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> Karla Helena-Bueno discovered a common hibernation factor when she accidentally left an Arctic bacterium on ice for too long. I love how this story follows t

> Karla Helena-Bueno discovered a common hibernation factor when she accidentally left an Arctic bacterium on ice for too long. I love how this story follows the magic pattern of so much of innovation and discovery - an accident. It's refreshingly human and not a mode of discovery that machine learning is going to completely take away from us.

dojomouse 2y ago
I think ML is likely to be material to us making many more such discoveries. So much of the current constraint is not in the knowledge to identify the interesting pattern, but the capacity to look for it at scale.
- grugagag 2y ago
  Yeah but you missed the point op was making
  - markburns 2y ago
    That seems an uncharitable view of the reply. The search space is huge, we sometimes find needles in haystacks by accident, isn’t it exciting that we have tools now that can systematically check every piece of hay?
    - richrichie 2y ago
      ML search is more about ‘averages’ based on samples. Innovations like these are more about ‘shocks’ that surface fitting cannot capture. Note universal approximation theorem applies only to smooth surfaces.
      - tomrod 2y ago
        Not always. Quantile regression exists. And you can develop "no match" categories.
        richrichie 2y ago
        Quantile regression is also about averages.
        tomrod 2y ago
        Averages are formulated as measures of centrality in the L2 norm ("straight line" distance), sum(values) / count(values). Quantile regression uses modifications the L1 norm ("city block" distance); if median (50%) then it is a measure of centrality. Not everything is an average. If you're interested, this is a good (but math heavy) treatment: https://en.wikipedia.org/wiki/Quantile_regression#Computation_of_estimates_for_regression_parameters https://en.wikipedia.org/wiki/Quantile_regression#Computatio...
      - kylehotchkiss 2y ago
        Well said.
      - radarsat1 2y ago
        But the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers.
  - dojomouse 2y ago
    Perhaps. I was thinking along the lines of MarkBurns response - ML will allow us to efficiently look in those places we might otherwise only have searched by accident. If ops point was rather that “accident”/“luck” are uniquely human… I don’t agree. Luck is when probability works out in your favour - and that can happen all the time with any sort of probabilistic search, which is rife in ML.
WalterBright 2y ago
I've been reading "The Making of the Atomic Bomb". But it's really about the process of discovery in nuclear physics. And most of the discoveries were made by accident.
- kylehotchkiss 2y ago
  Stephen B Johnson's "How we got to now" was enlightening on the topic of discovery for me (https://www.pbssocal.org/shows/how-we-got-now https://www.pbssocal.org/shows/how-we-got-now)
- galaxyLogic 2y ago
  But if you don't study the math and physics hard, you will not be able to understand that you may have found something, valuable. It would be like Pearls Before Swine.
COGlory 2y ago
I'm all for it. People get lucky, then try to rationalize the past with a skill narrative. Then they soak up all the grants.
- dghlsakjg 2y ago
  > People get lucky, then try to rationalize the past with a skill narrative. Then they soak up all the grants. They have to put themselves in the situation to get lucky first. This person got a graduate education, and was competent enough to be selected to be doing research in what is likely a multimillion dollar lab owned by an institution, then she had the knowledge and ability to notice and be able to identify what had "accidentally" happened with a micro-organism that we barely understand. Luck was the smallest part of this discovery. I would say that the grant money is well spent funding someone so "lucky".
  - COGlory 2y ago
    Everyone in science works hard. Only a few get lucky. People get scooped every day. Source: spent years looking hard for hibernation promotion factor in P. aeruginosa ribosomes via cryo-EM. Got a PhD and worked a whole lot of 16 hour days. Never got lucky.
    - brg 2y ago
      If this story were at all true, then you know very well that not everyone in science works hard. In my graduate cohort, those who did the sets first year, set themselves into research, and worked hard graduated. Those who did not left with a masters, although many found success in other fields. It was quite clearly delineated.
      - COGlory 2y ago
        I'm talking about at the PI level. And yes of course a few people don't work hard, but the overwhelming majority do not differentiate themselves by how hard they work, is the point I'm trying to make. Your average PI has the skill set to take advantage of getting lucky. Not sure what you're insinuating about the story not being true, would you like to see maps?
Karellen 2y ago
> The most exciting phrase to hear in science, the one that heralds new discoveries, is not "Eureka!" (I found it!) but "That's funny..." -- commonly attributed to Isaac Asimov
- neutronicus 2y ago
  As a one-time scientist, I think Asimov may have been tricked by extreme selection bias on "That's funny..." utterances. It almost always precedes a crushing realization that you have fucked something up and probably wasted a lot of time. You're probably days down exploring that explanation before the eventual "holy shit" (that I never really had the benefit of experiencing).
  - lebean 2y ago
    The most exciting phrase that heralds a new discovery is not "eureka!" but "holy shit!"
    - cardiffspaceman 2y ago
      The legend of “eureka” is that a fellow who had a specific problem in mind noticed something specific to his problem. Discoveries like sticky notes are not the same thing.
      - czl 2y ago
        Yes. The "eureka" legend refers to the ancient Greek scholar Archimedes. He was tasked with determining if a gold crown commissioned by the king was pure gold, or if the goldsmith had substituted some silver. While taking a bath, Archimedes noticed that the water level rose as he got in, and he suddenly realized that the volume of water displaced must be equal to the volume of the submerged object. This gave him the insight that by measuring the volume of water displaced by the crown, and comparing it to the volume displaced by an equal weight of pure gold, he could determine the purity of the crown. Excited by his discovery, Archimedes supposedly leapt out of the bath and ran naked through the streets of Syracuse shouting "Eureka!" which means "I have found it!" in Greek.
micromacrofoot 2y ago
not necessarily, machine learning can make more accidents faster
ASalazarMX 2y ago
Maybe AI won't forget bacteria in the ice, but like us, it is really good at finding patterns, but at a massive scale. Instead of an accident it could find the hibernation mechanism from another angle. And if AGI becomes a thing, it might go "Hey, this is funny" in weird ways after it has ingested enough data. I love the novel Colossus because almost 60 years ago it portayed realistically how a nascent AGI could behave: https://en.wikipedia.org/wiki/Colossus_(novel) https://en.wikipedia.org/wiki/Colossus_(novel)
nico 2y ago
Very similar pattern to the recent stories about grokking (someone leaving a model training for too long by accident, then discovering something unexpected when realizing the accident)
m463 2y ago
I've been using the humble refrigerator/freezer for accidental bacterial science experiments all my life. (I vividly remember as a kid leaving a slice of bread in the refrigerator as a for-credit experiment until it grew interesting green mold to study)

