5 ms·

I'm curious what people find is more likely given this evidence: - 100s of models were done incorrectly, and none even remotely correctly - there is no phenom

I'm curious what people find is more likely given this evidence: - 100s of models were done incorrectly, and none even remotely correctly - there is no phenomenon to correctly model Both seem extremely unlikely. Not sure what the alternatives are.

tablespoon 5y ago
That's a false choice. There's also: - Technology not suitable or adequate for this use case. I mean, we've been to the "AI over-promises and under-delivers" rodeo before.
- briefcomment 5y ago
  You think a 100% failure rate on the hottest topic in the world right now is likely?
  - tablespoon 5y ago
    > You think a 100% failure rate on the hottest topic in the world right now is likely? Yeah. Attempts at powered flight had a 100% failure rate in the 1800s. It's kind of absurd to think that "AI" (especially in its current incarnation) must be able to solve any "hot" problem that it's thrown at.
    - briefcomment 5y ago
      You think AI is so primitive that we can't make any headway into the biggest problem we currently have? That seems like a fairly fringe view. Especially on a problem that has well defined data like medical imaging.
      - gumby 5y ago
        Medical diagnosis has been one of the core ai domains domains since the early 1960s so people have been breaking the picks at that coalface for almost 60 years. And those older systems has more intelligence in them. Todays’s “AI” is a small number of tricks aimed at large amounts of data, with an unprecedented and enormous amount of marketing added.
        iamstupidsimple 5y ago
        > And those older systems has more intelligence in them. Todays’s “AI” is a small number of tricks aimed at large amounts of data, with an unprecedented and enormous amount of marketing added. If you're talking about deep neural networks, I can understand this viewpoint. But generally the last decade has proven widely successful for high quality vision recognition models that just weren't available before then. And with something like transfer learning, you can take a powerful off-the-shelf model and specialize it without a huge dataset. The real barriers IME are around legal and privacy implications. There's also a strong argument about if these models creating enough value in the first place, but they can work on a technical level.
      - boxercommemt 5y ago
        Hey alexa win the war on drugs.
      - tablespoon 5y ago
        > You think AI is so primitive that we can't make any headway into the biggest problem we currently have? More or less. It's almost certain if "AI" makes any headway at all, it will fall far short of the hype. > That seems like a fairly fringe view. Especially on a problem that has well defined data like medical imaging. I doubt it.
  - RandomLensman 5y ago
    Oh yeah. Just as AI has not made the stock market predictable. There is (maybe) progress in pockets but not in general. Systems that change strongly and react to forecasts are really difficult to handle (and also stuff happens, eg new variants). Also on the wet side, there is very little progress on something like general antiviral drugs. Some stuff is just really hard.
  - deleted 5y ago
    [deleted]
throwaway2048 5y ago
100s of models that all make the same wrong assumptions are going to be equally worthless. You can make all the models about phlogiston you want, none of them are going to accurately explain fire.
- briefcomment 5y ago
  Right, so is phlogiston to fire as our current understanding of what's happening is to reality?
new299 5y ago
My guess would be that it's hard to get a clean well labeled dataset where case and control datasets are otherwise similar. Particularly, in a pandemic the people doing the data collection are otherwise occupied. Another problem you have is when you do it right the results look worse than a biased dataset. So there's some incentive to continue working with a biased dataset.
webel0 5y ago
100s of DL groups with little previous experience in this area set out to train similar models at the outset of the pandemic. Many rushed out models. I’m not surprised that they ran into similar pitfalls.
roenxi 5y ago
> there is no phenomenon to correctly model COVID exists, it causes physical changes and affects the world outside the host. 100% it can be modelled somehow in theory. That being said, there are harsh rules against collecting data (for example, it would be extremely illegal to create a sample set by taking a random group of people and infecting half of them with COVID on purpose). So I expect actually getting training data is a huge hill to climb. If most of the models used the same training data I assume there is a reason like that. So in practice, I don't expect a model quickly.
- Barrin92 5y ago
  >100% it can be modelled somehow in theory. The entire premise of modelling something rests on the fact that the behaviour of what you're trying to model in the future depends on its behaviour in the past. This is not true for all systems, it's not even true for most systems. John Kay differentiates between 'resolvable uncertainty' and 'radical uncertainty'. Modelling a volcano eruption is resolvable because it's a reasonably predictable system and you can make confident stochastic claims. Complex human systems are chaotic, have 'unknown unknowns', are simply subject to chance or non-linear behaviour that completely throws any prediction off. You cannot model something that is subject to forces you cannot even know at the point of making the model, and this is true for any complex human system pretty much. Modelling Covid as if we have access to Hari Seldon's psycho-history was always a terrible idea.
  - pas 5y ago
    What? We have to model human health. Find good proxies for it (non invasive scans, bloodwork, PCR, rapid antigen test, whole genome sequencing, anamnesis [medical history]) and there's ample to predict. Of course there are better and worse signals to use.
    - Barrin92 5y ago
      >We have to model human health no, the correct behavior under uncertainty isn't modelling, it's something akin to Taleb's notion of antifragility or robustness. The correct response to a pandemic isn't some sort of Hari Seldon psycho-history which, as the article points out, is futile. The right response is building systems so responsive they can crush a pandemic before it gets to that stage.
jltsiren 5y ago
As someone with a background in CS (but not AI/ML) and working in bioinformatics: People with a background in methods like solving problems that are easy to solve with the methods they know. Those are rarely the problems people in biology/medicine want to solve. Some common pitfalls include: * Asking the right questions. * Framing the questions correctly. * Finding computational problems that capture the essence of the questions and that can be solved efficiently. * Interpreting the results. * Avoiding systemic biases in rare but important edge cases. Finding the right problems to solve takes time, and the first attempts will probably fail.
- briefcomment 5y ago
  > first attempts will probably fail First hundreds of attempts? In your experience, is that a reasonable number?
  - jltsiren 5y ago
    The first attempts made by any particular person are likely to fail. And when many people approach similar problems from similar perspectives, they are likely to make similar mistakes. Give them a few years, and someone will probably figure out what the mistakes were and how to avoid them.
    - beerandt 5y ago
      Right- thousands of people making the same predictable learning curve mistakes isn't thousands of man-hours of new research. It's thousands of (wo)men all duplicating the same few hours of research, which isn't likely to result in any breakthrough. If 10 people hike to Mt Everest's base camp, it's not the equivalent of one person climbing ~10 times further to the peak.
  - deleted 5y ago
    [deleted]
  - neolog 5y ago
    What field do you come from where people succeed so often at cutting-edge research?
LeetHacks 5y ago
Very early on in the pandemic one research group opensourced "COVID-Net", using the described flawed dataset. Given the fact that at that time there was still quite a lot of "if we all help we can beat the pandemic" positiveness going around. An easily run-able piece of small code that promised "results" was available and the fact that a lot of organizations wanted positive PR, you get these insultingly low quality "research" practices. I looked at a lot of the initial papers and models and most were forks of COVID-Net or used an already established model like ImageNet and retrained it with the bad dataset. Presto! Paper out PR happy. Very few questioned the dataset or the approach in general. But they loved bragging about it on LinkedIn.
toast0 5y ago
There's almost certainly something to model, you don't hear stories about hospitals filling up with patients and nobody knows if they're covid patients or how to deal with them; or at least, not anymore. 100s of wrong models doesn't sound extremely unlikely to me. It's really easy to train a model and test that it works on some dataset and then find it doesn't work on a different one. Setting up the inputs for training is difficult because of data privacy, there's intense pressure to publish, models have no expectation of explanability, lots of reasons for a wrong model that looks promising. I've worked adjacent to people trying to solve problems with machine learning and it's rough going. When things work, ok nice, but when they don't, it's much harder to tweak than something based on heuristics (which, of course, don't always apply either).

