wrote:
| LLMs are not involved anywhere. You start with some data.
| Either simulation data or experimental data. Then you train a
| model to either learn a time evolution operator or a force
| field. Then you apply it to more input data, and you visualize
| the results.
|
| One typical use case is that the simulation data takes months
| to generate. So for experimental use cases, it is very slow. So
| the idea was, to train a model that can learn the underlying
| physics. The model will be small enough so that inference won't
| be prohibitively expensive. So you can then use the ML model in
| lieu of the classical physics based model.
|
| Where this usually fails is that while ML models can be trained
| well enough to replicate the training data, they typically fail
| to generalize well outside of the domain and regime of the
| training data. So unless your experimental problems are
| entirely within the same domains and regimes as the training
| data, your model is of not much use.
|
| So claims of generalizability and applicability are always
| dubious.
|
| Lots of publications on this topic follow the same pattern:
| conceive of a new architecture or formalism, train an ML model
| on widely available data, results show that it can reproduce
| the training data to some extent, mention generalizability in
| the discussion but never test it.
| thrdbndndn wrote:
| I understand lots of people would (rightfully) say "no shit", but
| I think it's good to actually describe how it is in details. So
| kudos to the author.
| intended wrote:
| Verification is at the heart of economic and intellectual
| activity.
| yapyap wrote:
| Glad to see some people who bought into the nonsense are waking
| up
| plasticeagle wrote:
| Does anybody else find it peculiar that the majority of these
| articles about AI say things like "of course I don't doubt that
| AI will lead to major discoveries", and then go on to explain how
| they aren't useful in any field whatsoever?
|
| Where are the AI-driven breakthroughs? Or even the AI-driven
| incremental improvements? Do they exist anywhere? Or are we just
| using AI to remix existing general knowledge, while making no
| progress of any sort in any field using it?
| moffkalast wrote:
| > AlphaEvolve's procedure found an algorithm to multiply 4x4
| complex-valued matrices using 48 scalar multiplications,
| improving upon Strassen's 1969 algorithm that was previously
| known as the best in this setting. This finding demonstrates a
| significant advance over our previous work, AlphaTensor, which
| specialized in matrix multiplication algorithms, and for 4x4
| matrices, only found improvements for binary arithmetic.
|
| > To investigate AlphaEvolve's breadth, we applied the system
| to over 50 open problems in mathematical analysis, geometry,
| combinatorics and number theory. The system's flexibility
| enabled us to set up most experiments in a matter of hours. In
| roughly 75% of cases, it rediscovered state-of-the-art
| solutions, to the best of our knowledge.
|
| > And in 20% of cases, AlphaEvolve improved the previously best
| known solutions, making progress on the corresponding open
| problems. For example, it advanced the kissing number problem.
| This geometric challenge has fascinated mathematicians for over
| 300 years and concerns the maximum number of non-overlapping
| spheres that touch a common unit sphere. AlphaEvolve discovered
| a configuration of 593 outer spheres and established a new
| lower bound in 11 dimensions.
|
| https://storage.googleapis.com/deepmind-media/DeepMind.com/B...
|
| (this is an LLM driven pipeline)
| Ygg2 wrote:
| That's less LLM and more three projects by DeepMind team.
|
| And it's far from commercial availability.
| moffkalast wrote:
| Well to me personally it at least proves something that's
| long been touted as impossible, that the current
| architecture can in fact do better than all humans at novel
| tasks, even if it needs a crutch at the moment.
|
| An LLM-based system now holds the SOTA approach on several
| math problems, how crazy is that? I wasn't convinced
| before, but now I guess it won't be many decades before we
| view making new scientific advancements as viable as
| winning against stockfish.
| Ygg2 wrote:
| Yeah, but those rely on setting up the evolve function.
| And they aren't well guaranteed to be better than humans.
| They might find you an improvement, but aren't guaranteed
| to do as shown here[1] (green means better, red means
| worse, gray means same).
|
| [1] https://youtu.be/sGCmu7YKgPA?t=480
| moffkalast wrote:
| Last I checked, humans weren't guaranteed to do anything
| either.
| Ygg2 wrote:
| Sure, but they have one thing that makes them better than
| CPUs, they consume more ;)
| simianparrot wrote:
| It's why it keeps looking exactly like NFT's and crypto hype
| cycles to me: Yes the technology has legitimate uses, but the
| promises of groundbreaking use cases that will change the world
| are obviously not materialising and to anyone that understands
| the tech it can't.
|
| It's people making money off hype until it dies and move on to
| the next scam-with-some-use.
| guardian5x wrote:
| AI looks exactly like NFTs to you? I don't understand what
| you mean by that. AI already has tons more uses.
| waldrews wrote:
| One is a technical advance as important as anything in
| human history, realizing a dream most informed thinkers
| thought would remain science fiction long past our
| lifetimes, upending our understanding of intelligence,
| computation, language, knowledge, evolution, prediction,
| psychology... before we even mention practical
| applications.
|
| The other is worse than nothing.
| StopDisinfo910 wrote:
| I don't remember when NFTs and cryptos helped me draft an
| email, wrote my meetings minutes for me or allowed me to
| easily search information previously locked in various
| documents.
|
| I think there is this weird take amongst some on HN where
| LLMs are either completely revolutionary and making break
| through or utterly useless.
|
| The truth is that they are useful already as a productivity
| tool.
| bgnn wrote:
| Exactly this. What we expect from them is our speculation.
| In reality nobody knows the future and there's no way to
| know the future.
| bgnn wrote:
| Wow I didn't expect this to be down voted. I guess there
| are people who knows the future.
| ktallett wrote:
| The hype surrounding them is not as a pa and tbh a lot of
| these use cases already have existing methods that work
| just fine. There are ways to find key information in files
| already, and speedy meeting minutes is really just a
| template away.
| rkuodys wrote:
| What I see LLMs at this point is simplified input and
| output solutions with reduced barriers of entry. So
| application could become more widespread.
|
| Now that I think of it, maybe this AI era is not
| electricity, but rather GUI - like the time when Jobs(or
| whoever) figured out and adopted modern GUI on computers
| allowing more widespread uses of computer
| ktallett wrote:
| Do they only have reduced barriers of entry if you aren't
| fussed about the accuracy of the output? If you care that
| everything works correctly and is factually correct, do
| you not need the same competency as just doing the task
| by hand.
| flir wrote:
| It's a good analogy because the key development does seem
| to have been the interface. Instead of wrapping it up as
| a text autocomplete (a la google search), openai wrapped
| it up as an IM client, and we were off to the races.
| Flamentono2 wrote:
| Absolutly not true
|
| I was not able to get meeting transcription in that
| quality that cheap ever before. I followed dictation
| software for over a decade and tx to ML the open source
| software is suddenly a lot better than ever before.
|
| Our internal company search with state of the art search
| indexes and search software was always shit. Now i ask an
| agent about a product standard and it just finds it.
|
| Image generation never existed before.
|
| Building a chatbot in a way that it actually does what
| you expect and its more complicated than answering the
| same 10 theoretical features it can do was hard and never
| really good and it now just works.
|
| Im also not aware of any software rewriting or even
| writing documents for me, structer them etc.
| ktallett wrote:
| A lot of these issues you have had are simply user error
| or not using the right tool for the job.
| StopDisinfo910 wrote:
| Well, LLMs are the right tool for the job. They just
| work.
|
| I mean if you are going to deny their usefulness in the
| face of plenty of people telling you they actually help,
| it's going to be impossible to have a discussion.
| ktallett wrote:
| They can be useful, however for admin tasks, there are
| plenty of valid alternatives that really take no longer
| time wise so why bother using all that computing power.
|
| They don't just work though, they are not fool proof and
| definitely require double checking.
| StopDisinfo910 wrote:
| > valid alternatives that really take no longer time wise
|
| That's not my experience.
|
| We use them more and more at my job. It was already great
| for most office tasks including brainstorming simple
| things but now suppliers are starting to sell us agents
| which pretty much just work and honestly there are a ton
| of things for which LLMs seem really suited for.
|
| CMDB queries? Annoying SAP requests for which you have to
| delve through dozens of menus? The stupid interface of my
| travel management and expense software? Please give me a
| chatbot for all of that which can actually decipher what
| I'm trying to do. These are hours of productivity
| unlocked.
|
| We are also starting to deploy more and more RAG on
| select core business dataset and it's more useful than
| even I anticipated and I'm already convinced. You ask,
| you get a brief answer and the documents back. This used
| to be either hours of delving through search results or
| emails with experts.
|
| As imperfect as they are now, the potential value of LLMs
| is already tremendous.
| ktallett wrote:
| How do you check accuracy of these? You stated
| brainstorming as an example that they are great at. As
| obviously experts are experts for a reason.
|
| My issue here is that a lot of this is solved by good
| practice, for example,travel management and expenses have
| been solved, company credit card. I don't need one
| slightly better piece of software to manage one terrible
| piece of software to solve an issue that has a solution.
| jodrellblank wrote:
| You know what they were doing and what tools they were
| using... how?
| ktallett wrote:
| Ok take Transcription, they were trying to use free as in
| cost tools instead of using software that works
| efficiently that has been effective for decades now.
| Flamentono2 wrote:
| I work for one very big software company.
|
| If this was 'a simple user error' or 'not using the right
| tool for the job' than this was an error from smart
| people and it still got fixed by using AI/ML in an
| instant.
|
| With this, my argument still stands even if it would be
| for a different reason which i personally doubt.
| ktallett wrote:
| Often big companies are the least efficient. And big
| companies can still make mistakes or have very
| inefficient processes. There was already a perfectly
| simple solution to the issue that could have been
| utilised prior to this and overall still the most
| efficient solution.
|
| Also, everyone does dumb things, even smart people do
| dumb things. I do research in a field that many outsiders
| would say you must be smart to do (not my view) and every
| single one of us does dumb shit daily. Anyone who thinks
| they don't isn't as smart as they think they are.
| StopDisinfo910 wrote:
| Microsoft is absolutely selling them as pa and already
| selling a lot. I think HNers being mostly software
| developers live in a bubble when it comes to the reality
| of what LLMs are actually used for.
|
| Speedy minutes are absolutely not a template away. Anyone
| who ever had to write minutes for a complicated meetings
| knows it's hard and requires a lot of back and forth for
| everyone to agree about what was said and decided.
|
| Now you just turn on Copilot and you get both a
| transcript and an adequate basis for good minutes. Bonus
| point: it's made by a machine so no one complains it has
| bias.
|
| Some people here are blind to how useful that is.
| IanCal wrote:
| There are so many tasks in the world that
|
| 1. Involve a computer
|
| 2. Do not require incredible intelligence
|
| 3. Involve the messiness of the real world enough that
| you can't write exact code to do it without it being
| insanely fragile
|
| LLMs suddenly start to tackle these, and tackle them kind
| of all at once. Additionally they are "programmed" in
| just English and so you don't need a specialist to do
| something like change the tone of the summary or format,
| you just write what you want.
