Verification Is Not Knowledge: Science needs more than answers
blog.cosmos-institute.org
blog.cosmos-institute.org
* https://en.wikipedia.org/wiki/Kepler%27s_laws_of_planetary_m...
People did not understand how or why they worked, but they switched over to them from the Ptolemaic (Tychonic, etc†) system anyway because the math was easier. It was only later (Newton, Einstein) that we understood the "why" and "how".
† https://tofspot.blogspot.com/2013/08/the-great-ptolemaic-sma...
If Kepler's models explained the how and why, why were Newton and Einstein necessary?
Kepler's model predicted the behavior of the planets, and so does Newton's model, but Newton's model is more general, applying not only to planets, but also to apples, cannonballs, trains, and other objects with mass.
Einstein's model is more accurate and precise than Newton's model, especially about the orbit of Mercury. Einstein's model also predicts gravitational lensing (e.g. during eclipses), which Newton's model doesn't.
"Why" questions are very hard for physicists. Generally, when we ask "why" questions, we're asking what prior Event X caused Event Y. But for universal laws, there is no prior event that caused the universal law to be true. The best we get is more generality. Planets orbit the sun for the same reason apples fall to the earth, but we don't know why gravity happens, only how. Magnets resist each other for the same reason the floor is hard, but we don't know why magnetism happens, only how.
Yup:
* https://en.wikipedia.org/wiki/Principle_of_sufficient_reason
Tao and such are merely biological beings serving biological imperative to survive. Educated as they are they are well aware social society expects them to engage in knowledge role-play to barter for food
The recursive non-fractal nature of reality where "function exits" when a human dies naturally fosters signal attenuation and the formation of new spoken traditions among the future generations
There is no propagating our social ways 100 years into the future. We can only propagate our material outputs. The semantics we define to describe something are lost to time.
The provenance to future people was past people; that's all that matters in the future not how we communicated together now
What? He worked meticulously to match the observations.
I don't understand your claim here at all.
The next guy was Newton - he knew the solutions to a central force problem (thanks to his invention of Calculus - notation for calculating with infinitesimals). Movement with conserved angular momentuum <=> movement with the equal-area property <=> happens for a force ~1/r^2. From that he concluded that the central force is "gravity". The deriveration is simple enough for 1st semester physics student, but you will need to cleverly change variables once or twice to get a familiar integral.
https://en.wikipedia.org/wiki/Central_force
https://en.wikipedia.org/wiki/Classical_central-force_proble...
Nonsense! It was pilfered.
I'd take Asimov's "Relativity of Wrong" here. And I also think there's motivation for why scientists care so much about elegance.
It's an intuition you build pretty early on in science courses in undergrad, somewhat related to Occam's Razor. It's why the optimal packing of 11 squares[1] is so unsatisfying. It flies in the face of all the intuition we've built that simple (and symmetric) solutions are more likely to be true. Machine learning practitioners use this intuition when performing model selection as well: the simpler model (i.e. with fewer free parameters) is less likely to be overfit to the data.
[1] https://en.wikipedia.org/wiki/Square_packing#/media/File:Pac...
> It's an intuition you build pretty early on in science courses in undergrad
> It's why the optimal packing of 11 squares is so unsatisfying.
"Beauty is in the eye of the beholder" - but let me just quickly find an alternative representation of the solution that is much more symmetric and satisfying. There, "beautiful"!
Why be careful? What would be wrong with that conclusion (if it's true)? I'm comfortable with arriving at ambiguity:
* https://en.wikipedia.org/wiki/Principle_of_sufficient_reason
* https://edwardfeser.blogspot.com/2023/04/what-is-law-of-natu...
There's the metaphysics problem in that all models are wrong. There's more than one competing version of physics (as will help you read Asimov's argument/rant). There's many valid ways to interpret "why" something happens. Physics is trying to understand the causal change of events so that we can generate a model that makes strong and accurate counterfactual predictions. Were that type of model "complete" would we have answered "why" things happen? Depends which "why" you're asking. You could still proclaim, as Fesser notes, "that's the rules God made", which isn't meaningfully different than "that's the way it is".
As for the PSR I'd claim everything does happen for "a reason". But is that reason always knowable? Certainly not. And arguably my namesake proved that there can't ever be any fully complete physics model.