tablespoon 5y ago

That's a false choice. There's also: - Technology not suitable or adequate for this use case. I mean, we've been to the "AI over-promises and under-delivers" rodeo before.

briefcomment 5y ago
You think a 100% failure rate on the hottest topic in the world right now is likely?
- tablespoon 5y ago
  > You think a 100% failure rate on the hottest topic in the world right now is likely? Yeah. Attempts at powered flight had a 100% failure rate in the 1800s. It's kind of absurd to think that "AI" (especially in its current incarnation) must be able to solve any "hot" problem that it's thrown at.
  - briefcomment 5y ago
    You think AI is so primitive that we can't make any headway into the biggest problem we currently have? That seems like a fairly fringe view. Especially on a problem that has well defined data like medical imaging.
    - gumby 5y ago
      Medical diagnosis has been one of the core ai domains domains since the early 1960s so people have been breaking the picks at that coalface for almost 60 years. And those older systems has more intelligence in them. Todays’s “AI” is a small number of tricks aimed at large amounts of data, with an unprecedented and enormous amount of marketing added.
      - iamstupidsimple 5y ago
        > And those older systems has more intelligence in them. Todays’s “AI” is a small number of tricks aimed at large amounts of data, with an unprecedented and enormous amount of marketing added. If you're talking about deep neural networks, I can understand this viewpoint. But generally the last decade has proven widely successful for high quality vision recognition models that just weren't available before then. And with something like transfer learning, you can take a powerful off-the-shelf model and specialize it without a huge dataset. The real barriers IME are around legal and privacy implications. There's also a strong argument about if these models creating enough value in the first place, but they can work on a technical level.
    - boxercommemt 5y ago
      Hey alexa win the war on drugs.
    - tablespoon 5y ago
      > You think AI is so primitive that we can't make any headway into the biggest problem we currently have? More or less. It's almost certain if "AI" makes any headway at all, it will fall far short of the hype. > That seems like a fairly fringe view. Especially on a problem that has well defined data like medical imaging. I doubt it.
- RandomLensman 5y ago
  Oh yeah. Just as AI has not made the stock market predictable. There is (maybe) progress in pockets but not in general. Systems that change strongly and react to forecasts are really difficult to handle (and also stuff happens, eg new variants). Also on the wet side, there is very little progress on something like general antiviral drugs. Some stuff is just really hard.
- deleted 5y ago
  [deleted]

throwaway2048 5y ago

100s of models that all make the same wrong assumptions are going to be equally worthless. You can make all the models about phlogiston you want, none of them are going to accurately explain fire.