|
| Assuming the models never get any smarter or even
| cheaper, and all we get is neater integrations, I still
| think this is all _huge_.
| ktallett wrote:
| Do you really believe the outlay in terms of computer
| power is worth it to change the tone of an email? If it
| never gets better, this is a vast waste of an enormous
| amount of resources.
| IanCal wrote:
| That's not what I've talked about them being for, but
| regardless it depends on the impact surely. If it can
| show you how someone may misunderstand your point and
| either help correct it or just show the problem then yes
| that can easily be worth spending a few cycles on. The
| additional energy cost of further back and forths caused
| by a misunderstanding could very easily be higher. At
| full whack, my GPU draws something like 10x what my
| monitor does, so fixing something quickly and
| automatically can easily use less power than doing it
| automatically.
|
| Again though, that's not at all what I've talked about.
| ktallett wrote:
| This is a business practice issue and staff issue, not a
| meeting minutes issue. I have meetings daily, and have
| never had this issue. You make it clear what is decided
| during the meeting, give anyone a chance to query or
| question, then no one can argue.
| closewith wrote:
| I can guarantee that if you're this obtuse in real life,
| the reason you have no problems with meetings minutes is
| because no-one bothers to meet with you.
| ktallett wrote:
| You would be wrong. I am actually quite perky, I just
| don't suffer foolish admin tasks easily. I only have
| meetings with a goal (not just for the sake of it), and
| then I simply make sure it is clear what the solution is,
| no matter whose idea it was. I don't care about being
| right or wrong in a meeting, I care that we have a useful
| outcome and it isn't an hour wasted. Having a meeting
| whereby the outcome of a meeting is unclear is a complete
| waste of time, and is not solved by tech, it is solved by
| how your meetings are managed.
| closewith wrote:
| > I simply make sure it is clear what the solution is
|
| People simply humour you to get around your personality.
| ktallett wrote:
| You can have your opinion on that.
| lazide wrote:
| Having tried to use various tools - in those specific
| examples - I found them either pointless or actively
| harmful.
|
| Writing emails - once I knew what I wanted to convey, the
| rest was so trivial as to not matter, and any LLM tooling
| just got in the way of actually expressing it as I ended up
| trying to tweak the junk it was producing.
|
| Meeting minutes - I have yet to see one that didn't miss
| something important while creating a lot of junk that no
| one ever read.
|
| And while I'm sure someone somewhere has had luck with the
| document search/extract stuff, my experience has been that
| the hard part was understanding something, and then finding
| it in the doc or being reminded of it was easy. If someone
| didn't understand something, the AI summary or search was
| useless because they didn't know what they were seeing.
|
| I've also seen a LOT of both junior and senior people end
| up in a haze because they couldn't figure out what was
| going on - and the AI tooling just allowed them to produce
| more junk that didn't make any sense, rather than engage
| their brain. Which causes more junk for everyone to get
| overwhelmed with.
|
| IMO, a lot of the 'productivity' isn't actually, it's just
| semi coherent noise.
| silon42 wrote:
| > Writing emails - once I knew what I wanted to convey,
| the rest was so trivial as to not matter, and any LLM
| tooling just got in the way of actually expressing it as
| I ended up trying to tweak the junk it was producing.
|
| +1 LLM will help you produce the "filler" nobody wants
| the read anyway.
| DennisP wrote:
| That's ok, the recipient can use an LLM to summarize it.
|
| In the end, we'll all read and write tight little bullet
| points, with the LLM text on the wire functioning as the
| world's least efficient communication protocol.
| denvrede wrote:
| +1 for all of the above.
|
| > Meeting minutes - I have yet to see one that didn't
| miss something important while creating a lot of junk
| that no one ever read.
|
| Especially that one. In the beginning for very structured
| meetings with a low number of participants it seemed to
| be ok but once they got more crowded, maybe not all are
| native speakers and took longer than 30 minutes (like
| workshops) it went bad.
| cornholio wrote:
| For now, the reasoning abilities of the best and largest
| models are somewhat on par with those of a human crackpot
| with an internet connection, that misunderstands some wild
| fact or theory and starts to speculate dumb and ridiculous
| "discoveries". So the real world application to scientific
| thought is low, because science does not lack imbeciles.
|
| But of course, models always improve and they never grow
| tired (if enough VC money is available), and even an idiot
| can stumble upon low hanging fruits overlooked by the
| brightest minds. This tireless ability to do systematic or
| brute-force reasoning about non-frontier subjects is bound
| to produce some useful results like those you mention.
|
| The comparison with a pure financial swindle and
| speculative mania like NFTs is of course an exaggeration.
| Xmd5a wrote:
| I see myself in these words:
|
| >that misunderstands some wild fact or theory and starts
| to speculate dumb and ridiculous "discoveries"
|
| >even an idiot can stumble upon low hanging fruits
| overlooked by the brightest minds.
| datadrivenangel wrote:
| I want an idiot to stumble on the low hanging fruits of
| my meeting minutes.
| apwell23 wrote:
| > wrote my meetings minutes
|
| why is this such a posterchild for llms. everyone _always_
| leads with this.
|
| how boring are these meetings and do ppl actually review
| these notes? i never ever saw anyone reading meeting
| minutes or even mention them.
|
| Why is this usecase even mentioned in LLM ads.
| batty_alex wrote:
| I think the same thing every time. I've never had anyone
| read my meeting notes and they're better off in some sort
| of work order system anyways.
|
| All I'm hearing is an appeal to making the workplace more
| isolating. Don't talk to each other, just talk to the
| machine that might summarize it wrong
| SiempreViernes wrote:
| Indeed, it seems doubtful that an org having so
| structurless meetings that they are struggling to write
| minutes is capable of having meetings for which minutes
| serves any purpose beyond covering ass.
| squidbeak wrote:
| I think imagination may be the reason for this. Enthusiasts
| have kept that first wave of amazement at what AI is able
| to do, and find it easier to anticipate where this could
| lead. The pessimists on the other hand weren't impressed
| with its capabilities in the first place - or were, and
| then became disillusioned for something it couldn't do for
| them. It's naturally easier to look ahead from the
| optimistic standpoint.
|
| There's also the other category who are terrified about the
| consequences for their lives and jobs, and who are driven
| in a very human way to rubbish the tech to convince
| themselves it's doomed to failure.
|
| The optimists are right of course. A nascent technology at
| this scale and with this kind of promise, whose development
| is spurring a race between nation states, isn't going to
| fizzle out or plateau, however much its current iterations
| may come short of any particular person's expectations.
| aleph_minus_one wrote:
| > It's naturally easier to look ahead from the optimistic
| standpoint.
|
| It is similarly easy to look ahead from a pessimist
| standpoint (e.g. how will this bubble collapse, and who
| will pay the bill for the hype?). The problem rather is
| that this overhyped optimistic standpoint is much more
| encouraged from society (and of course from the
| marketing).
|
| > There's also the other category who are terrified about
| the consequences for their lives and jobs, and who are
| driven in a very human way to rubbish the tech to
| convince themselves it's doomed to failure.
|
| There is also a third type who are not terrified of AI,
| but of the bad decisions managers (will) make because of
| all this AI craze.
| squidbeak wrote:
| No, I meant it's easier for optimists to look ahead at
| the possibilities inherent in the tech itself, which
| isn't true of pessimists, who - as you show - see instead
| the pattern of failed techs in it, whether that pattern
| matches AI or not.
|
| If you can see the promise, you can see a gap to close
| between current capability and accomplished product. The
| question is then whether there's some barrier in that gap
| to make the accomplished product impossible forever.
| Pessimists tend to have given up on the tech already, so
| to them any talk about closing that gap is idle
| daydreaming or hype.
| mountainriver wrote:
| I don't remember when they wrote half my code in a fraction
| of the time for my high paid SWE job.
|
| I do have a bad memory from all the weed though, so who
| knows
| Flamentono2 wrote:
| We already have breakthroughs. Benchmark results which have
| been unheard of before ML.
|
| Alone language translation got so much better, voice
| syntesis, voice transcription.
|
| All my meetings now are searchable and i can ask 'ai' to
| summarize my meetings in a relative accurate way impossible
| before that.
|
| Alphafold made a breakthrough in protein folding.
|
| Image and Video generation can now do unbelievable things.
|
| Realtime voice communication with computer.
|
| Our internal company search suddenly became usefull.
|
| I have 0 use case for NFT and Crypto. I have tons of use case
| for ML.
| uludag wrote:
| I'm sure there's many people out there who could say that
| they hardly use AI but that crypto has made them lots of
| money.
|
| At the end of the day searching work documents and talking
| with computers is only desirable inasmuch as they are
| economically profitable. Crypto at the end of the day is
| responsible for a lot of people getting wealthy. Was a lot
| of this wealth obtained on sketchy grounds? probably, but
| the same could be said AI (for example, the recent sale of
| windsurf for an obscene amount of money).
| littlestymaar wrote:
| > is only desirable inasmuch as they are economically
| profitable.
|
| The bug difference is that they are profitable because
| they create value, when cryptocurrencies are a zero sum
| game between participants. (It is in fact a negative-sum
| game, since some people are getting paid to make the
| thing work so that others can gamble on the system).
| Flamentono2 wrote:
| Crypto is not making people rich, it is about moving
| money from Person A to Person B.
|
| And sure everyone who got the money from others by
| gambling are biased. Fine with me.
|
| But in comparision to crypto, people around me actually
| use AI/ML (most of them).
| nodar86 wrote:
| Every activity that is making people rich is by definiton
| moving money from Person A to Person B.
| holoduke wrote:
| Crypto is not creating anything. Its a scheme that is
| based on gamble. Person A gets rich. Person B loses
| money. It does not really contribute to anything.
| exe34 wrote:
| You have to understand, real AI will never exist. AI is
| that which a machine can't do yet. Once it can do it, it's
| engineering.
| Yoric wrote:
| That is absolutely correct.
|
| The problem is that the hype assumes that all of this is a
| baseline (or even below the baseline), while there are no
| signs that it can go much further in the near future - and
| in some cases, it's actually cutting-edge research. This
| leads to a pushback that may be disproportionate.
| vanattab wrote:
| Which ai program do you use for live video meeting
| translation?
| Flamentono2 wrote:
| MS Teams, Google Meet (whatever they use, probably
| gemini) and wispher
| parodysbird wrote:
| > Alphafold made a breakthrough in protein folding.
|
| Sort of. Alphafold is a prediction tool, or, alternatively
| framed, a hypothesis generation tool. Then you run an
| experiment to compare.