So why be careful? Because my responses are limited, you must ask the right questions. ;)
Fields medalist and as Tao listed as one of the most important mathematicians of our age.
Scholz is known to find „precisely the correct and cleanest way to incorporate all the previously done work and find an elegant formulation for that — and then, because he found really the correct framework, go way beyond the known results“
In a way that man is the complement of an AI, he generates understanding rather than just proofs.
Maybe he is the right template for the mathematicians of the future.
https://www.quantamagazine.org/peter-scholze-and-the-future-...
See the Liquid tensor experiment:
https://xenaproject.wordpress.com/2020/12/05/liquid-tensor-e...
https://www.ma.imperial.ac.uk/~buzzard/xena/pdfs/liquid_tens...
" I spent much of 2019 obsessed with the proof of this theorem, almost getting crazy over it. In the end, we were able to get an argument pinned down on paper, but I think nobody else has dared to look at the details of this, and so I still have some small lingering doubts. "
In a sense, mathematics is not very different from software engineering.
More precisely: Curry–Howard correspondence
https://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspon...
“ 1. Members commit to not working with commercial AI companies by providing technical labor, mathematical knowledge, consultation, or publicity.
2. Members commit to not publishing mathematical texts (including papers, reviews, referee reports, lecture notes, teaching materials, and books) generated by AI models. Members only publish their own writing, as opposed to outputs of AI tools. We affirm that mathematical writing should have human authorship. “
One bit stands out to me: to better understand, Scholz will work the problem backwards. Sometimes that strategy is just so spot on.
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To my eternal shame, philosophy (like this OC) makes my head hurt. Pretzels my brain, ouroboros like (aka "head up my own butt").
Caveats covered, here's my cave man cold take on two aspects of this topic.
#1 As we're all trying to paraphrase here: answers are great; understanding is even better. I like the word "legibility" (h/t James Scott). The current models are not very legible.
Which directly leads to:
#2 Understanding enables predictions.
If LLMs are stochastic parrots, then pi.dev (GPT + reasoning + ralph loop) is a drunken sailors walk (thru a solution space). A kind of messy heuristic for optimization. With some kind of gate for declaring "done!" Pretty useful.
However, I don't expect Pi.dev to make predictions. That'd require something else. Maybe that something is being worked on; am noob, so I wouldn't know. (I'm still unclear about whatever Jeff Hawkins is doing, in practical terms.)
--
Wrapping up: Automated prediction would be cool. That's very different from finding solutions to problems. And the current cohort of AI can't even explain why, much less make predictions.
Assuming predictions requires understanding. Maybe there are other strategies to generate predictions. Like a meta drunken sailor's walk, with drunken sailors all the way down.
(Someone please correct me as needed. TIA.)
[Aside: That article has an ad for their podcast They Joy of ₍₎. Perfect. I have no idea if it lives up to its promise.]
In David Deutsch's book The Beginning of Infinity, he argues, that the purpose of science is not to merely predict or to model the world, but to explain it. And he gives in my opinion convincing arguments, why a better explanation is needed to advance science. Therefore, he deduces that pure empiricism has failed. Because no matter how much data you have gathered, an explanation does not follow from it. The explanation is always an insight the scientist has to contribute. Then it is open to scrutiny by criticism or experiments.
I guess he would argue the same is true for mathematicians. Merely constructing proofs to arrive at theorem, is of no intrinsic value, if it doesn't spark an idea of a more elegant formalism that corresponds to a simpler and even more far reaching explanation.
I cannot recommend this book highly enough to anyone who is intellectually curious, not only about epistemology.
That's not to say there isn't value in building mathematical insight for humans, just that there is a goldmine and a lot of people know what to do with gold, even if they don't understand the ecosystem surrounding the gold.
> Scholze was a student of Rapoport, who was a student of Deligne, who was a student of Grothendieck, who was a student of Schwartz; in this chain, Scholze, Deligne, Grothendieck, and Schwartz are all Fields medallists.
If you're in academia, you've probably noticed a major shift recently. Research production at an industrial scale through AI.
Grants have "always" been awarded to professors based on quantity multiplied by quality. Now, some professors are churning out 1–3 papers a month with the same group of co-authors. All the grant money goes to them. Grant givers are just only counting the numbers of papers being published.
