7 ms·

Graph-based AI model maps the future of innovation

92 points by laurex 2y ago

deleted 2y ago
[deleted]
drawnwren 2y ago
Is it just me or does this read like complete word soup? > The application could lead to the development of innovative sustainable building materials, biodegradable alternatives to plastics, wearable technology, and even biomedical devices. That a transform from materials to a 19th century Russian painter somehow is applicable to what just so happens to be the zeitgeist of materials science beggars belief.
gran_colombia 2y ago
> One comparison revealed detailed structural parallels between biological materials and Beethoven’s 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. This is not serious.
- gran_colombia 2y ago
  > The resulting material integrates an innovative set of concepts that include a balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. The article itself seems generated.
  - uoaei 2y ago
    I encounter this take more and more, where jargony sciencey language is dismissed as "generated". We forget that actual people do write like this, and self-satisfied researchers especially so. More likely, this author read a bit too much Deleuze and is echoing that language to make the discovery feel more important than incidental.
    - equestria 2y ago
      Paste it into any AI detector (e.g., https://quillbot.com/ai-content-detector https://quillbot.com/ai-content-detector). They're not perfect, but they're pretty good in the aggregate. This text is almost certainly generated by an LLM.
      - sdesol 2y ago
        I ran this across my AI spelling and Grammar checker at https://app.gitsense.com/?doc=4715cf6d95689&other-models=Claude+3.5+Sonnet%2CGPT-4o%2CLlama+3.1+70B&highlight=conflicts&samples=3 https://app.gitsense.com/?doc=4715cf6d95689&other-models=Cla... Note, sentences highlighted in yellow means one or more models disagree. The sentence that makes me think this might not be AI generated is "Researchers can use this framework to answer complex questions, find gaps in current knowledge, suggest new designs for materials, and predict how materials might behave, and link concepts that had never been connected before." The use of "and" before "predict how materials" was obviously unnecessary and got caught by both gpt-4o and claude 3.5 sonnet and when I questioned Llama 3.5 about it, it also agreed. For AI generated, it seems like there are too many imperfections, which makes me believe it might well be written by a human.
        observationist 2y ago
        It's definitely a run on academic writing that didn't get enough editing. It's consistently bad in ways LLMs typically correct for. Run your papers through AI and have them identify simple corrections. It's like having an endlessly patient English Literature major at your beck and call.
        sdesol 2y ago
        I messed up and didn't really analyze the paper but the article. Since I can't edit, here is the introduction to the paper: https://app.gitsense.com/?doc=696357b733b https://app.gitsense.com/?doc=696357b733b which contains enough grammatical errors that I'm pretty sure it was not generated by AI.
        equestria 2y ago
abeppu 2y ago
Skimming the actual paper ... it seems pretty bad? The thing about Beethoven's 9th and biological materials which is mentioned in the OP is just that, out of a very large knowledge graph, they found small subgraph isomorphic to a subgraph created from a text about the symphony. But they seem not to cover the fact that a sufficiently large graph with some high-level statistical properties would have small subgraphs isomorphic to a 'query' graph. Is this one good or meaningful in some way, or is it just an inevitable outcome of having produced such a large knowledge graph at the start? The reader can't really tell, because figure 8 which presents the two graphs has such a poor resolution that one cannot read any of the labels. We're just expected to see "oh the nodes and their degrees match so it has the right shape", but that doesn't really tell us that their system had any insight through this isomorphism-based mining process. For the stuff about linking art (e.g. a Kandinsky painting) with material design ... they used an LLM to generate a description of a material for DALL-E where the prompt includes information about the painting, and then they show the resulting image and the painting. But there's no measure of what a "good" material description is, and there certainly is no evaluation of the contribution of the graph-based "reasoning". In particular an obvious comparison would be to "Describe this painting." -> "Construct a prompt for DALL-E to portray a material whose structure has properties informed by this description of a painting ..." -> render. It really seems like the author threw a bunch of stuff against the wall and didn't even look particularly closely to see if it stuck. Also, the only equation in the paper is the author giving the definition of cosine similarity, before 2 paragraphs justifying its use in constructing their graph. Like, who is the intended audience? https://iopscience.iop.org/article/10.1088/2632-2153/ad7228#mlstad7228t6 https://iopscience.iop.org/article/10.1088/2632-2153/ad7228#...
- refulgentis 2y ago
  Thank you for taking the time to read and write this up, something was "off" in the quotes describing the materials that had me at 4 of 5 alarm bells ringing. Now I can super skim confidently and giggle. - real output here is text, using a finetuned Mixtral provided leading Qs - the initial "graph" with the silly beethoven-inspired material is probably hand constructed, they don't describe its creation process at all - later, they're constructing graphs with GPT-3.5 (!?) (they say rate limits, but somethings weird with the whole thing, they're talking about GPT-4 vision preview etc., which was roughly a year before the paper was released) - Whole thing reads like someone had a long leash to spend a year or two exploring basic consumer LLMs, finetune one LLM, and sorta just published whatever they got 6 months to a year later.
