[HN Gopher] Artificial Intelligence, Scientific Discovery, and P...
___________________________________________________________________
Artificial Intelligence, Scientific Discovery, and Product
Innovation [pdf]
Author : therabbithole
Score : 98 points
Date : 2024-11-12 13:29 UTC (9 hours ago)
(HTM) web link (aidantr.github.io)
(TXT) w3m dump (aidantr.github.io)
| newyankee wrote:
| Well I hope it works well and fast enough. I cannot wait for my
| 10k cycles, 300 Wh/kg batteries. 35% efficiency solar modules in
| market at cheap prices and plenty of nanotech breakthroughs that
| were promised yet we are still waiting on
| 11101010001100 wrote:
| Any idea if the points raised here
|
| https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00643
|
| were considered in the analysis?
| bbor wrote:
| Well damn, that's a lot more specific and empirical than I was
| expecting given the title. Fascinating stuff, talk about a useful
| setup for studying the issue! "AI is useless to many but
| invaluable to some" (as mentioned in the abstract) is a great
| counterpoint to anti-AI luddites. No offense to any luddites on
| here ofc, the luddites were pretty darn woke for their time, all
| things considered
| youoy wrote:
| From the conclusions:
|
| > I find that AI substantially boosts materials discovery,
| leading to an increase in patent filing and a rise in downstream
| product innovation. However, the technology is effective only
| when paired with sufficiently skilled scientists.
|
| I can see the point here. Today I was exploring the possibility
| of some new algorithm. I asked Claude to generate some part which
| is well know (but there are not a lot of examples on the
| internet) and it hallucinated some function. In spite of being
| bad, it was sufficiently close to the solution that I could
| myself "rehallucinate it" from my side, and turn it into a
| creative solution. Of course, the hallucination would have been
| useless if I was not already an expert in the field.
| darepublic wrote:
| I find proofreading the code gen ai less satisfying than
| writing it myself though it does depend on the nature of the
| function. Migrating mindless mapping type functions to
| autocomplete is nice
| mkatx wrote:
| This is one big point I've subscribed to, I'd rather write
| the code and understand it that way, than read and try to
| understand code I did not write.
|
| Also, I think it would be faster to write my own than try to
| fully understand others (LLM) code. I have developed my own
| ways of ensuring certain aspects of the code, like security,
| organization, and speed. Trying to knead out how those things
| are addressed in code I didn't write takes me longer.
|
| Edit; spelling
| vatys wrote:
| I wonder if the next generation of experts will be held back by
| use of AI tools. Having learned things "the hard way" without
| AI tools may allow better judgement of these semi-reliable
| outputs. A younger generation growing up in this era would not
| yet have that experience and may be more accepting of AI
| generated results.
| mkatx wrote:
| Yeah, as a cs student, some professors allow use of LLM's
| because it is what will be a part of the job going forward. I
| get that, and I use them for learning, as opposed to internet
| searches, but I still manually write my code and fully
| understand it, cause I don't wanna miss out on those lessons.
| Otherwise I might not be able to verify an LLM's output.
| daveguy wrote:
| Excellent approach. You will be leagues ahead of someone
| who relies on LLM alone.
| svara wrote:
| I'm pretty sure people said the same thing about compilers.
|
| That's how progress works. Clever people will still be
| clever, but maybe about slightly different things.
| prisenco wrote:
| I call this the "babysitting problem."
|
| If a model is right 99.99% of the time (which nobody has come
| close to), we still need something that understands what it's
| doing enough to observe and catch that 0.01% where it's wrong.
|
| Because wrong at that level is often _dangerously_ wrong.
|
| This is explored (in an earlier context) in the 1983 paper
| "Ironies of Automation".
|
| https://en.wikipedia.org/wiki/Ironies_of_Automation
| adrianN wrote:
| I'm pretty sure humans make mistakes too and it happens
| rather frequently that nobody catches them until it's too
| late. In most fields we're okay with that because perfection
| is prohibitively expensive.
| prisenco wrote:
| Obviously systems have always had to be resilient. But the
| point here is how dangerous a "set it and forget it" AI can
| be. Because the mistakes it makes, although fewer, are much
| more dangerous, unpredictable, and inscrutable than the
| mistakes a human would make.
|
| Which means the people who catch these mistakes have to be
| operating at a very high level.
|
| This means we need to resist getting lulled into a false
| sense of security with these systems, and we need to make
| sure we can still get people to a high level of experience
| and education.
| Animats wrote:
| > we still need something that understands what it's doing
| enough to observe and catch that 0.01% where it's wrong.
|
| Nobody has figured out how to get a confidence metric out of
| the innards of a neural net. This is why chatbots seldom say
| "I don't know", but, instead, hallucinate something
| plausible.
|
| Most of the attempts to fix this are hacks outside the LLM.
| Run several copies and compare. Ask for citations and check
| them. Throw in more training data. Punish for wrong answers.
| None of those hacks work very well. The black box part is
| still not understood.
|
| This is the elephant in the room of LLMs. If someone doesn't
| crack this soon, AI Winter #3 will begin. There's a lot of
| startup valuation which assumes this problem gets solved.
| JohnMakin wrote:
| > There's a lot of startup valuation which assumes this
| problem gets solved.
|
| Not just solved, but solved _soon._ I think this is an
| extremely difficult problem to solve to the point it 'd
| involve new aspects of computer science to even approach
| correctly, but we seem to just think throwing more CPU and
| $$$ at the problem will work itself out. I myself am
| skeptical.
| fsndz wrote:
| I came to the same conclusion a while back. LLMs are very
| useful when user expertise level is medium to high, and task
| complexity is low to medium. Why ? because it those scenarios,
| the user can use the LLM as a tool for brainstorming on drawing
| the first sketch before improving it. Human in the loop is the
| key and will stay key for the forceable future no matter what
| the autonomous AI agent gurus are saying.