briefcomment 5y ago
Right, so is phlogiston to fire as our current understanding of what's happening is to reality?

new299 5y ago

My guess would be that it's hard to get a clean well labeled dataset where case and control datasets are otherwise similar. Particularly, in a pandemic the people doing the data collection are otherwise occupied. Another problem you have is when you do it right the results look worse than a biased dataset. So there's some incentive to continue working with a biased dataset.

webel0 5y ago

100s of DL groups with little previous experience in this area set out to train similar models at the outset of the pandemic. Many rushed out models. I’m not surprised that they ran into similar pitfalls.

roenxi 5y ago

> there is no phenomenon to correctly model COVID exists, it causes physical changes and affects the world outside the host. 100% it can be modelled somehow in theory. That being said, there are harsh rules against collecting data (for example, it would be extremely illegal to create a sample set by taking a random group of people and infecting half of them with COVID on purpose). So I expect actually getting training data is a huge hill to climb. If most of the models used the same training data I assume there is a reason like that. So in practice, I don't expect a model quickly.

Barrin92 5y ago
>100% it can be modelled somehow in theory. The entire premise of modelling something rests on the fact that the behaviour of what you're trying to model in the future depends on its behaviour in the past. This is not true for all systems, it's not even true for most systems. John Kay differentiates between 'resolvable uncertainty' and 'radical uncertainty'. Modelling a volcano eruption is resolvable because it's a reasonably predictable system and you can make confident stochastic claims. Complex human systems are chaotic, have 'unknown unknowns', are simply subject to chance or non-linear behaviour that completely throws any prediction off. You cannot model something that is subject to forces you cannot even know at the point of making the model, and this is true for any complex human system pretty much. Modelling Covid as if we have access to Hari Seldon's psycho-history was always a terrible idea.
- pas 5y ago
  What? We have to model human health. Find good proxies for it (non invasive scans, bloodwork, PCR, rapid antigen test, whole genome sequencing, anamnesis [medical history]) and there's ample to predict. Of course there are better and worse signals to use.
  - Barrin92 5y ago
    >We have to model human health no, the correct behavior under uncertainty isn't modelling, it's something akin to Taleb's notion of antifragility or robustness. The correct response to a pandemic isn't some sort of Hari Seldon psycho-history which, as the article points out, is futile. The right response is building systems so responsive they can crush a pandemic before it gets to that stage.

jltsiren 5y ago

As someone with a background in CS (but not AI/ML) and working in bioinformatics: People with a background in methods like solving problems that are easy to solve with the methods they know. Those are rarely the problems people in biology/medicine want to solve. Some common pitfalls include: * Asking the right questions. * Framing the questions correctly. * Finding computational problems that capture the essence of the questions and that can be solved efficiently. * Interpreting the results. * Avoiding systemic biases in rare but important edge cases. Finding the right problems to solve takes time, and the first attempts will probably fail.

briefcomment 5y ago
> first attempts will probably fail First hundreds of attempts? In your experience, is that a reasonable number?
- jltsiren 5y ago
  The first attempts made by any particular person are likely to fail. And when many people approach similar problems from similar perspectives, they are likely to make similar mistakes. Give them a few years, and someone will probably figure out what the mistakes were and how to avoid them.
  - beerandt 5y ago
    Right- thousands of people making the same predictable learning curve mistakes isn't thousands of man-hours of new research. It's thousands of (wo)men all duplicating the same few hours of research, which isn't likely to result in any breakthrough. If 10 people hike to Mt Everest's base camp, it's not the equivalent of one person climbing ~10 times further to the peak.
- deleted 5y ago
  [deleted]
- neolog 5y ago
  What field do you come from where people succeed so often at cutting-edge research?

LeetHacks 5y ago

Very early on in the pandemic one research group opensourced "COVID-Net", using the described flawed dataset. Given the fact that at that time there was still quite a lot of "if we all help we can beat the pandemic" positiveness going around. An easily run-able piece of small code that promised "results" was available and the fact that a lot of organizations wanted positive PR, you get these insultingly low quality "research" practices. I looked at a lot of the initial papers and models and most were forks of COVID-Net or used an already established model like ImageNet and retrained it with the bad dataset. Presto! Paper out PR happy. Very few questioned the dataset or the approach in general. But they loved bragging about it on LinkedIn.

toast0 5y ago

There's almost certainly something to model, you don't hear stories about hospitals filling up with patients and nobody knows if they're covid patients or how to deal with them; or at least, not anymore. 100s of wrong models doesn't sound extremely unlikely to me. It's really easy to train a model and test that it works on some dataset and then find it doesn't work on a different one. Setting up the inputs for training is difficult because of data privacy, there's intense pressure to publish, models have no expectation of explanability, lots of reasons for a wrong model that looks promising. I've worked adjacent to people trying to solve problems with machine learning and it's rough going. When things work, ok nice, but when they don't, it's much harder to tweak than something based on heuristics (which, of course, don't always apply either).