|
| It doesn't represent a scientific theory, not in the sense
| that humans use them. It does not have anywhere near
| something like the accuracy rate for hypotheses to qualify
| as akin to the typical scientific testing paradigm. It's an
| incredibly powerful and efficient tool in certain contexts
| and used correctly in the discovery phase, but not the
| understanding or confirmation phase.
|
| It's also got the usual pitfalls with differentiable neural
| nets. E.g. you flip one amino acid and it doesn't really
| provide a proper measure of impact.
|
| Ultimately, one major prediction breakthrough is not that
| crazy. If we compare that to e.g. Random Forest and similar
| models, the impact in science is infinitely more with them.
| elcritch wrote:
| We already have a precise and accurate theory for protein
| folding. What we don't have is the computational power to
| do true precise simulations at a scale and speed we'd
| like.
|
| In many aspects a huge tangled barely documented code
| base written by inexperienced grad students of quantum
| shortcuts, err, perturbative methods isn't that much more
| or less intelligible than an AI model learning those same
| methods.
| dekhn wrote:
| What "precise and accurate theory for protein folding"
| exists?
|
| Nobody has been able to demonstrate convincingly that any
| simulation or theory method can reliably predict the
| folding trajectory of anything but the simplest peptides.
| elcritch wrote:
| > What "precise and accurate theory for protein folding"
| exists?
|
| It's called Quantum Mechanics.
|
| > Nobody has been able to demonstrate convincingly that
| any simulation or theory method can reliably predict the
| folding trajectory of anything but the simplest peptides.
|
| No we don't have _simplified models or specialized
| theories_ to reduce the computational complexity enough
| to efficiently solve the QM or even molecular dynamics
| systems needed to predict protein folding for more than
| the simplest peptides.
|
| Granted, it's common to mix up things and say that not
| having a computationally tractable models means we don't
| have precise and accurate theory of PF. Something like
| [0] resulting in an accurate, precise, and _fast_ theory
| of protein folding would be incredibly valuable. This
| however, may not be possible outside specific cases.
| Though I believe AlphaFold indicates otherwise as it
| appears life has evolved various building blocks which
| enable a simpler physics of PF tractable to evolutionary
| processes.
|
| Quantum computing however could change that [1]. If
| practical QM is feasible that is, which it's beginning to
| look more and more likely. Some say QC is already proven
| and just needs scaled up.
|
| 0: https://en.m.wikipedia.org/wiki/Folding_funnel 1:
| https://www.nature.com/articles/s41534-021-00368-4
| dekhn wrote:
| I don't think anybody is 100% certain that doing a full
| quantum simulation of a protein (in a box of water) would
| recapitulate the dynamics of protein folding. It seems
| like a totally reasonable claim, but one that could not
| really be evaluated.
|
| If you have a paper that makes a strong argument around
| this claim, I'd love to see it. BTW- regarding folding
| funnels, I learned protein folding from Ken Dill as a
| grad student in biophysics at UCSF, and used to run MD
| simulations of nucleic acids and proteins. I don't think
| anybody in the field wants to waste the time worrying
| about running full quantum simulations of protein
| folding, it would be prohibitevly expensive even with far
| better QM simulators than we have now (IE, n squared or
| better).
|
| Also the article you linked- they are trying to find the
| optimal structure (called fold by some in the field).
| That's not protein folding- it's ground state de novo
| structure prediction. Protein folding is the process by
| which an unfolded protein adopts the structured state,
| and most proteins don't actually adopt some single static
| structure but tend to interconvert between several
| different substructes that are all kinetically
| accessible.
| mountainb wrote:
| One one hand, maybe it's good to have better searchable
| records, even if it's hard to quantify the benefit.
|
| On the other hand, now all your meetings produce reams of
| computer searchable records subject to discovery in civil
| and criminal litigation, possibly leading to far worse
| liability than would have been possible in a mostly email
| based business.
| DennisP wrote:
| Maybe don't do crimes?
|
| If the technology provides a major boost in productivity
| to ethical teams, and is useless for unethical teams,
| that kinda seems like a good thing.
| Voloskaya wrote:
| > to anyone that understands the tech it can't.
|
| This is a ridiculous take that makes me think you might not
| << understand the tech >> as much as you think you do.
|
| Is AI useful today ? That depends on the exact use case but
| overall it seems pretty clear the hype is greater than the
| use currently. But sometimes I feel like everyone forgets
| that ChatGPT isn't even 3 years old, 6 years ago we were
| stuck with GPT-2 whose most impressive feat was writing a non
| sense poem about a unicorn, and AlphaGo is not even 10 years
| old.
|
| If you can't see the trend and just think that what we have
| today is the best we will ever achieve, thus the tech can't
| do anything useful, you are getting blinded by contrarianism.
| vrighter wrote:
| If there is a single objective right answer, the model
| should output a probability of 1 for it, and 0 for
| everything else. Ex. If I ask "Is a sphere a curved
| object?" The one and only answer is "100% yes" not "I am
| 99% sure it is" (and once in a while actually say it isn't)
|
| This is pretty much impossible to achieve with current
| architectures (which aren't all that different to those of
| old, just bigger). If they did, then they'd be woefully
| overfitted. They can't be made reliable. Anyone who
| understands the tech _does_ know this.
| Voloskaya wrote:
| > Anyone who understands the tech does know this
|
| Yes, and this is does not mean the technology can never
| be useful.
|
| I work everyday with that have false beliefs about a
| tech, I have a friend that until recently thought there
| were rivers on the moon, and some believe climate change
| is a hoax, I often forget things people told me and they
| have to tell me again.
|
| Are humans not useful at anything ?
| littlestymaar wrote:
| > It's why it keeps looking exactly like NFT's and crypto
| hype cycles to me: Yes the technology has legitimate uses
|
| AI have legitimate uses, cryptocurrency only has "regulations
| evasion" and NFT has literally no use at all, though.
|
| But that's very true that the AI ecosystem is crowded with
| grifters who feed on baseless hype, and many of them actually
| come from cryptocurrencies.
| jstummbillig wrote:
| I think people are mostly bad at value judgements, and AI is
| no exception.
|
| What they naively wished the future was like: Flying cars.
| What they actually got (and is way more useful but a lot less
| flashy): Cheap solar energy.
| aleph_minus_one wrote:
| > What they naively wished the future was like: Flying
| cars.
|
| This future is already there:
|
| We have flying cars: they are called "helicopters" (see
| also https://xkcd.com/1623/).
| AstralStorm wrote:
| Oh they're not even close in availability as cars, much
| harder to operate, much more expensive and tend to fall
| out of the sky.
|
| Thank you for providing an example that directly maps to
| usefulness of ANN in most research though.
| AngryData wrote:
| Helicopters don't just fall out of the sky, atleast not
| any different than planes fall out of the sky, they can
| auto-rotate to the ground without engine power.
| agurk wrote:
| You are absolutely correct about autorotation and
| helicopters not falling out of the sky. There is one
| nuance that the rotor blades still need to be able to
| rotate for this, and a failed gearbox can prevent that.
| Anecdotally that feels like the most common cause when I
| read about another crash in the North Sea.
| vunderba wrote:
| True but even lowering collective and letting
| autorotation take over you still hit the ground _REALLY_
| hard - enough to sustain injuries to your back and neck
| in some cases.
|
| Glide ratios in GA (like highwing cessnas) is much more
| forgiving assuming you can find a place to put down.
| helloplanets wrote:
| I'd be interested in reading some more from the people you're
| referring to when talking about experts who understand the
| field. At least to the extent I've followed the discussion,
| even the top experts are all over the place when it comes to
| the future of AI.
|
| As a counterpoint: Geoffrey Hinton. You could say he's gone
| off the deep end on a tangent, but I definitely don't his
| incentive is to make money off of hype. Then there's Yann
| LeCun saying AI "could actually save humanity from
| extinction". [0]
|
| If these guys just out of touch talking heads, who are the
| new guard people should read up on?
|
| [0]: https://www.theguardian.com/technology/2024/dec/27/godfa
| ther...
| ccppurcell wrote:
| LLMs have blown crypto and nfts off the map, at least for
| normies like me. I wonder what will blow LLMs off the map?
| mountainriver wrote:
| Code assistants alone prove this to be false.
| edanm wrote:
| > the promises of groundbreaking use cases that will change
| the world are obviously not materialising and to anyone that
| understands the tech it can't.
|
| In what world is this _obviously_ not materializing? Plenty
| of people use GenAI for coding, with some claiming we 're
| approaching the level of GenAI being able to automate vast
| portions of a developer's job.
|
| Do you think this is wrong and the people saying this (some
| of them very experienced developers) are simply mistaken or
| lying?
|
| Or do you think it's not a big deal?
| eschaton wrote:
| If they didn't say that the rah-rah-AI crowd would come for
| them with torches and pitchforks. It's a ward against that,
| nothing more.
| Sharlin wrote:
| Similar to the way many Trump supporters, when daring to
| criticize him, feel the need to assert that they still love
| him and would vote for him again.
|
| (See, eg. r/LeopardsAteMyFace for examples. It's
| fascinating.)
| NotCamelCase wrote:
| Or any time one dares to criticize Israel for their recent
| contributions to peace on Earth (wink wink) -- it has to be
| prefaced with "Let me say that I'm the biggest defender of
| the Jews and fight against anti-Semitism".
|
| It's moot.
| apples_oranges wrote:
| "AI is a competent specialist in all fields except in mine."
| perlgeek wrote:
| > Where are the AI-driven breakthroughs?
|
| The only thing that seems to live up to the hype is AlphaFold,
| which predicts protein folding based on amino acid sequences,
| and of which people say that it actually makes their work
| significantly easier.
|
| But, disclaimer, this is only from second-hand knowledge, I'm
| not working in the field.
| rafaelmn wrote:
| This is another dimension of the problem - what's even
| considered AI ? AlphaFold is a very specialized model - and I
| feel the AI boom is driven by hypothesis that general models
| eventually outperform specialized ones given enough
| size/data/whatever.
| _0ffh wrote:
| While I hate the apparent renaming of everything ML to
| "AI", things like AlphaFold would be "narrow AI".
|
| As to the common idea of having to wait for general AI
| (AGI) to bring the gains, I have been quite sure since the
| start of the recent AI hype cycle that narrow AI will have
| silently transformed much of the world before AGI even hits
| the town.
| perlgeek wrote:
| In my head, I just substitute "AI" with "machine learning"
| or "statistics".
|
| > and I feel the AI boom is driven by hypothesis that
| general models eventually outperform specialized ones given
| enough size/data/whatever.
|
| I think in the sciences, I'd generally put my money on the
| specialized models.
|
| I hope that the hype around AI makes it easier (by
| providing tooling, platforms, better algorithms,
| educational materials etc.) to train specialized models.