dacox 2y ago
...k
CatWChainsaw 2y ago
"The Future of Innovation" sounds exactly like freshly squeezed GPT drivel I'd expect to read from a vapid "hustler" on LinkedIn.
quataran 2y ago
Wow, what's happened to MIT?
- bbor 2y ago
  Well... Markus J. Buehler is the McAfee Professor of Engineering and former Head of the MIT Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology. He directs the Laboratory for Atomistic and Molecular Mechanics (LAMM), leads the MIT-Germany program, and is Principal Investigator on numerous national and international research program... [he] is a founder of the emerging research area of materiomics. He has appeared on numerous TV and radio outlets to explain the impact of his research to broad audiences. I think this guy's just playing political/journalistic games with his research, and tailoring it for impact rather than rigor. I'm not sure I endorse it necessarily, but I don't think we should write this off as "dumb article from MIT", but rather "the explorations of a media-savvy department head". That doesn't excuse the occasional overselling of results of course, as that's dangerous to science no matter the motivation.
youoy 2y ago
I think this article marks the "peak of inflated expectations" of AI for HN posts.
quantadev 2y ago
Since all humans alive today have undergone the sum total of all human evolution, and are the ultimate creation of millions of years of evolution, it makes sense that the kinds of things we find "artistically pleasing" (both visually and thru sound) could have many patterns that apply to reality in deeper ways than any of us know, and so letting AI use art as it's inspiration for using those patterns in it's search for knew knowledge seems like a good idea. Also there are also certain aspects of physical geometric relationships and even sound relationships that would not be able to be conveyed to an AI by any other means than thru art and music. So definitely using art to inspire science is a good approach. Even the great Physicists throughout history have often appreciated how there is indeed beauty in the mathematical symmetries and relationships exhibited in the mathematics of nature, and so there is definitely a connection even if not quite tangible nor describable by man.
331c8c71 2y ago
Can we call this "Deep Trolling"?
- kubb 2y ago
  The very fact that some people are trying to take this seriously is probably the point he’s trying to make.
woozyolliew 2y ago
One to save for April 1st
fudged71 2y ago
Oh I'm glad that I'm not the only one who has gotten lost in the sauce by asking LLMs to recursively synthesize from data towards some grand insights--we want to see results when there is none apparent. What you end up getting is some bizarre theories overfit on the data with zero causal relationships. LLMs are fundamentally pattern matching systems and they will find "connections" between any two domains if prompted. It just reeks of confirmation bias; researchers looking for connections between art and science will find them. The simpler explanation makes more sense: knowledge graphs naturally show certain structural properties, and these properties appear across domains due to basic mathematical constraints, common organizational principles, and human cognitive patterns reflected in data. Sure, LLMs trained on human knowledge can identify these patterns, generate plausible narratives, and create appealing connections - but this doesn't necessarily indicate novel scientific insights, predictive power, or practical utility. If you find yourself going down a rabbit hole like this (and trust me, we've all been there), my advice is to ask "is there a simpler explanation that I'm missing?" Then start from square one: specific testable hypotheses, rigorous controls, clear success metrics, practical demonstrations, and independent validation. And maybe add a "complexity budget" - if your explanation requires three layers of recursive AI analysis to make sense, you're probably way too deep in the sauce.
nnurmanov 2y ago
Since the article mentions graphs, I’d like to ask what would be the advantages of graph databases over relational? Graph databases have become popular in RAG related topics, maybe mainly GraphRag related work by MS. So I wonder if the same accuracy with RAG could be achieved by traditional databases. Or if graph databases are an absolute must, then what are their limitations? Are there any successful production usage cases of graph databases?
IAmGraydon 2y ago
We should probably flag this article out of existence as it's pure garbage. Quite strange its getting enough upvotes to stay on the front page, but literally zero positive comments. The OP has an interesting history of posting lots of low quality articles.
- CatWChainsaw 2y ago
  Maybe SEO/Amazon-esque shadow work is keeping it on the front page.
pmfgpmfg 2y ago
[dead]
byyoung3 2y ago
did it actually make a novel material that is plausibly useful?
__reset__ 2y ago
The author removed bridge construction from the civil engineering curriculum at MIT when he was heading the department (competitive steel bridge building is a big thing between CE departments in the US). He said they were producing too many engineers and not enough scholars. When alumni offered to endow the program (in case it was a funding issue) he refused our donations. Which makes this “scholarship” of chaining together some GPT prompts especially insulting.