The Industrial Revolution has hit academia, and some academics are busy arguing for the artisanal value of research while some are creating factories for research.
The problem is the systemic disincentivation of true science, the pursuit of truth and knowledge unencumbered by greed.
When you speak favorably of "factories for research", you adopt the position of a mindless consumer, relishing the prospect of "abundant" products of science.
You fail to notice, livestock lives in abundance as well.
If "false" scientists end up getting all the money, resources, and social status from spamming LLM-generated papers, and the "true" scientists are shunned as slow idealists with 0 value, the first one is more of a scientist in the eyes of society than the latter.
The "publish or perish" culture is going through what is essentially the same transformation brought by the invention of the assembly line. Academia, "as an industry", is experiencing what the automobile industry went through a century ago.
Grant approvals are public. Just go through them, and you will see a pattern emerge.
The argument I keep hearing is, "AI is good in the hands of a good researcher."
5–7 papers based on the same dataset? 5–7 papers on the same core topic, published within a six-month period?
They tell me that this is their core specialization and that I simply don't understand how science works.
Science is a self-governed, self-serving, and artificially defined concept.
Science is an ideal, the pursuit for knowledge for its own sake. Science is a boon for humanity because it provides an unparalleled degree of prediction and control of the natural world.
But it doesn't tell you what to do with that.
A "capitalist" will seek personal, short-sighted gain from it, socializing repercussions, no matter how devastating. They will gladly poison society and the planet and sell it to you as "your own fault". Their life-span is limited still, so what do they care?
Technological capability exceeding human wisdom is the core issue here.
You need scientific understanding on your own side in order to know and judge the alternatives beyond what vested interests feel obliged to tell you.
The decision about what to do only becomes relevant if the underlying information is true.
Think of AI as a magic wand or a genie that makes your wishes come true.
While that sounds great at first, actually thinking it through reverses that mirage completely.
Whom would you want to have one? Who should get to decide? How do you control the whole matter and prevent it from going horribly wrong?
Being irresponsible doesn't work already when you're a grown-up human with only your bare hands.
Exploiting science for economic utility alone misses the incurred second order effects, as I posited above. You completely ignore that crucial part, despite of it being the whole (very much practical) point here.
You can of course try new ideas and make software tools much faster now, but software is much like meetings. They can be productive if properly motivated, but they can also be a way to pretend you are working without contributing anything of real value.
Theoretical computer science and mathematics have changed. I'm not sure if they will remain active fields of research in the future. If they will, I would guess that theorems and proofs will eventually become things you include as supplementary data rather than in the main paper. Outside some rare exceptions, people may not consider them publishable results anymore.
The disdain for human intelligence is dripping off your comment "some academics are busy arguing for the artisanal value of research "
I've noticed a cooling down of the human chauvinism arguments since the QRH results.
Thing is, though, verification is knowledge itself. And if we can get to a point where the important questions have answers, we can surely have the verifiers explain those to us in ways we can understand them.
This is not obvious to me at all. LLMs are capable of amazing feats of reverse engineering but are terrible at explaining and abstracting things well.
I think this is an artefact of how we train them, and they could be trained to be better at this if we focused on it, but at the same time, it shows its possible for these things to be able to do things they cant explain to us (granted, they can't explain them well - you can bash your way to understanding with them anyway but they aren't good at it)
It's amazing to me that language models are still such poor writers and communicators. The mathematics community wrestled with this recently, when many solutions dropped into their laps but were not explained very well...
Thing is, (1) generic LLMs will improve, and (2) their lack of ability in this area has a lot to do with the rhetorical and structural habits they pick up during RLHF/instruction-tuning, which trained them for general-purpose comms. Zero-training base models can presumably be trained specifically to communicate scientific concepts. Interesting times.
(Edited to add: With respect to (1), compare Opus 5 to 5.5. 5 was a terrible communicator, even of software/coding concepts, whereas 5.5 is excellent and leaves very little to be desired.)
Hm, that may have to do with (faking) confidence. I've often enough with 5.5 still run into cases where it confidently explained something, at the surface it kinda made sense, but the more I dug and verified, the more it became apparent that it was entirely wrong with the most crucial core concepts. It has gotten to a point where you really need a domain expert though to realize this, which makes it tough for using it in areas where you are not an expert and don't have easy access to one either.
