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But aren't all science basically like this? If you know your hypothsis works before you do the experiments, it's not science anymore.

nerdponx 3y ago
Yes, and science is "hard" compared to software development in a lot of ways. Less certainty of success and poorly defined success criteria.
bugglebeetle 3y ago
Not really. It’s easy to tell relative to existing methods whether the size of data will solve the problem. For example, if you’re trying to solve a classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work.
- krisoft 3y ago
  > classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work That is true. On the other hand i have seen someone once perform a trick which looked miraculous to me. We had a classification problem with a small number of labels (~3). And one of the labels had unfortunately way less samples in our training set. Then someone trained a GAN to turn the images of the abundant labels into images of the rare labels. We added those syntetically generated images to the training set and it improved our classification performance as best as we could tell. That one still feels a bit like black magic to me to be honest. Almost as if we got more out of less with a trick.
- DeathArrow 3y ago
  >but only have a small amount of training data for some (or all) of them, it will probably never work. Transfer learning might help in some cases.
dylan604 3y ago
Sometimes something you've done works, but you really don't know why/how. You then have to walk it back to figure out by experimenting what is causing it to work. I feel like this happen(ed|s) in chemistry a lot. Was it the fact that I stirred it counter clockwise this time, or that I got distracted and the temp went 5° hotter than intended, or that I didn't quite clean my beaker properly and some residue contaminated this batch, or any number of other steps.
eru 3y ago
You are right. That's why you want to avoid doing science, when you can. Ideally, you want to be solving engineering problems instead.
- hgomersall 3y ago
  You being? We really need people to try to solve problems that might not work because that is how new technology develops.
  - immibis 3y ago
    Let's rephrase: If you have to solve a problem, you'd better hope that problem is an engineering problem rather than a science problem.
    - hgomersall 3y ago
      Even many engineering problems are too difficult to know a priori that they will work. No new knowledge is needed as such, just the bounds on what is possible might be fuzzy.
      - eru 3y ago
        Yes, of course. If you care about solving your specific problem, you want to avoid having to acquire new knowledge. That's risky. (And I don't just mean knowledge that's new to you, that you get from reading a book. I mean knowledge that's new to humanity as far as you can tell.) Eventually, someone will have to acquire some new knowledge to drive humanity forward. Just like in war, someone will have to go and fight the opponent; but you still prefer to achieve your objectives without having to fight whenever possible.

nerdponx 3y ago

Yes, and science is "hard" compared to software development in a lot of ways. Less certainty of success and poorly defined success criteria.

bugglebeetle 3y ago

Not really. It’s easy to tell relative to existing methods whether the size of data will solve the problem. For example, if you’re trying to solve a classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work.

krisoft 3y ago
> classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work That is true. On the other hand i have seen someone once perform a trick which looked miraculous to me. We had a classification problem with a small number of labels (~3). And one of the labels had unfortunately way less samples in our training set. Then someone trained a GAN to turn the images of the abundant labels into images of the rare labels. We added those syntetically generated images to the training set and it improved our classification performance as best as we could tell. That one still feels a bit like black magic to me to be honest. Almost as if we got more out of less with a trick.
DeathArrow 3y ago
>but only have a small amount of training data for some (or all) of them, it will probably never work. Transfer learning might help in some cases.

dylan604 3y ago

Sometimes something you've done works, but you really don't know why/how. You then have to walk it back to figure out by experimenting what is causing it to work. I feel like this happen(ed|s) in chemistry a lot. Was it the fact that I stirred it counter clockwise this time, or that I got distracted and the temp went 5° hotter than intended, or that I didn't quite clean my beaker properly and some residue contaminated this batch, or any number of other steps.

eru 3y ago

You are right. That's why you want to avoid doing science, when you can. Ideally, you want to be solving engineering problems instead.

hgomersall 3y ago
You being? We really need people to try to solve problems that might not work because that is how new technology develops.
- immibis 3y ago
  Let's rephrase: If you have to solve a problem, you'd better hope that problem is an engineering problem rather than a science problem.
  - hgomersall 3y ago
    Even many engineering problems are too difficult to know a priori that they will work. No new knowledge is needed as such, just the bounds on what is possible might be fuzzy.
    - eru 3y ago
      Yes, of course. If you care about solving your specific problem, you want to avoid having to acquire new knowledge. That's risky. (And I don't just mean knowledge that's new to you, that you get from reading a book. I mean knowledge that's new to humanity as far as you can tell.) Eventually, someone will have to acquire some new knowledge to drive humanity forward. Just like in war, someone will have to go and fight the opponent; but you still prefer to achieve your objectives without having to fight whenever possible.