dojomouse 2y ago

I think ML is likely to be material to us making many more such discoveries. So much of the current constraint is not in the knowledge to identify the interesting pattern, but the capacity to look for it at scale.

grugagag 2y ago
Yeah but you missed the point op was making
- markburns 2y ago
  That seems an uncharitable view of the reply. The search space is huge, we sometimes find needles in haystacks by accident, isn’t it exciting that we have tools now that can systematically check every piece of hay?
  - richrichie 2y ago
    ML search is more about ‘averages’ based on samples. Innovations like these are more about ‘shocks’ that surface fitting cannot capture. Note universal approximation theorem applies only to smooth surfaces.
    - tomrod 2y ago
      Not always. Quantile regression exists. And you can develop "no match" categories.
      - richrichie 2y ago
        Quantile regression is also about averages.
        tomrod 2y ago
        Averages are formulated as measures of centrality in the L2 norm ("straight line" distance), sum(values) / count(values). Quantile regression uses modifications the L1 norm ("city block" distance); if median (50%) then it is a measure of centrality. Not everything is an average. If you're interested, this is a good (but math heavy) treatment: https://en.wikipedia.org/wiki/Quantile_regression#Computation_of_estimates_for_regression_parameters https://en.wikipedia.org/wiki/Quantile_regression#Computatio...
    - kylehotchkiss 2y ago
      Well said.
    - radarsat1 2y ago
      But the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers.
- dojomouse 2y ago
  Perhaps. I was thinking along the lines of MarkBurns response - ML will allow us to efficiently look in those places we might otherwise only have searched by accident. If ops point was rather that “accident”/“luck” are uniquely human… I don’t agree. Luck is when probability works out in your favour - and that can happen all the time with any sort of probabilistic search, which is rife in ML.