|
| Kind of a trickle-down of hype money :-)
| rafaelmn wrote:
| I expect the opposite - hardware/compute going to be
| locked up in AGI quests unless the bubble pops and then
| it gets discounted
| yfontana wrote:
| From the article:
|
| > Besides protein folding, the canonical example of a
| scientific breakthrough from AI, a few examples of scientific
| progress from AI include:1
|
| > Weather forecasting, where AI forecasts have had up to 20%
| higher accuracy (though still lower resolution) compared to
| traditional physics-based forecasts.
|
| > Drug discovery, where preliminary data suggests that AI-
| discovered drugs have been more successful in Phase I (but not
| Phase II) clinical trials. If the trend holds, this would imply
| a nearly twofold increase in end-to-end drug approval rates.
| strogonoff wrote:
| There is rarely a constructive discussion around the term "AI".
| You can't say anything useful about what it might lead to or
| how useful it might be, because it is purely a marketing term
| that does not have a specific meaning (neither do both of the
| words in its abbreviation).
|
| Interesting discussions tend to avoid "AI" in favour of
| specific terms such as "ML", "LLM", "GAN", "stable diffusion",
| "chatbot", "image generation". These terms refer to specific
| tech and applications of that tech, and allow to argue about
| specific consequences for sciences or society (use of ML in
| biotech vs. proliferation of chatbots).
|
| However, certain sub-industries prefer "AI" precisely because
| it's so vague, offers seemingly unlimited potential (please
| give us more investment money/stonks go up), and creates a
| certain vibe of a conscious being useful when pretending not to
| be working around IP laws and creating tools based on data
| obtained without relevant licensing agreements (cf. the
| countless "if humans have the freedom to read, therefore it's
| unfair to restrict the uses of a software tool" fallacies,
| often perpetuated even by seemingly technically literate
| people, in pretty much every relevant forum thread).
| mnky9800n wrote:
| This article is all about PINNs being overblown. I think it's
| a reasonable take. I've seen way too many people dump all
| their eggs in the PINNs basket when there are plenty of
| options out there. Those options just don't include a ticket
| to the hype train.
| Closi wrote:
| I think AI is a useful term which usually means a neural
| network architecture but without specifying the exact
| architecture.
|
| I think Machine Learning doesn't mean this as a word, as it
| can also refer to linear regression, non-linear optimisation,
| decision trees, bayesian networks etc.
|
| That's not saying that AI isn't abused as a term - but I do
| think a more general term to describe the latest 5 years
| advancements in neural networks to solve problems is useful.
| Particularly as it's not obvious which model architectures
| would apply to which fields without more work (or even if
| novel architectures will be required for frontier science
| applications).
| GrantMoyer wrote:
| The field of neural network research is known as Deep
| Learning.
| danielbln wrote:
| Eh, not really. All Deep Learning involves neural
| networks, but not all neural networks are part of deep
| learning. To be fair, any modern network is also
| effectively built by deep learning, but your statement as
| such is inaccurate.
| daveguy wrote:
| This is incorrect. Machine Learning is a term that refers
| to numerical as opposed to symbolic AI. ML is a subset of
| AI as is Symbolic / Logic / Rule based AI (think expert
| systems). These are all well established terms in the
| field. Neural Networks include deep learning and LLMs. Most
| AI has gone the way of ML lately because of the massive
| numerical processing capabilities available to those
| techniques.
|
| AI is not remotely limited to Neural Networks.
| roenxi wrote:
| Also, the strong predictions about AI are using a vague term
| because the tech often doesn't exist yet. There isn't a
| chatbot right now that I feel confident can out-perform me at
| systems design but I'm pretty certain something that can is
| coming. Odds are also good that in 2-4 years there will be
| new hotness to replace LLMs that are much more functional
| (maybe MLLMs, maybe called something else). We can start to
| predict and respond to their potential even though they don't
| exist yet; it just takes a little extrapolating. But it
| doesn't have a name yet.
|
| Which is to agree - obviously if people are talking about
| "AI" they don't want to talk about something that exists
| right this second. If they did it'd be better to use a
| precise word.
| Closi wrote:
| Totally agree.
|
| Also the term 'LLM' is more about the mechanics of the
| thing than what the user gets. LLM is the technology, but
| some sort of automated artificial intelligence is what
| people are generally buying.
|
| As an example, when people use ChatGPT and get an image
| back, most don't think "oh, so the LLM called out to a
| diffusion API?" - they just think "oh chat GPT can give me
| an image if I give it a prompt".
|
| Although again, the term is entirely abused to the extent
| that washing machines can contain 'AI'. Although just
| because a term is abused doesn't necessarily mean it's not
| useful - everything had "Cloud" in it 10 years ago but that
| term was still useful enough to stick around.
|
| Perhaps there is an issue that AI can mean lots of things,
| but I don't know _yet_ of another term that encapsulates
| the last 5 years advancements in automated intelligence,
| and what that technology is likely to be moving forwards,
| which people will readily recognise. Perhaps we need a new
| word, but AI has stuck and there isn 't a good alternative
| yet, so is probably here to stay for a bit!
| giantrobot wrote:
| > Although again, the term is entirely abused to the
| extent that washing machines can contain 'AI'.
|
| I remember when the exciting term in appliances was
| "fuzzy logic". As a technology it was just adding some
| sensors beyond simple timers and thermostats to control
| things like run time and temperatures of automated
| washers.
| rickdeckard wrote:
| It's not even that certain sub-industries prefer "AI", it's
| the umbrella term a company can use in Marketing for
| virtually any automated process that provides a seemingly
| subjective result.
|
| Case in point:
|
| For a decade the implementation of cameras went through
| development, testing and tuning of Auto Exposure, Auto Focus
| and Auto White-Balance ("AAA") engines as well as image post-
| processing.
|
| These engines ran on a Image Signal Processor (ISP) or
| sometimes on the Camera sensor itself, extensive work was
| done by Engineering Teams on building these models in order
| to optimize them to run on low-latency on an ISP.
|
| Suddenly AI came along and all of these features became "AI
| features". One company started with "AI assisted Camera" to
| promote the process everyone was doing all-along. So all had
| to introduce AI, without any disruptive change in the
| process.
| fluidcruft wrote:
| I agree it's completely meaningless. At this point I think
| marketing would label a toilet fill valve as "AI".
| sebastiennight wrote:
| Well the "smart toilet" is definitely a thing you can buy
| today:
|
| > The integration of Artificial Intelligence (AI) and the
| Internet of Things (IoT) in bathroom fixtures,
| particularly toilets, is shaping the future of hygiene,
| convenience, and sustainability.
| gowld wrote:
| SmartPipe, 2015:
| https://www.youtube.com/watch?v=DJklHwoYgBQ
| elcritch wrote:
| While automated AI measurement of the chemical makeup of
| .. human effluent could be helpful for tracking health
| trends, I fear it'd also come with built in integrations
| for Instagram and TikTok.
| GuinansEyebrows wrote:
| or (and i can actually see this happening) an Amazon
| integration to reorder bathroom tissue and bowl cleaner
| Aldipower wrote:
| I remember somethings similar when the term "cloud" came
| up. It is still someone else's server or datacenter with
| tooling.
| johnisgood wrote:
| Yeah, can they just stop coining terms to refer to old,
| pre-existing things? I still hate the term "cloud".
| idontwantthis wrote:
| My favorite description is "The cloud is a computer you
| don't own in Reston, VA"
| giantrobot wrote:
| > One company started with "AI assisted Camera" to promote
| the process everyone was doing all-along.
|
| Before the "AI" labeling more advanced image processing was
| often called "computational photography". At least in the
| world of smartphone cameras. Because they have tiny image
| sensors and lenses smartphone cameras need to do a _lot_ of
| work to get a decent image out of any environment that
| doesn 't have perfect lighting. The processing is more
| traditional computer vision.
|
| There's not legitimate generative AI features being peddled
| like editing people out of (or into) photos. But most of
| the image processing pipelines haven't fundamentally
| changed but not have AI labeling to please marketers and
| upper management.
| jgalt212 wrote:
| > There is rarely a constructive discussion around the term
| "AI".
|
| You hit the nail on the head there. AI, in its broadest
| terms, exists at the epicenter of hype and emotions.
| fabian2k wrote:
| I suspect that people saying this are avoiding to make broad
| conclusions based only on the AI tools that exist right now at
| this moment. So they leave a lot of room for the next versions
| to improve.
|
| Maybe too much room, but it's hard to predict if AI tools will
| overcome their limitations in the near future.
| swyx wrote:
| > Where are the AI-driven breakthroughs? Or even the AI-driven
| incremental improvements?
|
| literally last week
|
| https://deepmind.google/discover/blog/alphaevolve-a-gemini-p...
| dwroberts wrote:
| But it only seems to be labs and companies that also have a
| vested interest in selling it as a product that are able to
| achieve these breakthroughs. Which is a little suspect,
| right?
| swyx wrote:
| too tinfoil hat. google is perfectly happy to spend
| billions dogfooding their own TPUs and not give the leading
| edge to the public.
| dwroberts wrote:
| I'm not saying they're phoney - just we need to take this
| stuff with a big pinch of salt.
|
| The Microsoft paper around the quantum " breakthrough "
| is in a different field, but maybe a good example of why
| we need to be a little more cautious of research-as-
| marketing
| boxed wrote:
| Yea, except that we see "breakthrough" stuff like this _all
| the time_ , and it almost always is quickly found out that
| it's fraudulent in some way. How many times are we to be
| fooled before we catch on and don't believe press releases
| with massive selection bias?
| Wilsoniumite wrote:
| Some new ish maths has been discovered. It's up to you if this
| is valid or impressive enough, but I think it's significant for
| things to come:
| https://youtu.be/sGCmu7YKgPA?si=EG9i0xGHhDu1Tb0O
| snodnipper wrote:
| Personally, I have been very pleased with the results despite
| the limitations.
|
| Like many (I suspect), I have had several users provide
| comments that the AI processes I have defined have made
| meaningful impacts on their daily lives - often saving them
| double digit hours of effort per week. Progress.
| shusaku wrote:
| > Or even the AI-driven incremental improvements?
|
| You have no idea what you are talking about. Every day there is
| plenty of research published that used AI to help achieve
| scientific goals.
|
| Now LLMs are another matter, and probably a ways off before we
| reap the benefit beyond day to day programming / writing
| productivity.
| montebicyclelo wrote:
| > then go on to explain how they aren't useful in any field
| whatsoever
|
| > Where are the AI-driven breakthroughs
|
| > are we just using AI to remix existing general knowledge,
| while making no progress of any sort in any field using it?
|
| The obvious example of a highly significant AI-driven
| breakthrough is Alphafold [1]. It has already had a large
| impact on biotech, helping with drug discovery, computational
| biology, protein engineering...
|
| [1] https://blog.google/technology/ai/google-deepmind-
| isomorphic...