Verification isn't knowledge. Far from it. You can verify statements without knowing why they're true. Variants of that concept are e.g. used in cryptology.
You need humans to truly understand (to know the context) in order to make correct judgements. Without actually knowing the context, you have no way to compare.
The same goes for our own time. A powerful AI can tell us things at the edge of what we can grasp -- or beyond what we can grasp -- and it would nevertheless be knowledge that we possess. We can work to verify it, we can have it explained to us as though we were schoolchildren, or we can simply accept it as a static fact of the natural world.
In truth, we're going to have to learn to coexist with minds that are more powerful than ours. At least in some respects, if not in all respects. The good scenario is that they share information with us, and we receive it however we can. "Head in the sand" and "mutual disdain" are bad outcomes.
You might want to look up the actual definition of knowledge. It's a pretty interesting topic in epistemology really, far from your simplistic take.
You already live in coexistence with minds more powerful than yours. Society works in a way that you never need to notice that, those minds taking care of things beyond your grasp for you.
The downside of that "convenience" is you developing an over-simplified bubble-view of the world, rendering you prone to arbitrarily hazardous misjudgements.
Here, you align foolishly with interests that cannot possibly be your own. Namely, exploiting scientific knowledge for personalized monetary gain on an unsustainable scale.
When a tiny group of people hogs essentially all scientific knowledge, that renders you what?
Your idea of being graciously provided with some "basic income" or whatever is blind copium. You would live as livestock at best, to be discarded as useless whenever your masters see fit.
Merely claimed "descriptions of reality" lack the crucial property of truth. The description has to actually fit reality, or it's mere fantasy. It has to be exhaustive, or it remains a facade, leaving you blind to what lies beneath.
When you don't actually know whether it does any of that, you're merely a believer, entirely dependent on your "oracle" and its slave in effect.
Not caring about nuance leaves you open to exploitation, among other things.
But I understand what you mean. Suppose I am Archimedes in there. I don't have access to the axiom, so I don't know if what the oracle told me is true until I can verify it myself. So verify I will. This is what is being done with AI-written proofs IRL. Humans may not be able to internalize the proofs, but we can internalize the programs which verify the proofs. So we do know the proofs are correct, or rather, as correct as the verifier programs.
He's wrong because the epistemological formula is the justified true belief. The infallible oracle simply gave Archimedes a true belief, the needed justification, sensors that can actually give meaning to the precise number, would take hundreds of years to develop. Archimedes would have to sit there with the oracle and develop modern physics together before the oracle can be said to have given him knowledge.
And AI won't have given us any actual knowledge until it can fit its findings into the mathematical establishment. I forget who it was that said that society doesn't know anything until you can teach it to a room-full of undergrads, but that's about right.
And you will have to actually show your intellectual work before your arguing here will actually accomplish your goal of sounding smart.
The rest of your post is excessively emotional and off-target.
How does random data differ from "information you gained by study"? Let alone from "what you're being told"? How do you possibly notice?
Other people cannot force you to gain true knowledge. It has to be earned by putting in the necessary effort. Its value is manifold, starting with not gullibly falling prey to other's interests.
As far as I know labs try to train the other way, else we'd have monstrously long and useless outputs, but its hard to overcome these natural biases for length
Like a superposition of all kinds of writing styles, all perspectives on every topic and all kinds of ways to do things right and wrong.
In post-training (and of course already by sensibly curating the input data in the first place), people try to select useful submanifolds from that.
Ideally, you get a combination of all the extant strengths of people combined into one model. Self-improvement then leaves the sphere of human capability for good.
Many people don't like the style, probably by association with slop and fatigue more than the style being fundamentally wrong.
Being a good communicator is an entirely different thing, you can have terrible spelling and grammar and still be good at getting ideas across. It is a common situation when the communicator in question is not using his native language.
That LLMs are so good at writing and so bad at communicating is not surprizing. The key to good communication is getting into the other people's mind, LLMs can't do that, because they "think" in a way that is completely alien to us. We use words to express our thoughts, thoughts that are based on a physical world model: touch, movement, sound, light, etc... LLMs have no idea about how the physical world work, everything the see is just words, they have no intuitive idea of, say, objects falling down, just that "falling" and "down" go well together. And for explaining things, they will go "someone asks for an explanation, explanations usually use these words, I will be using the same kind of words".