| https://www.lycee.ai/blog/mistral-ai-strategy-openai
| slopeloaf wrote:
| " _Survey evidence reveals that these gains come at a cost,
| however, as 82% of scientists report reduced satisfaction with
| their work due to decreased creativity and skill
| underutilization._ "
|
| What an interesting finding and not what I was expecting. Is this
| an issue with the UX/tooling? Could we alleviate this with an
| interface that still incorporates the joy of problem solving.
|
| I haven't seen any research that Copilot and similar tools for
| programmers have a similar reduction in satisfaction. Likely with
| how much the tools feel like an extension of traditional auto
| complete, and you still spend a lot of time "programming". You
| haven't abandoned your core skill.
|
| Related: I often find myself disabling copilot when I have a fun
| problem I want the satisfaction of solving myself.
| gmaster1440 wrote:
| AI appears to have automated aspects of the job scientists
| found most intellectually satisfying.
|
| - Reduced creativity and ideation work (dropping from 39% to
| 16% of time)
|
| - Increased focus on evaluating AI suggestions (rising to 40%
| of time)
|
| - Feelings of skill underutilization
| sourcepluck wrote:
| > Related: I often find myself disabling copilot when I have a
| fun problem I want the satisfaction of solving myself.
|
| The way things seem to be going, I'd be worried management will
| find a way to monitor and try cut out this "security risk" in
| the coming months and years.
| dennisy wrote:
| I feel if people are finding programming as creative and
| interesting with AI as without there is a chance they actually
| prefer product management?
|
| Half statement, half question... I have personally stopped
| using AI assistance in programming as I felt it was making my
| mind lazy, and I stopped learning.
| aerhardt wrote:
| The thing I like the most about AI coding is how it lowers
| the threshold of energy and motivation needed to start a
| task. Being able to write a detailed spec of what I want, or
| even discussing an attack plan (for high-level architecture
| or solution design) and getting an initial draft is game-
| changing for me. I usually take it from there, because as far
| as I can tell, it sucks after that point anyway.
| rwyinuse wrote:
| As a programmer I feel that software development as in
| "designing and building software products" can be still be
| fun with AI. But what absolutely isn't fun is feeding
| requirements written by someone else to ChatGPT / Copilot and
| then just doing plumbing / QA work to make sure it works. The
| kind of work junior devs would typically do feels devalued
| now.
| uxhacker wrote:
| It's interesting to see how this research emphasizes the
| continued need for human expertise, even in the era of advanced
| AI. It highlights that while AI can significantly boost
| productivity, the value of human judgment and domain knowledge
| remains crucial.
| nyrikki wrote:
| Even Warren McCulloch and Walter Pitts were the two who
| originally modeled neurons with OR statements, realized it
| wasn't sufficient for a full replacement.
|
| Biological neurons have many features like active dendritic
| compartmentalization that perceptrons cannot duplicate.
|
| They are different with different advantages and limitations.
|
| We have also known about the specification and frame problems
| for a long time also.
|
| Note that part of the reason for the split between the symbolic
| camp and statistical camp in the 90s was due to more practical
| models being possible with existential quantification.
|
| There have been several papers on HN talking about a shift to
| universal quantification to get around limitations lately.
|
| Unfortunately discussions about the limits of first order logic
| have historical challenges and adding in the limits of
| fragments of first order logic like grounding are compounded
| upon those challenges with cognitive dissonance.
|
| While understanding the abilities of multi level perceptrons is
| challenging, there is a path of realizing the implications of
| an individual perceptron as a choice function that is useful
| for me.
|
| The same limits that have been known for decades still hold in
| the general case for those who can figure a way to control
| their own cognitive dissonance, but they are just lenses.
|
| As an industry we need to find ways to avoid the traps of the
| Brouwer-Hilbert controversy and unsettled questions and opaque
| definitions about the nature of intelligence to fully exploit
| the advantages.
|
| Hopefully experience will tempor the fear and enthusiasm for
| AGI that has made it challenging to discuss the power and
| constraints of ML.
|
| I know that even discussing dropping the a priori assumption of
| LEM with my brother who has a PhD in complex analysis is
| challenging.
|
| But the platonic ideals simply don't hold for non-trivial
| properties, and no matter if we are using ML or BoG Sat, the
| hard problems are too high in the polynomial hierarchy to make
| that assumption.
| gmaster1440 wrote:
| How generalizable are these findings given the rapid pace of AI
| advancement? The paper studies a snapshot in time with current AI
| capabilities, but the relationship between human expertise and AI
| could look very different with more advanced models. I would love
| to have seen the paper:
|
| - Examine how the human-AI relationship evolved as the AI system
| improved during the study period
|
| - Theorize more explicitly about which aspects of human judgment
| might be more vs less persistent
|
| - Consider how their findings might change with more capable AI
| systems
| Animats wrote:
| _" The tool automates a majority of "idea generation" tasks,
| reallocating scientists to the new task of evaluating model-
| suggested candidate compounds. In the absence of AI, researchers
| devote nearly half their time to conceptualizing potential
| materials. This falls to less than 16% after the tool's
| introduction. Meanwhile, time spent assessing candidate materials
| increases by 74%"_
|
| So the AI is in charge, and mostly needs a bunch of lab
| assistants.
|
| _" Machines should think. People should work."_ - not a joke any
| more.
| caycep wrote:
| would there be a difference in accuracy of the statement if you
| replace AI w/ "data science and statistical models"?
___________________________________________________________________
(page generated 2024-11-12 23:00 UTC)