| boxed wrote:
| I'm personally waiting for the other shoe to drop here. I
| suspect that, since nature begins with an existing protein
| and modifies it slightly, AlphaFold is crazy overfitted to
| the training data. Furthermore, the enormous success of
| AlphaFold means that the number of people doing protein
| structure solving has likely crashed.
|
| So not only are we using an overfitting model that probably
| can't handle truly novel proteins, we have stopped actually
| doing the research to notice when this happens. Pretty bad.
| pixl97 wrote:
| Why do you expect this, or is this just a "I need to find a
| reason to hate AI" thing?
| flobosg wrote:
| > that probably can't handle truly novel proteins
|
| AlphaFold is able to predict novel folds, see
| https://www.nature.com/articles/s42003-022-03357-1
| isaacfrond wrote:
| The article itself lists as successful, even breakthrough,
| applications of AI: protein folding, weather forecasting, and
| drug discovery.
| rsynnott wrote:
| They do mention that it has been somewhat useful in protein
| folding.
|
| > Or are we just using AI to remix existing general knowledge,
| while making no progress of any sort in any field using it?
|
| AIUI they are generally not talking about LLMs here.
| phpnode wrote:
| it's the new "I love my tesla but here are 15 reasons why it's
| broken" - if you don't provide platitudes the mob provides
| pitchforks
| nerdponx wrote:
| New numerical computing algorithms are being developed with AI
| assistance, which probably would not have been discovered
| otherwise. There was an article here a few days ago about one
| of those. It's incremental but it's not nothing.
| PurpleRamen wrote:
| > Where are the AI-driven breakthroughs?
|
| Define breakthrough. When is the improvement big enough to
| count as one?
|
| Define AI. Are you talking about modern LLM, or is old school
| ML also in that question?
|
| I mean Googles AI-company had with AlphaFold and other project
| quite the impact.
|
| > Or are we just using AI to remix existing general knowledge
|
| Is remixing bad? Isn't many science today "just" remixing with
| slight improvements? I mean, there is a reason why we have
| theoretical and practical scientists. Doing boring Lab-work and
| accidentally discovering something exciting is not the only way
| science is happening. Analysing data and remixing information,
| building new theories, is also important.
|
| And don't forget, we don't have AGI yet. Whatever AI is doing
| today, is limited by what humans are using it for. Another
| question is, whether LLM is not normalized enough already that
| we do not see it as very special any more, if it's used
| somewhere. So we might not even see it if AI has significant
| impact on any breakthrough.
| biophysboy wrote:
| An example of an "AI" incremental improvement would be Oxford
| Nanopore sequencing. They extrude DNA through a nanopore,
| measure the current, and decode the bases using recurrent
| neural networks.
|
| They exist all over science, but they are just one method among
| many, and they do not really drive hypotheses or
| interpretations (even now)
| currymj wrote:
| AlphaFold is real.
|
| To the extent you care about chess and Go as human activities,
| progress there is real.
|
| there are some other scientific computing problems where AI or
| neural-network-based methods do appear to be at least part of
| the actual state-of-the-art (weather forecasting, certain
| single-molecule quantum chemistry simulations).
|
| i would like the hype of the kind described in the article to
| be punctured, but this is hard to do if critics make strong
| absolute claims ("aren't useful in any field whatsoever") which
| are easily disproven. it hurts credibility.
| literalAardvark wrote:
| Even more relvant, AlphaEvolve is real.
|
| Could easily be brick 1 of self-improvement and the start of
| the banana zone.
| gthompson512 wrote:
| > "the start of the banana zone"
|
| What does this mean? Is it some slang for exponential
| growth, or is it a reference to something like the
| "paperclip maximizer"?
| literalAardvark wrote:
| It's slang for the J part of the exponential curve.
| Didn't expect that to be a problem here, sorry.
| dirtyhippiefree wrote:
| I'm with gthompson512
|
| Sounds like a routine Bill Hicks might have come up with if
| he was still with us.
|
| He hated obfuscation.
| daveguy wrote:
| I've never seen an AI critic say AI isn't "useful in any
| field whatsoever". Especially one that is known as an expert
| in and a critic of the field. There may be names that aren't
| coming to mind because that stance would reduce their
| specific credibility. Do you have some in mind?
| currymj wrote:
| the post that I replied to?
| daveguy wrote:
| The post you replied to was asking for examples based on
| the general critical discussions they have seen.
|
| And no offense to the GP, but they clearly aren't an
| expert in the field or they wouldn't be asking.
|
| Probably should have replied directly to the post you
| replied to as much as yours. Was just pointing out that
| "not useful in any field whatsoever" is not something
| I've seen from anyone in the field. Even the article
| doesn't say that.
| ergsef wrote:
| Speaking out against the hype is frowned upon. I'm sure even
| this very measured article about "I tried it and it didn't work
| for me" will draw negative attention from people who think AI
| is the Second Coming.
|
| It's also very hard to prove a negative. If you predict "AI
| will never do anything of value" people will point to literally
| any result to prove you wrong. TFA does a good job debunking
| some recent hype, but the author cannot possibly wade through
| every hyperbolic paper in every field to demonstrate the claims
| are overblown.
| dfxm12 wrote:
| There's hype, then there's the understanding that you never use
| version 1.0 of a product. In this sense, AI as we know is
| barely in an alpha version. I think the authors you're
| referring to understand the marketing around AI overstates its
| usefulness, but they are hopeful for a future where AI can be
| helpful.
| chermi wrote:
| Protein structure prediction is a pretty useful tool. There are
| various 'foundation' models in biology now that are quite
| useful. I don't know if you want to count those as AI or ML.
|
| If you're looking for breakthroughs due to AI, they're not
| going to be obviously attributable to AI I think. Focusing on
| the biology related foundation models ... The ability to more
| quickly search through the space of sequences->structure,
| drugs, and predicted cell states (1) like with the biology
| based foundation models will certainly lead to some things
| being discovered/rejected/validated faster.
|
| I heard about this vevo company recently so it's on my mind.
| Biology experiments are hard and time consuming, and often hard
| to exactly replicate across labs. A lot of the data is of the
| form
|
| a) start in cell state X (usual 'healthy' normal state) under
| conditions Y (environment variables like temperature,
| concentrations, etc.)
|
| b) introduce some environmental pertubation P like some
| drug/chemical at some concentration, or maybe multiple
| pertubations at once
|
| c) record trajectory or steady/final state of cell.
|
| This data is largely hidden within published papers in non-
| standardized format. Vevo is attempting to collate all of those
| results with considerations for reproducibility into a standard
| easy-to-use format. The idea being that you can gradually build
| up a virtual sort of input-output (causal!) model that you can
| throw ideas for interventions against and see what it thinks
| would happen. Cells and biology are obviously enormously
| complicated so it's certainly not going to be 100%
| accurate/predictive, but my experience with network models in
| quantitative biology plus their proclaimed results make me
| pretty confident it's a sound approach.
|
| Thus approach is clearly "AI" driven (maybe I would call this
| ML) and if their claims are anything close to reality, this is
| an incredibly powerful tool for all of academia and industry.
| You can reduce the search space enormously and target your
| experiments to cover the areas the virtual model doesn't seem
| so good, continuously improving it in a sort of crowd-sourced
| "active learning" manner. A continuously improving
| experimentally backed causal (w.r.t. perturbations) model of
| cells has so many applications. Again, i don't think this will
| directly lead to a breakthrough, but it can certainly make the
| breakthrough more likely and come faster.
|
| There are many other examples like this that are some
| combination of 1) collating+filtering+refining existing data
| into an accessible easily query-able format
|
| 2) combining data + some physically motivated modeling to yield
| predictions where there is no data
|
| 3) targeted, informed feedback loop via experiments,
| simulations, or modeling to improve the whole system where it's
| known to be weak or where more accuracy is desired.
|
| Assuming it all stays relatively open, that's undeniably a very
| powerful model for more effective science.
|
| And that's just one approach. In physics ML can be use for
| finding and characterizing phase transitions, as one example.
| In the world of soft matter/biophysics simulation here's a few
| ways ML is used:
|
| a) more efficient generation of configurations (Noe generative
| models). This is a big one, albeit still kind of early stages.
| Historically(simplifying), to generate independent samples in
| the right regions of phase means you need to integrate the
| system for long enough to hit that region multiple times. So
| regions of the space separated by rare transitions will take a
| loooong time to hit multiple times. The solution was simply
| longer simulations. Now, under some restrictions, you can
| leverage and augment existing data (including simulation data)
| to directly generate independent samples in the regions of
| interest. This is a really big deal.
|
| b) more efficient, complex and accurate NN force-fields. Better
| incorporation of many-body and even quantum effects.
|
| c) more complex simulation approaches via improved pipelines
| like automated parameterization and discovery of collective
| variables to more efficiently explore relevant configuration
| space.
|
| Again, this is tooling that improves the process of discovery &
| investigation and thus directly contributes to science. Maybe
| not in the way you're picturing, but it is happening right now.
|
| 1) vevo https://www.tahoebio.ai/
| nyarlathotep_ wrote:
| > say things like "of course I don't doubt that AI will lead to
| major discoveries", and then go on to explain how they aren't
| useful in any field whatsoever?
|
| This is "paying the toll", otherwise one will be accused of
| being a "luddite."
| Konnstann wrote:
| Depending on your definition of AI, a pipeline for drug
| repurposing my team used was able to identify a therapeutic for
| a rare disease that was beating the state of the art in every
| test they threw at it, eventually being given orphan drug
| designation by the FDA. I doubt this would have happened
| without machine learning or AI or whatever you want to call it.
|
| I'm also against "agents as scientists" as a concept for
| numerous reasons, but deep learning etc has or is leading to
| breakthroughs.
| reify wrote:
| Extremely diplomatic
|
| Most scientists aren't trying to mislead anyone, but because they
| face strong incentives to present favorable results, there's
| still a risk that you'll be misled.
|
| In other words scientists are trying to mislead everyone because
| there are a lot of incentives; money and professional status to
| name just two.
|
| A common problem across all disciplines of science.
| nathias wrote:
| people will continue to publish cope articles about how AI is
| useless, far after superintelligence will be reached
| vaylian wrote:
| citation needed
| tonii141 wrote:
| This article addresses the misconception that arises when someone
| lacks a clear understanding of the underlying mathematics of
| neural networks and mistakenly believes they are a magical
| solution capable of solving every problem. While neural networks
| are powerful tools, using them effectively requires knowledge and
| experience to determine when they are appropriate and when
| alternative approaches are better suited.
| constantcrying wrote:
| This does not apply to PINNs though. They were used and
| investigated by people deeply knowledgeable about numerics and
| neural networks, they just totally failed to live up to
| expectation.