WalterBright 2y ago

I've been reading "The Making of the Atomic Bomb". But it's really about the process of discovery in nuclear physics. And most of the discoveries were made by accident.

kylehotchkiss 2y ago
Stephen B Johnson's "How we got to now" was enlightening on the topic of discovery for me (https://www.pbssocal.org/shows/how-we-got-now https://www.pbssocal.org/shows/how-we-got-now)
galaxyLogic 2y ago
But if you don't study the math and physics hard, you will not be able to understand that you may have found something, valuable. It would be like Pearls Before Swine.

COGlory 2y ago

I'm all for it. People get lucky, then try to rationalize the past with a skill narrative. Then they soak up all the grants.

dghlsakjg 2y ago
> People get lucky, then try to rationalize the past with a skill narrative. Then they soak up all the grants. They have to put themselves in the situation to get lucky first. This person got a graduate education, and was competent enough to be selected to be doing research in what is likely a multimillion dollar lab owned by an institution, then she had the knowledge and ability to notice and be able to identify what had "accidentally" happened with a micro-organism that we barely understand. Luck was the smallest part of this discovery. I would say that the grant money is well spent funding someone so "lucky".
- COGlory 2y ago
  Everyone in science works hard. Only a few get lucky. People get scooped every day. Source: spent years looking hard for hibernation promotion factor in P. aeruginosa ribosomes via cryo-EM. Got a PhD and worked a whole lot of 16 hour days. Never got lucky.
  - brg 2y ago
    If this story were at all true, then you know very well that not everyone in science works hard. In my graduate cohort, those who did the sets first year, set themselves into research, and worked hard graduated. Those who did not left with a masters, although many found success in other fields. It was quite clearly delineated.
    - COGlory 2y ago
      I'm talking about at the PI level. And yes of course a few people don't work hard, but the overwhelming majority do not differentiate themselves by how hard they work, is the point I'm trying to make. Your average PI has the skill set to take advantage of getting lucky. Not sure what you're insinuating about the story not being true, would you like to see maps?

Karellen 2y ago

> The most exciting phrase to hear in science, the one that heralds new discoveries, is not "Eureka!" (I found it!) but "That's funny..." -- commonly attributed to Isaac Asimov

neutronicus 2y ago
As a one-time scientist, I think Asimov may have been tricked by extreme selection bias on "That's funny..." utterances. It almost always precedes a crushing realization that you have fucked something up and probably wasted a lot of time. You're probably days down exploring that explanation before the eventual "holy shit" (that I never really had the benefit of experiencing).
- lebean 2y ago
  The most exciting phrase that heralds a new discovery is not "eureka!" but "holy shit!"
  - cardiffspaceman 2y ago
    The legend of “eureka” is that a fellow who had a specific problem in mind noticed something specific to his problem. Discoveries like sticky notes are not the same thing.
    - czl 2y ago
      Yes. The "eureka" legend refers to the ancient Greek scholar Archimedes. He was tasked with determining if a gold crown commissioned by the king was pure gold, or if the goldsmith had substituted some silver. While taking a bath, Archimedes noticed that the water level rose as he got in, and he suddenly realized that the volume of water displaced must be equal to the volume of the submerged object. This gave him the insight that by measuring the volume of water displaced by the crown, and comparing it to the volume displaced by an equal weight of pure gold, he could determine the purity of the crown. Excited by his discovery, Archimedes supposedly leapt out of the bath and ran naked through the streets of Syracuse shouting "Eureka!" which means "I have found it!" in Greek.

micromacrofoot 2y ago

not necessarily, machine learning can make more accidents faster

ASalazarMX 2y ago

Maybe AI won't forget bacteria in the ice, but like us, it is really good at finding patterns, but at a massive scale. Instead of an accident it could find the hibernation mechanism from another angle. And if AGI becomes a thing, it might go "Hey, this is funny" in weird ways after it has ingested enough data. I love the novel Colossus because almost 60 years ago it portayed realistically how a nascent AGI could behave: https://en.wikipedia.org/wiki/Colossus_(novel) https://en.wikipedia.org/wiki/Colossus_(novel)

nico 2y ago

Very similar pattern to the recent stories about grokking (someone leaving a model training for too long by accident, then discovering something unexpected when realizing the accident)

m463 2y ago

I've been using the humble refrigerator/freezer for accidental bacterial science experiments all my life. (I vividly remember as a kid leaving a slice of bread in the refrigerator as a for-credit experiment until it grew interesting green mold to study)