92 points by laurex 2y ago

deleted 2y ago

[deleted]

Is it just me or does this read like complete word soup? > The application could lead to the development of innovative sustainable building materials, biodegradable alternatives to plastics, wearable technology, and even biomedical devices. That a transform from materials to a 19th century Russian painter somehow is applicable to what just so happens to be the zeitgeist of materials science beggars belief.

gran_colombia 2y ago

> One comparison revealed detailed structural parallels between biological materials and Beethoven’s 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. This is not serious.

gran_colombia 2y ago
> The resulting material integrates an innovative set of concepts that include a balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. The article itself seems generated.
- uoaei 2y ago
  I encounter this take more and more, where jargony sciencey language is dismissed as "generated". We forget that actual people do write like this, and self-satisfied researchers especially so. More likely, this author read a bit too much Deleuze and is echoing that language to make the discovery feel more important than incidental.
  - equestria 2y ago
    Paste it into any AI detector (e.g., https://quillbot.com/ai-content-detector https://quillbot.com/ai-content-detector). They're not perfect, but they're pretty good in the aggregate. This text is almost certainly generated by an LLM.
    - sdesol 2y ago
      I ran this across my AI spelling and Grammar checker at https://app.gitsense.com/?doc=4715cf6d95689&other-models=Claude+3.5+Sonnet%2CGPT-4o%2CLlama+3.1+70B&highlight=conflicts&samples=3 https://app.gitsense.com/?doc=4715cf6d95689&other-models=Cla... Note, sentences highlighted in yellow means one or more models disagree. The sentence that makes me think this might not be AI generated is "Researchers can use this framework to answer complex questions, find gaps in current knowledge, suggest new designs for materials, and predict how materials might behave, and link concepts that had never been connected before." The use of "and" before "predict how materials" was obviously unnecessary and got caught by both gpt-4o and claude 3.5 sonnet and when I questioned Llama 3.5 about it, it also agreed. For AI generated, it seems like there are too many imperfections, which makes me believe it might well be written by a human.
      - observationist 2y ago
        It's definitely a run on academic writing that didn't get enough editing. It's consistently bad in ways LLMs typically correct for. Run your papers through AI and have them identify simple corrections. It's like having an endlessly patient English Literature major at your beck and call.
      - sdesol 2y ago
        I messed up and didn't really analyze the paper but the article. Since I can't edit, here is the introduction to the paper: https://app.gitsense.com/?doc=696357b733b https://app.gitsense.com/?doc=696357b733b which contains enough grammatical errors that I'm pretty sure it was not generated by AI.
      - equestria 2y ago

abeppu 2y ago

Skimming the actual paper ... it seems pretty bad? The thing about Beethoven's 9th and biological materials which is mentioned in the OP is just that, out of a very large knowledge graph, they found small subgraph isomorphic to a subgraph created from a text about the symphony. But they seem not to cover the fact that a sufficiently large graph with some high-level statistical properties would have small subgraphs isomorphic to a 'query' graph. Is this one good or meaningful in some way, or is it just an inevitable outcome of having produced such a large knowledge graph at the start? The reader can't really tell, because figure 8 which presents the two graphs has such a poor resolution that one cannot read any of the labels. We're just expected to see "oh the nodes and their degrees match so it has the right shape", but that doesn't really tell us that their system had any insight through this isomorphism-based mining process. For the stuff about linking art (e.g. a Kandinsky painting) with material design ... they used an LLM to generate a description of a material for DALL-E where the prompt includes information about the painting, and then they show the resulting image and the painting. But there's no measure of what a "good" material description is, and there certainly is no evaluation of the contribution of the graph-based "reasoning". In particular an obvious comparison would be to "Describe this painting." -> "Construct a prompt for DALL-E to portray a material whose structure has properties informed by this description of a painting ..." -> render. It really seems like the author threw a bunch of stuff against the wall and didn't even look particularly closely to see if it stuck. Also, the only equation in the paper is the author giving the definition of cosine similarity, before 2 paragraphs justifying its use in constructing their graph. Like, who is the intended audience? https://iopscience.iop.org/article/10.1088/2632-2153/ad7228#mlstad7228t6 https://iopscience.iop.org/article/10.1088/2632-2153/ad7228#...

refulgentis 2y ago
Thank you for taking the time to read and write this up, something was "off" in the quotes describing the materials that had me at 4 of 5 alarm bells ringing. Now I can super skim confidently and giggle. - real output here is text, using a finetuned Mixtral provided leading Qs - the initial "graph" with the silly beethoven-inspired material is probably hand constructed, they don't describe its creation process at all - later, they're constructing graphs with GPT-3.5 (!?) (they say rate limits, but somethings weird with the whole thing, they're talking about GPT-4 vision preview etc., which was roughly a year before the paper was released) - Whole thing reads like someone had a long leash to spend a year or two exploring basic consumer LLMs, finetune one LLM, and sorta just published whatever they got 6 months to a year later.