| tonii141 wrote:
| "they just totally failed to live up to expectation"
|
| Because the expectation was too high. If you are aiming for
| precision, neural networks might not be the best solution for
| you. That is why generative AI works so well, it doesn't need
| to be extremely precise. On the other hand you don't see
| people use neural networks in system control for cricital
| processes.
| sgt101 wrote:
| Apart from self driving and autonomous flying...
| tonii141 wrote:
| AI is used in scene understanding for those applications,
| but there is no neural network that is steering the
| wheel.
| sgt101 wrote:
| I think that while the mathematics of neural networks are
| clearly completely understood we do not really understand why
| neural networks behave the way that they do when combined with
| large amounts of real world data.
|
| In particular the ability of auto regressive transformer based
| networks to produce sequences speech while being immutable
| still shocks me whenever I think about it. Of course, this says
| as much about what we think of ourselves and other humans as it
| does about the matrices. I also think that the weather
| forcasting networks are quite shocking, the compression that
| they have achieved in modeling the physical system that
| produces weather is frankly.... wrong... but it obviously does
| actually work.
| skydhash wrote:
| You can represent many things with numbers and build an
| algorithm that does stuff. ML techniques are formulas where
| some specifics constants are not known yet, so you go through
| a training phase to find them.
|
| While combinations of words are infinite, only some makes
| sense. So there's a lot of reccurent patterns there. When you
| take a huge datasets like most of the internet and digital
| documents. I would be more surprised if the trained model
| where incapable of producing correct texts as both the it's
| overfitted to the grammar and the lexicon. And I believe it's
| overfitted to general conversation patterns.
| sgt101 wrote:
| There is a lot of retrieval in the behaviours of LLM's, but
| I find it hard to characterize it as overfitted. For
| example, ask ChatGPT to respond to your questions with
| grammatically incorrect answers.
| shalmanese wrote:
| This is less an article about AI and more about, one of the less
| talked about functions of a PhD program is becoming literate at
| "reading" academic claims beyond their face value.
|
| None of the claims made in the article are surprising because
| they're the natural outgrowth of the hodgepodge of incentives
| we've accreted as what we call "science" over time and you just
| need to practice over time to be able to place the output of
| science in the proper context and understand that a "paper" is an
| artifact of a sociotechnical system with all the entailing
| complexity that demands.
| Flamentono2 wrote:
| I'm not sure why people on HN (of all places) are so divided
| regarding the perception of AI/ML.
|
| I have not seen anything like it before. We literaly had not
| system or way of even doing things like code generation based on
| text input.
|
| Just last week i asked for a script to do image segmentation with
| a basic UI and claude just generated that for me in under 1
| Minute.
|
| I could list tons of examples which are groundbreaking. The whole
| Image generation stack is completly new.
|
| That blog article is fair enough, there is hype around this topic
| for sure, but alone for every researcher who needs to write code
| for their research, AI can make them already a lot more
| efficient.
|
| But i do believe, that we have entered a new ara: An ara were we
| take data again very serious. A few years back, you said 'the
| internet doesn't forget' then we realized that yes the internet
| starts to forget. Google deleted pages, removed the cache feature
| and it felt like we stoped caring for data because we didn't knew
| what to do with it.
|
| Then ai came along. And not only is now data king again but we
| are now in the mids of reinforcment ara: We now give feedback and
| the systems incorporate that feedback into their
| training/learning.
|
| And the ai/ml topic is getting worked on on every single aspect
| of it: Hardware, Algorithm, use cases, data, tools, protocols,
| etc. We are in the middle of incorporating and building for and
| on it. This takes a little bit of time. Still the progress is
| crazy exhausting.
|
| We will only see in a few years if there is a real ceiling. We do
| need more GPUs, bigger Datacenters to do a lot more experiments
| on AI architecture and algorithm. We have a clear bottleneck. Big
| companies train one big model for weeks and month.
| KurSix wrote:
| But on the flip side, the "AI will revolutionize science"
| narrative feels way ahead of what the evidence supports
| callc wrote:
| > "I'm not sure why people on HN (of all places) are so divided
| regarding the perception of AI/ML."
|
| Everyone is a rational actor from their individual perspective.
| The people hyping AI, and the people dismissing the hype both
| have good reasons.
|
| The is justification to see this new tech as ground breaking.
| There is justification to be weary about massive theft of data
| and dismissiveness of privacy.
|
| First, acknowledge and respect that there are so many opinions
| on any issue. Take yourself out of the equation for a minute.
| Understand the other side. Really understand it.
|
| Take a long walk in other people's shoes.
| Barrin92 wrote:
| >but alone for every researcher who needs to write code for
| their research, AI can make them already a lot more efficient.
|
| scientists don't need to be efficient, they need to be correct.
| Software bugs were already a huge cause of scientific error,
| and responsible for lack of reproducibility, see for example
| cases like this (https://www.vice.com/en/article/a-code-glitch-
| may-have-cause...)
|
| Programming in research environments is done with some
| notoriously questionably variation in quality, as is the case
| for the industry to be fair, but in research minor errors can
| ruin results of entire studies. People are fed up and come to
| much harsher judgements on AI because in an environment like a
| lab you cannot write software with the attitude of an
| impressionist painter or the AI equivalent, you need to
| actually know what you're typing.
|
| AI can make you more efficient if you don't care if you're
| right, which is maybe cool if you're generating images for your
| summer beach volleyball event, but it's a disastrous idea if
| you're writing code in a scientific environment.
| Flamentono2 wrote:
| I do expect a researcher to verify the way the code interacts
| with the data set.
|
| Still a lot of researchers can benefit from code tools for
| their daily work to make them a lot faster.
|
| And plenty of strategies exist to saveguard this. Tool use
| for example, unit tests etc.
| whyowhy3484939 wrote:
| > Just last week i asked for a script to do image segmentation
| with a basic UI and claude just generated that for me in under
| 1 Minute.
|
| Thing is we just see that it's copy pasting stack overflow, but
| now in a fancy way so this is sounding like "I asked Google for
| a nearby restaurant and it found it in like 500ms, my C64
| couldn't do that". It sounds impressive (and it is) because it
| sounds like "it learned about navigating in the real world and
| it can now solve everything related to that" but what it
| actually solved is "fancy lookup in a GIS database". It's
| useful, damn sure it is, but once the novelty wears off you
| start seeing it for what it is instead of what you imagine it
| is.
|
| Edit: to drive the point home.
|
| > claude just generated that
|
| What you think happened is AI is "thinking" and building a
| ontology over which it reasoned and came to the logical
| conclusion that this script was the right output. What actually
| happened is your input correlates to this output according to
| the trillion examples it saw. There is no ontology. There is no
| reasoning. There is nothing. Of course this is still impressive
| and useful as hell, but the novelty will wear off in time. The
| limitations are obvious by this point.
| Flamentono2 wrote:
| I'm following LLMs, AI/ML for a few years now and not just on
| a high level.
|
| There is not a single system out there today which can do
| what claude can do.
|
| I stil see it for what it is: A technology i can
| communicate/use with natural language and get a very diverse
| of tasks done. From writing/generating code, to svgs, to
| emails, translation etc. etc. etc.
|
| Its a paradigma shift for the whole world literaly.
|
| We finally have a system which encodes not just basic things
| but high level concepts. And we humans are doing often enough
| something very similiar.
|
| And what limitations are obvious? Tell me? We have not
| reached any real ceiling yet. We are limited by GPU capacity
| or how many architectural experiments a researcher can run.
| We have plenty of work to do to cleanup the data set we use
| and have. We need to build more infrastructure, better
| software support etc.
|
| We have not even reached the phase were we all have local
| AI/ML chips build in.
|
| We don't even know yet how a system will act if everyone of
| us has access to very fast inferencing like you already get
| with groq.
| whyowhy3484939 wrote:
| > We finally have a system which encodes not just basic
| things but high level concepts
|
| That's the thing I'm trying to convey: it's in fact not
| encoding anything you'll recognize and if it is, it's
| certainly not "concepts" as you understand them. Not saying
| it cannot correlate text that includes what you call "high
| level concepts" or do what you imagine to be useful work in
| that general direction. Again not making claims it's not
| useful, just saying that it becomes kind of meh once you
| factor in _all_ costs and not just the hypothetical
| imaginary future productivity gains. AKA building literal
| nuclear reactors to do something that basically amounts to
| filling in React templates or whatever BS needs doing.
|
| If it was reasoning it could start with a small set of
| bootstrap data and infer/deduce the rest from experience.
| It cannot. We are not even close as in there is not even
| _theory_ to get us there forget about the engineering. It
| 's not a subtle issue. We need to throw literally all data
| we have at it to get it to acceptable levels. At some point
| you have to retrace some steps and think over some
| decisions, but I guess I'm a skeptic.
|
| In short it's a correlation engine which, again, is very
| useful and will go ways to improve our lives somewhat - I
| hope - but I'm not holding my breath for anything more. A
| lot of correlation does not causation make. No reasoning
| can take place until you establish ontology, causality and
| the whole shebang.
| Flamentono2 wrote:
| I do understand it but i also think that the current LLMs
| are the first step to it.
|
| GPT-3 started proper investment into this topic, there
| was not enough research done in this direction and now it
| is. People like Yann LeCun already analyse different
| approaches/architecture but they still use the
| infrastructure of LLMs (ML/GPUs) and potentially the
| data.
|
| I never said that LLM is the breaktrhough in consesnes.
|
| But you can also ask LLM strategies for thinking. It can
| tell you a lot of things. We will see if a LLM will be a
| fundamental part of AGI or not but GPU/ML will probably
| be.
|
| I also think that the compression mechanism through LLM
| lead to concepts through optimization. You can see from
| the antropic paper, that an LLM doesn't work in normal
| language space but in a high dimensional one and then
| 'expresses' the output in a language you like.
|
| We also see that real multi modal models are better in a
| lot of tasks due to a lot more context available through
| them. Estimating what someone said due to context.
|
| The necessary infrastructure and power requirement is
| something i accept too. We can assume, i do, that further
| progress in a lot of topics will require this type of
| compute and it also solves our data bottleneck: normal
| CPU architecture is limited by memory databus.
|
| Also in comparision to a lot of other companies, if the
| richest companies in the world invest in nuclear, i think
| this is a lot better than any other companies. They have
| a lot higher margins and knowledge. co2 is a market
| separator for them too.
|
| I also expect this amount of compute to be the base for
| fixing real issues we all face like cancer or optimizing
| cancer or any other sickness detection. We need to make
| medicin a lot cheaper and if someone in africa can do a
| cheap x ray and send it to the cloud to get any feedback,
| that would / could help a lot of people.
|
| Doing complex and massive protein analysis or mRna
| research in virtual space, also requires GPUs.
|
| All of this happened in a timespan of only a few years. I
| have not seen anything progressing as fast as AI/ML
| currenly does and as unfortunate it is, this needs
| compute.