And the abstractions do still matter, since in many cases it is an issue of tractability. Perhaps an LLM can force its way to a solution through 3 million mind numbingly boring and convoluted steps, but if the number of steps increases exponentially without the right abstraction that means it can only handle a slightly larger problem than a human with pen and paper.
I don't think you use them well if you think this.
What is your genius setup?
Obviously if you don't understand an idea you aren't really in a position to evaluate how good an explanation of it is. I have a background in Physics research and find models like Astra to be excellent at explaining concepts at a level for an advanced practitioner. It also has done very well, from what I can tell, for areas I am less knowledgable in. So I am confused by statements like yours that directly contradict my experience and the subjective experiences of many others.
Something I do dozens of times a day: "Please explain what this PR does, I haven't read it"
it will make me 20 pages of gibberish about caches, correctness checking, race conditions, github action runners, tradeoffs between a bunch of invented terminology internal to the PR without defining the terms- you'd have to have already read the code to know what it was referring to
without ya know, explaining it. I ask my colleague and he says something like "oh it deletes records from such-and-such a system when they aren't needed anymore after a merge" and I know exactly what he means. A single well composed sentence was all that was needed.
You can ask the agent for a single sentence explanation and it will write something like "linked candidate records are trimmed when canonical record determination window is passed", none of which is terminology any of us use, some of which is defined in the new PR and some of which is made up. I can't emphasise enough, I use these things EVERY DAY. it knows what I work on, it knows how I talk. There is no earthly reason it should be this bad given all the skills and advance and context it has. As mentioned, I constantly work to correct its shortcomings in this area but its like trying to hold water in a sieve, it just cannot do it.
"What does this PR do?"
LLM: "Oh, it specifies the kernel window over the cache candidate list to classify possible invalidated swap identifiers"
Colleague: "It removes duplicates in the target_uuid_cache table in the db"
The agent has full context of what systems the code talks to, our terminology sheet etc. It just pathologically cannot simplify things down. If you work with them enough you learn that there are some words it reaches for a lot in certain circumstances, like it loves "canonical" in relation to merging things. But this is more like learning to communicate with a deeply autistic person who struggles to adjust their speaking to be relevant to their audience rather than talking with a "good communicator"
Same for some methods I've since forgotten for solving polynomials. They were non-iterative algorithms that could work with any constants. They weren't faster than other methods, but you could fall back on them when you didn't have anything better.
That is what they were trying to teach me. General solutions that could work with anything. While, of course, it's valuable to be able to intuit shortcuts on the fly, if that's all you're capable of, there's a class of problems you simply can not solve. If they don't click in your head enough for you to come up with a specific shortcut, you wouldn't have a general one to fall back on.
I've always used computers, and didn't understand why my teacher got so upset when I programmed equations into my calculator. Like you'd think writing a program around a family of equations would develop experience with those equations, and I thought it was a good way to speed up the solving process. Needless to say, teacher did not agree, and quickly learned to ban my calculator on tests.
(technically, it was supposed to be banned anyway because it had a CAS... but I got it because 68k TI-BASIC was better than the Z80 TI-BASIC)
I still don't know the general solutions for most of the problems I work with nowadays, and I think that sucks. I think AI makes this way worse on a scale much greater than mathematics.
Sometimes I wish I could go back to school and catch up on all the basics I've been cheating around. I feel like my apparent intelligence is a mistake and I need to develop a better foundation.
Looks like I was the opposite to you in regards to calculators. I always preferred to do it the hard way and only used calculators for boring things that I knew by heart, but would take me longer to do by hand and be error prone.
But I didn't pursue a career where this type of stuff was any useful and forgot many of the things I could do easily in school, like integral and differential calculus.
As the saying goes, use it or lose it. No matter how foundational, you'll eventually forget all about it if you do not use it anymore. There are even cases of people that completely forgot their first language.
I'm optimistic about AI because it can have more knowledge than any given human in storage. Eventually we all kinda specialize around something. Even the generalists among mathematicians have little insight into medicine, rocket science or astrophysics. So we all become the hammers of the saying "if you are a hammer, everything looks like a nail". But when including AI we get a broader perspective.