dacox 2y ago

...k

CatWChainsaw 2y ago

"The Future of Innovation" sounds exactly like freshly squeezed GPT drivel I'd expect to read from a vapid "hustler" on LinkedIn.

quataran 2y ago

Wow, what's happened to MIT?

bbor 2y ago
Well... Markus J. Buehler is the McAfee Professor of Engineering and former Head of the MIT Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology. He directs the Laboratory for Atomistic and Molecular Mechanics (LAMM), leads the MIT-Germany program, and is Principal Investigator on numerous national and international research program... [he] is a founder of the emerging research area of materiomics. He has appeared on numerous TV and radio outlets to explain the impact of his research to broad audiences. I think this guy's just playing political/journalistic games with his research, and tailoring it for impact rather than rigor. I'm not sure I endorse it necessarily, but I don't think we should write this off as "dumb article from MIT", but rather "the explorations of a media-savvy department head". That doesn't excuse the occasional overselling of results of course, as that's dangerous to science no matter the motivation.

youoy 2y ago

I think this article marks the "peak of inflated expectations" of AI for HN posts.

quantadev 2y ago

Since all humans alive today have undergone the sum total of all human evolution, and are the ultimate creation of millions of years of evolution, it makes sense that the kinds of things we find "artistically pleasing" (both visually and thru sound) could have many patterns that apply to reality in deeper ways than any of us know, and so letting AI use art as it's inspiration for using those patterns in it's search for knew knowledge seems like a good idea. Also there are also certain aspects of physical geometric relationships and even sound relationships that would not be able to be conveyed to an AI by any other means than thru art and music. So definitely using art to inspire science is a good approach. Even the great Physicists throughout history have often appreciated how there is indeed beauty in the mathematical symmetries and relationships exhibited in the mathematics of nature, and so there is definitely a connection even if not quite tangible nor describable by man.

331c8c71 2y ago

Can we call this "Deep Trolling"?

kubb 2y ago
The very fact that some people are trying to take this seriously is probably the point he’s trying to make.

woozyolliew 2y ago

One to save for April 1st

fudged71 2y ago

Oh I'm glad that I'm not the only one who has gotten lost in the sauce by asking LLMs to recursively synthesize from data towards some grand insights--we want to see results when there is none apparent. What you end up getting is some bizarre theories overfit on the data with zero causal relationships. LLMs are fundamentally pattern matching systems and they will find "connections" between any two domains if prompted. It just reeks of confirmation bias; researchers looking for connections between art and science will find them. The simpler explanation makes more sense: knowledge graphs naturally show certain structural properties, and these properties appear across domains due to basic mathematical constraints, common organizational principles, and human cognitive patterns reflected in data. Sure, LLMs trained on human knowledge can identify these patterns, generate plausible narratives, and create appealing connections - but this doesn't necessarily indicate novel scientific insights, predictive power, or practical utility. If you find yourself going down a rabbit hole like this (and trust me, we've all been there), my advice is to ask "is there a simpler explanation that I'm missing?" Then start from square one: specific testable hypotheses, rigorous controls, clear success metrics, practical demonstrations, and independent validation. And maybe add a "complexity budget" - if your explanation requires three layers of recursive AI analysis to make sense, you're probably way too deep in the sauce.

nnurmanov 2y ago

Since the article mentions graphs, I’d like to ask what would be the advantages of graph databases over relational? Graph databases have become popular in RAG related topics, maybe mainly GraphRag related work by MS. So I wonder if the same accuracy with RAG could be achieved by traditional databases. Or if graph databases are an absolute must, then what are their limitations? Are there any successful production usage cases of graph databases?

IAmGraydon 2y ago

We should probably flag this article out of existence as it's pure garbage. Quite strange its getting enough upvotes to stay on the front page, but literally zero positive comments. The OP has an interesting history of posting lots of low quality articles.

CatWChainsaw 2y ago
Maybe SEO/Amazon-esque shadow work is keeping it on the front page.

pmfgpmfg 2y ago

[dead]

byyoung3 2y ago

did it actually make a novel material that is plausibly useful?

__reset__ 2y ago

The author removed bridge construction from the civil engineering curriculum at MIT when he was heading the department (competitive steel bridge building is a big thing between CE departments in the US). He said they were producing too many engineers and not enough scholars. When alumni offered to endow the program (in case it was a funding issue) he refused our donations. Which makes this “scholarship” of chaining together some GPT prompts especially insulting.