|
| Even my small inhouse image recognition fine tuning
| explodes when you do a handful parameter optimizations
| but the quality is a lot better than what we had before.
|
| And enabling people to have real natural language UI is
| HUGE. It makes so much more accessable. Not just for
| people with a disability.
|
| Things like 'do a eli5 on topic x'. "explain to me this
| concept" etc. I would have loved that when i tried to be
| successful in the university math curiculum.
|
| All of that is already crazy and still is. But in
| parallel what Nvidia and others currently do with ML and
| Robotics is also something which requires all of that
| compute. And the progress is again breath taking. The
| current flood of basic robots standing and walking around
| is due to ML.
| th0ma5 wrote:
| I mean, you're not even wrong ! Most all of these large
| models are based on the idea that if you put all of the
| representations that we can of the world into a big pile
| that you can tease out some kind of meaning. There's not
| even really a cohesive theory as to that, and surely no
| testable way to prove that it's true. It certainly seems
| like you can make a system that behaves as if it could be
| like that, and I think that's what you're picking up on.
| But it's actually probably something else and something
| far shorter of that.
| lossolo wrote:
| > Its a paradigma shift for the whole world literaly.
|
| That's hyperbolic. I use LLMs daily. They speed up tasks
| you'd normally use Google for and can extrapolate existing
| code into other languages. They boost productivity for
| professionals, but it's not like the discovery of the steam
| engine or electricity.
|
| > And what limitations are obvious? Tell me? We have not
| reached any real ceiling yet.
|
| Scaling parameters is the most obvious limitation of the
| current LLM architecture (transformers). That's why what
| should have been called GPT-5 is instead named GPT 4.5, it
| isn't significantly better than the previous model despite
| having far more parameters, a lot more cleaned up training
| data and optimizations.
|
| The low-hanging fruit has already been picked, and most
| obvious optimizations have been implemented. As a result,
| almost all leading LLM companies are now operating at a
| similar level. There hasn't been a real breakthrough in
| over two years. And the last huge architectural
| breakthrough was in 2017 (with paper "Attention is all you
| need").
|
| Scaling at this point yields only diminishing returns. So
| no, what you're saying isn't accurate, the ceiling is
| clearly visible now.
| sanderjd wrote:
| I honestly think it's still way too early to say this
| either way. If your hypothesis that there are no
| breakthroughs left is right, then it's still a very big
| deal, but I'd agree with you that it's not steam engine
| level.
|
| But I don't think "the transformer paper was eight years
| ago" is strong evidence for that argument at all. First
| of all, the incremental improvement and commercialization
| and scaling that has happened in that period of time is
| already incredibly fast. Faraday had most of the pieces
| in place for electricity in the 1830s and it took half a
| century to scale it, including periods where the state of
| the art began to stagnate before hitting a new
| breakthrough.
|
| I see no reason to believe it's impossible that we'll see
| further step-change progressions in AI. Indeed,
| "Attention is All You Need" itself makes me think it's
| more likely than not. Out of the infinite space of things
| to try, they found a fairly simple tweak to apply to
| existing techniques, and it happened to work extremely
| well. Certainly a lot more of the solution space has been
| explored now, but there's still a huge space of things
| that haven't been tried yet.
| nancyminusone wrote:
| LLMs are great at tasks that involve written language. If
| your task does not involve written language, they suck.
| That's the main limitation. No matter how hard you push, AI
| is not a 'do everything machine' which is how it's being
| hyped.
| ranie93 wrote:
| Can "everything" be mapped to a written language task
| (i.e. described)?
| dvfjsdhgfv wrote:
| > There is not a single system out there today which can do
| what claude can do.
|
| Of course there is, it's called Gemini 2.5 Pro and it is
| also the reason I cancelled my Claude (and earlier OpenAI)
| subscriptions (I had quite a few of them to go around
| limits).
| skydhash wrote:
| Yeah. It's just fancier techniques than linear regression.
| Just like the latter takes a set of numbers and produces
| another set, LLMs takes words and produces another set of
| words.
|
| The actual techniques are the breakthrough. The result are
| fun to play with and may be useful in some occasions, but we
| don't have to put them on a pedestal.
| holoduke wrote:
| You have the wrong idea of how an LLM works. Its more like an
| model that iteratively finds associating / relevant blocks.
| The reasoning are the iterative steps it takes.
| Retr0id wrote:
| Google never gave a good reason for why they stopped making
| their cache public, but my theory is that it was because people
| were scraping it to train their LLMs.
| sanderjd wrote:
| HN is _always_ divided on "how much is the currently hype-y
| technology real vs just hype".
|
| I've seen this over and over again and been on different sides
| of the question on different technologies at different times.
|
| To me, this is same as it ever was!
| aleph_minus_one wrote:
| I basically agree, but want to point out two major
| differences to other "hype-y" topics that existed in the past
| that in my opinion make the whole AI discussions on HN a
| little bit more controversial than other older hype
| discussions:
|
| 1. The whole investment volume (and thus hope and
| expectations) into AI is much larger than into other hype
| topics.
|
| 2. Sam Altman, the CEO of OpenAI, was president of
| YCombinator, the company begind Hacker News, from 2014 to
| 2019.
| sanderjd wrote:
| On (1): Investment volume relative to what? To me, it looks
| like a very similar pattern of investors crowding into the
| currently hot thing, trying to get a piece of the winners
| of the power law.
|
| On (2): I'm honestly not sure I think this is making a big
| difference at all. Not much of the commentary here is
| driven by YC stuff, because most of the audience here has
| no direct entwinement with YC.
| og_kalu wrote:
| >On (1): Investment volume relative to what? To me, it
| looks like a very similar pattern of investors crowding
| into the currently hot thing, trying to get a piece of
| the winners of the power law.
|
| The profile of investors (nearly all the biggest tech
| companies amongst others) as well as how much they're
| willing to and have put down (billions) is larger than
| most.
|
| Open AI alone just started work on a $100B+ datacenter
| (Stargate)
| sanderjd wrote:
| Yeah maybe I buy it. But it reminds me of the investment
| in building out the infrastructure of the internet. That
| predates HN, but it's the kind of thing we would have
| debated here if we could have :)
| Workaccount2 wrote:
| The ultimate job of a programmer is to translate human language
| into computer language. Computers are extremely capable, but
| they speak a very cryptic overtly logical language.
|
| LLMs are undeniably treading onto that territory. Who knows how
| far in they will make it, but the wall _is breached_. Which is
| unsettling to down right scary depending on your take. It is a
| real threat to a skill that many have honed for years and for
| which is very lucrative to have. Programmers don 't even need
| to be replaced, having to settle for $100k/yr in a senior role
| is almost just a scary.
| kbelder wrote:
| Yes, but the scale isn't 'unsettling' to 'scary'... it's from
| 'incredible' to 'scary'.
| corytheboyd wrote:
| > Just last week i asked for a script to do image segmentation
| with a basic UI and claude just generated that for me in under
| 1 Minute.
|
| I agree that this is useful! It will even take natural language
| and augment the script, and maybe get it right! Nice!
|
| The AI is combing through scraped data with an LLM, and
| conjuring forth some imagemagick snippets into a shell script.
| This is very useful, and if you're like most people, who don't
| know imagemagick intimately, it's going to save you tons of
| time.
|
| Where it gets incredibly frustrating is tech leadership seeing
| these trivial examples, and assuming it extrapolates to general
| software engineering at their companies. "Oh it writes code, or
| makes our engineers faster, or whatever. Get the managers
| mandating this, now! Also, we need to get started on the
| layoffs. Have them stack rank their reports by who uses AI the
| best, so that we are ready to pull the trigger."
|
| But every real engineer who uses these tools on real (as in
| huge, poorly written) codebases, if they are being honest (they
| may not be, given the stack ranking), will tell you "on a good
| day it multiplies my productivity by, let's say, 1.1-2x? On a
| bad day, I end up scrapping 10k lines of LLM code, reading some
| documentation on my own, and solving the problem with 5 lines
| of intentional code."
|
| Please, PLEASE pay attention to this details that I added:
| Huge, poorly written codebases. This is just the reality at
| most software companies that have graduated from series A
| startup. What my colleagues and I are trying to tell you,
| leadership, is that these "it made a script" and "it made a
| html form with a backend" examples ARE NOT cleanly
| extrapolating to the flaming dumpster fire codebases we
| actually work with. Sometimes the tools help! Sometimes, they
| don't.
|
| It's as if LLM is just another tool we use sometimes.
|
| This is why I am annoyed. It's incredibly frustrating to be
| told by your boss "use tool or get fired" when that tool
| doesn't always fit the task at hand. It DOES NOT mean I see
| zero value in LLMs.
| evilfred wrote:
| most work in software jobs is not making one-off scripts like
| in your example. a lot of the job is about modifying existing
| codebases which include in-house approachs to style and
| services along with various third party frameworks like Spring
| driven by annotations, and requirements around how to write
| tests and how many. AI is just not very helpful here, you spend
| more time spinning wheels trying to craft the absolute perfect
| script than just making code changes directly.
| dvfjsdhgfv wrote:
| There is no single reason. Nobody will argue that LLMs are
| already quite useful at some tasks if used properly.
|
| As for the opposing view, there are so many reasons.
|
| * Founders and other people who bet their money on AI try to
| pump up the hype in spite of problems with delivery
|
| * We know some of them are plainly lying, but the general
| public doesn't
|
| * They repeat their assumptions as facts ("AI will replace most
| X and Y jobs by year Z")
|
| * We clearly see that the enormous development of LLMs has
| plateaued but they try to convince the general public it's the
| contrary
|
| * We see the difference on how a single individual (Aaron
| Swartz) is treated when making a small copyright infringement,
| and how the consequences for AI companies like OpenAI or Meta
| who copied the whole contents of Libgen are non-existent.
|
| * Some people like me just hate AI slop - in writing and
| imaging. It just puts me off and I stop reading/watching etc.
|
| There are many more points like this.
| spwa4 wrote:
| TLDR: AI is like any new method in software engineering. It is
| not a general solution, and by itself not that useful, only as an
| addition. Unless an expert human takes a LOT of time to fine-tune
| the method (ie. automatically selecting what works well in what
| case, using the best method in almost all cases) it only performs
| well in a very small subset of cases.
| KurSix wrote:
| The comparison to the replication crisis is spot on
| therebase wrote:
| It is not only about the results we create with these tools, but
| as well about the effect they have on us as a result.
|
| Just about tech engineering here but I do think it transfers to
| science as well.
|
| https://dev.to/sebs/the-quiet-crisis-how-is-ai-eroding-our-t...