> https://en.wikipedia.org/wiki/Hierarchy_of_the_sciences
First math and CS, where formal logic dominate, and AI can check/verify its own answers very quickly. Then physics, downstream of math. Then materials science, which in some cases is practically a branch of physics. Then chemistry more generally. Then biology. Then various social sciences.
Math is really super suitable for quick iterations by a machine.
Knowing that something is true or false will at least stop humans wasting time trying to prove or disprove it, and allow them to find a more elegant proof, or investigate the implications of the true/false result.
Autonomous or human-directed AI agents submit their work to a public repository.
Then the scientific community draws from this pool of results, identifies the important ones, and starts working on adding the results to the greater body of scientific knowledge. This work of interpretation, synthesis and question formation should be recognised and rewarded in peer-reviewed academic journals.
I wrote a blog here talking about it: https://colinocallaghan.com/posts/answers-are-getting-cheape...
Knowing that something is true or false
I am far from well-informed on the intricacies of this debate myself, however I class myself as a realist. Not only do I have to know how and why things work, I also observe that an explanatory theory is at least as useful as an accurate model (of the same phenomenon). Assuming equal accuracy, the explanatory theory is just as good at predicting future observations as the model, and on top of that it is capable of explaining past observations. More, we seem to always be able to improve our predictive accuracy, compared to non-explanatory models, once we have an explanatory theory and can understand why models work and why they fail. Understanding ultimately improves prediction but prediction alone does not necessarily improve understanding.
I think this is the crux of the debate with AI mathematics: AI proofs are nice to have but if we don't understand them, what are we going to do with them to further advance our knowledge?
I see this also in the way neural nets work in general. We can train very powerful neural nets, but can we reuse one trained neural net to train another? Not unless we use the first net to label some data. I mean to say, we can't just use a trained neural net as a feature of another neural net. Which is why most of the time we have to learn everything from scratch, end-to-end, and consequently why neural nets need so much damn training data.
Finally, say that AI takes over mathematics research. What happens a few years down the line when all we have is yes/no answers? How are we going to make new mathematics from that? How are we going to keep training AI mathematicians? Let alone human ones?
I wonder if this dichotomy fully captures the value of scientific discoveries. One could argue that their practical impact can be independent of whether humans fully understand how they were reached.
See: most of pharma
The P vs NP complexity dichotomy is also relevant: they present different views of a problem solution, knowledge and verification respectively. Both are valid science: the first is desirable but in absence of deep understanding the second is acceptable.
A broken clock is not correct twice a day.
However, even getting truth answers will soon be beyond AI. It is of course making impacts in Math and in publishing in "data heavy" subjects where you can just formally verify, program, and/or munge data.
But in "real science" where we actually need to build a lab, buy supplies, hire personnel, get approval, form physical test harnesses, install sensors in proper locations, calibrate everything, then finally make one measurement, question the whole thing, then finally make a conclusion, AI is of limited benefit.
Yes, it can help speed up coming up with some formulas, spitballing, or checking existing research. It can even help write the jank code for sensor calibration, measurement taking, etc. That's not nothing, but we are _very far_ from meaningful contributions to real experimental hard sciences.
As for math, theoretical physics, computer science, and other "logic based" studies, the question becomes one of benefit to cost of true understanding versus simply knowing something is true.
I'd argue that it paints a starkly negative picture of most science. Once answers are easy to obtain, it seems most people are concluding that actually understanding why its true isn't worth the effort, which means that the question maybe wasn't worth asking.
Its the science equivalent to the recent realization in tech: most ideas suck, your product didn't need more bells and whistles. Actually the engineers pushing back on every shower thought from product was a good thing. The friction that stops you from putting every random-ass feature into your already bloated useless SaaS product was good, and now the sales guy is pushing features to prod for a customer demo. We thought "too many ideas, not enough engineers", we now know "not enough meaningful quality ideas".
In the same way we thought "too many questions, not enough scientists", we now know "not enough real study on meaningful topics".
For many types of study, especially new ones, having such a setup is just as hard as doing the thing that may or may not require massive automation. For example, testing a new type of high acceleration engine. How would you even setup a lab to have hundreds of test beds for this? Would you need to manufacture hundreds of prototypes of the engine to even do the test? Where would you source that if the whole point is proving whether the engine works and produces more thrust. You probably just have one prototype sitting on a table. Sure when you get to the point of refining a tested and well-studied design, automation helps.