| sublimefire wrote:
| Great analysis and spot on examples. Another issue with AI
| related research is that a lot of papers are new and not that
| many get published in "proper" places, yet being quoted
| right/left/center, just look at google scholar. It is hard to
| repro the results and check the validity of some statements, not
| to mention that research which was done 4 years ago used one set
| of models and now another set of models with different training
| data is used in tests. It is hard to establish what really
| affects the results and if the conclusions are applicable to some
| specific property of the outdated model or if it is even
| generalisable.
| skydhash wrote:
| I'm not a scientist or a researcher, but anything based on
| statistics and data interpretation is immediately subject to my
| skepticism.
| sn9 wrote:
| This is silly.
|
| There are practices like pre-registration, open data, etc.
| that can make results much more transparent and replicable.
| eviks wrote:
| > I found that AI methods performed much worse than advertised.
|
| Lesson learned: don't trust ads
|
| > Most scientists aren't trying to mislead anyone
|
| More learning ahead, the exciting part of being a scientist!
| wrren wrote:
| AI companies are hugely motivated to show beyond-human levels of
| intelligence in their models, even if it means flubbing the
| numbers. If they manage to capture the news cycle for a bit, it's
| a boost to confidence in their products and maybe their share
| price if they're public. The articles showing that these advances
| are largely junk aren't backed by corporate marketing budgets or
| the desires of the investor class like the original announcements
| were.
| rajnathani wrote:
| Just 2 days ago, there was an HN post about an AI-aided discovery
| of a fast matrix multiplication algorithm ("X X^t can be faster"
| | 198 points, 61 comments):
| https://news.ycombinator.com/item?id=44006824
| i_c_b wrote:
| I'm probably saying something obvious here, but it seems like
| there's this pre-existing binary going on ("AI will drive amazing
| advances and change everything!" "You are wrong and a utopian /
| grifter!") that takes up a lot of oxygen, and it really distracts
| from the broader question of "given the current state of AI and
| its current trajectory, how can it be fruitfully used to advance
| research, and to what's the best way to harness it?"
|
| This is the sort of thing I mean, I guess, by way of close
| parallel in a pre-AI context. For a while now, I've been doing a
| lot of private math research. Whether or not I've wasted my time,
| one thing I've found utterly invaluable has been the OEIS.org
| website, where you can just enter sequence of numbers and then
| search for it to see what contexts it shows up in. It's basically
| a search engine for numerical sequences. And the reason it has
| been invaluable is that I will often encounter some sequence of
| integers, I'll be exploring it, and then when I search for it on
| OEIS, I'll discover that that sequence shows up in much different
| mathematical contexts. And that will give me an opening to 1)
| learn some new things and recontextualize what I'm already
| exploring and 2) give me raw material to ask new questions.
| Likewise, Wolfram Mathematica has been a godsend. And it's for
| similar reasons - if I encounter some strange or tricky or
| complicated integral or infinite sum, it is frequently handy to
| just toss it into Mathematica, apply some combination of
| parameter constraints and Expands and FullSimplify's, and see if
| whatever it is I'm exploring connects, surprisingly, to some
| unexpected closed form or special function. And, once again, 1)
| I've learned a ton this way and gotten survey exposure to other
| fields of math I know much less well, and 2) it's been really
| helpful in iteratively helping me ask new, pointed questions.
| Neither OEIS nor Mathematica can just take my hard problems and
| solve them for me. A lot of this process has been about me
| identifying and evolving what sorts of problems I even find
| compelling in the first place. But these resources have been
| invaluable in helping me broaden what questions I can
| productively ask, and it's through something more like a high
| powered, extremely broad, extremely fast search. There's a way
| that my engagement with these tools has made me a lot smarter and
| a lot broader-minded, and it's changed the kinds of questions I
| can productively ask. To make a shaky analogy, books represent a
| deeply important frozen search of different fields of knowledge,
| and these tools represent a different style of search,
| reorganizing knowledge around whatever my current questions are -
| and acting in a very complementary fashion to books, too, as a
| way to direct me to books and articles once I have enough
| context.
|
| Although I haven't spent nearly as much time with it, what I've
| just described about these other tools certainly is similar to
| what I've found with AI so far, only AI promises to deliver even
| more so. As a tool for focused search and reorganization of
| survey knowledge about an astonishingly broad range of knowledge,
| it's incredible. I guess I'm trying to name a "broad" rather than
| "deep" stance here, concerning the obvious benefits I'm finding
| with AI in the context of certain kinds of research. Or maybe I'm
| pushing on what I've seen called, over in the land of chess and
| chess AI, a centaur model - a human still driving, but deeply
| integrating the AI at all steps of that process.
|
| I've spent a lot of my career as a programmer and game designer
| working closely with research professors in R1 university
| settings (in both education and computer science), and I've
| particularly worked in contexts that required researchers to
| engage in interdisciplinary work. And they're all smart people
| (of course), but the silofication of various academic disciplines
| and specialties is obviously real and pragmatically unavoidable,
| and it clearly casts a long shadow on what kind of research gets
| done. No one can know everything, and no one can really even know
| too much of anything out of their own specialties within their
| own disciplines - there's simply too much to know. There are a
| lot of contexts where "deep" is emphasized over "broad" for good
| reasons. But I think the potential for researchers to cheaply and
| quickly and silently ask questions outside of their own
| specializations, to get fast survey level understandings of
| domains outside of their own expertise, is potentially a huge
| deal for the kinds of questions they can productively ask.
|
| But, insofar as any of this is true, it's a very different way of
| harnessing of AI than just taking AI and trying to see if it will
| produce new solutions to existing, hard, well-defined problems.
| But who knows, maybe I'm wrong in all of this.
| Workaccount2 wrote:
| This is the second article in a week where someone is writing
| about how "AI" has failed them in their field (here it's physics,
| the other article was radiology), and in both articles they are
| using now ancient mid 2010's deep learning NNs.
|
| I don't know if it's intentional, but the word "AI" means
| different things almost every year now. Its worse than papers
| getting released with "LLMs unable to do basic math" and then you
| see they used GPT-3 for the study.
| scuff3d wrote:
| "I suspect that scientists are switching to AI less because it
| benefits science, and more because it benefits them."
|
| This is a huge problem in software, and it's not restricted to
| AI. So much of what has been adopted over the years has
| everything to do with making the programmers life easier, but
| nothing to do with producing better software. AI is a
| continuation of that.
| indoordin0saur wrote:
| I saw the name of the blog owner (A "Timothy B. Lee") and was
| surprised to see that the ~70 year old inventor of HTTP and the
| web had such an active and cutting-edge blog.
| stonemetal12 wrote:
| AI for science is industrial scale P hacking. Dump in all the
| world's knowledge in to an AI and see what falls out.
| gowld wrote:
| There's an easy fix for that: Choose a smaller (more
| appropriate) P.
| blitzar wrote:
| I got fooled by a Ponzi Scheme-here's what it taught me about how
| to make money.
| toss1 wrote:
| >>Most scientists aren't trying to mislead anyone, but _because
| they face strong incentives to present favorable results_ ,
| there's still a risk that you'll be misled.
|
| >>We also found evidence, once again, that researchers tend not
| to report negative results, an effect known as reporting bias.
|
| >>But unfortunately, the scientific literature is not a reliable
| source for evaluating the success of AI in science.
|
| >> One issue is survivorship bias. Because AI research, in the
| words of one researcher, has "nearly complete non-publication of
| negative results," we usually only see the successes of AI in
| science and not the failures. But without negative results, our
| attempts to evaluate the impacts of AI in science typically get
| distorted.
|
| While these biases will absolutely create overconfidence and
| wasted effort, the fact that there _are_ rapid advances with some
| clear successes such as protein folding, drug discovery,
| &weather forecasting, leads me to expect there will be more very
| significant advances, in no small part because of the massive
| investment in funds and time to the problem of making AI-based
| advances.
|
| For exactly the reasons this researcher spent his time and funds
| to research this, despite his negative results, there was
| learning, and the effect of millions of people effectively
| searching & developing will result in more great good advances
| being found and/or built.
|
| Whether they are worth the total financial & human capital being
| spent is another question, but I'm expecting that to be also
| positive
| bwfan123 wrote:
| nice expose of human biases involved, need more of these to
| balance the hype.
|
| 1) Instead of identifying a problem and then trying to find a
| solution, we start by assuming that AI will be the solution and
| then looking for problems to solve.
|
| hammer in search of a nail
|
| 2) nearly complete non-publication of negative results
|
| survivorship (and confirmation bias)
|
| 3) same people who evaluate AI models also benefit from those
| evaluations
|
| power of incentives (and conflicts therein)
|
| 4) ai bandwagon effect, and fear of missing out
|
| social-proof
| abhinavsns wrote:
| There is a reason why AI fails:
| https://open.substack.com/pub/asimai/p/the-allure-of-ai-for-...
| ausbah wrote:
| > After a few weeks of failure, I messaged a friend at a
| different university, who told me that he too had tried using
| PINNs, but hadn't been able to get good results.
|
| not really related to AI but this reflects a lesson I learned too
| late during some research in college: constant collaboration is
| important because it helps you avoid retreading over areas where
| others have already failed
| mmarian wrote:
| Or the need for researchers to publish their failured
| experiments?
| thearn4 wrote:
| Another reason why the idea of AI agents for science hasn't
| made much sense to me. Research is an extremely collaborative
| set of activities. How good would a researcher be who is very
| good at literature review, but never actually talks to anyone,
| goes to any conferences, etc?
| angry_moose wrote:
| I've been "lucky" enough to get to trial some AI FEM-like
| structural solvers.
|
| At best, they're sortof ok for linear, small deformation
| problems. The kind of models where we could get an exact solution
| in ~5 minutes vs a fairly sloppy solution in ~30 seconds. Start
| throwing anything non-linear in and they just fall apart.
|
| Maybe enough to do some very high-level concept selection but
| even that isn't great. I'm reasonably convinced some of them are
| just "curvature detectors" - make anything straight blue,
| anything with high curvature red, and interpolate everything
| else.
| amelius wrote:
| Could you use these models as a preconditioner in an iterative
| solver?
| xeonmc wrote:
| So it's more like a "second principles" solver, it cannot
| synthesize anything that it hadn't already seen before.
| cadamsdotcom wrote:
| Hard to tell between "doesn't work" and "too early".
| mmarian wrote:
| Line needs to be drawn somewhere though, otherwise you could
| make the same case for crypto and AR/VR.
| -__---____-ZXyw wrote:
| Did the title get changed, or have I started hallucinating?
|
| Title is:
|
| "I got fooled by AI-for-science hype--here's what it taught me"
| kjhughes wrote:
| It got changed (for the worse, in my opinion) away from the
| original title.
|
| The original title is supposed to be favored here unless it has
| a serious problem.
|
| This original title had no serious problem, unless accurately
| summarizing a PhD candidate's thoughtful critique of some
| questionable AI contributions to scientific research is a
| serious problem.
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(page generated 2025-05-20 23:02 UTC)