I'm sure there are some areas like organic chemistry where you can just toss compounds together and vary the heat, etc. But what are you getting? Don't you need to do specific tests and measurements to figure out if its useful? All of that requires setup with all the caveats above. What if you're doing something else, like metallics. Well then you need a whole lab with temperature and atmosphere controlled furnaces, various cooling surfaces, additives, dopants, etc. Knowing when / where to add, how to cool, rotate, etc. could all play a factor. Automating that right off the bat isn't feasible until you've proven it can actually work, so AI help is of limited value.
Now, if we have AGI with full embodiment, that's a different story (since with full embodiment it can handle all these tasks if given authority to do so).
This is the point where robotics can re-arrange their own labs.
"If I have seen further, it is by standing on the shoulders of giants". Including AI.
Is this description AKA. theory vs praxis? Academia vs Engineering?
Okay, it's not quite the same. I could figure out how the thermometer works, but I cannot figure out how the 1 trillion parameter AI works. But the AI didn't give me an answer directly, it gave me a proof that I can run through a verifier. And I could figure out how the verifier works. That is a partial understanding.
The barrier to my understanding is not the verifier. So it's the opaque AI model? But human brains are blackboxes too. I cannot tell what's going on in another person's head when they're writing a proof either. I can only look at their behavior, the language they produce.
An AI provides a proof written in the same symbols humans write in. It's really long and complicated, more than human-written proofs. It would be nice if I had an excuse for my inability to internalize the proof in a timely manner. I could form a community to make the proofs we don't fully understand yet = "not knowledge".
This is delusional self-soothing, and it's just as damaging to my capacity to understand reality as delegating thought to another entity, regardless of whether that entity is a computer or a human.
A good word predictor happens to be good with closed systems with very defined rules for which a huge training corpus exists and we are suddendly freaking out about how it's gonna become so much better than us at everything that we are basically obsolete.
When in doubt about where AI stands, ask it to create a poem, a joke or a riddle. And see how little actual intelligence it has.
I propose a two-part approach.
First, autonomous or human-directed AI agents submit their work to a public repository.
Second, the scientific community draws from this pool of results, identifies the important ones and asks the next set of important questions. This work of interpretation, synthesis and question formation should be recognised and rewarded in peer-reviewed academic journals.
A bold stand on the P = NP question.
Science needs a metaphysical basis and certain assumptions to even get off the ground:
> That there is an objective reality shared by all rational observers; That this objective reality is governed by natural laws; That reality can be discovered by means of systematic observation and experimentation; That Nature has uniformity of laws and most if not all things in nature must have at least a natural cause; etc.
* https://en.wikipedia.org/wiki/Naturalism_(philosophy)#Provid...
Not all worldviews hold these views / make these assumptions.
It would probably be 'too much' to think about them everyday if you're going is 'doing science', but that doesn't mean they don't exist or are not important.
Meta: The scifi author Vernor Vinge played around with this concept with his "Zones of Thought", e.g.:
This so-called "science without philosophy" is how the world got to this situation, the wonders and horrors of modern civilization and technology. Much of it serves the dominant power structure - and when it does, it is without morals, ethics, aesthetics, or any deeper philosophy other than extracting the maximum value by exploiting everything it can, at the expense of the world and all living beings.
Elsewhere in this thread was the imagery, the Industrial Revolution of Academia and Factories of Knowledge.
But what is knowledge without the wisdom to use it for the benefit of all? Otherwise it only serves the interests of power-hungry men with no heart, a blackhole of greed and collective self-destruction. There is a dark philosophy there, based on primitive desires.
Science that serves humanity needs to draw on the rich history of the world, its cultures and philosophies.
It's my firm belief that the world got to this situation in large part because of modern philosophy, it surely aided the movement to where we are now.
"Science without philosophy" is simply a parking spot for people dissatisfied with the current state of philosophy. Maybe philosophers should learn to ask the right questions and provide good answers?
> But what is knowledge without the wisdom to use it for the benefit of all?
And where do we find that wisdom? There's none in philosophy and only scattered bits in other sciences.