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These sorts of posts are just so ridiculously off base. People are absolutely desperate to show that ML isn’t the answer to everything, which nobody in ML clai

by laingc 8y ago

These sorts of posts are just so ridiculously off base. People are absolutely desperate to show that ML isn’t the answer to everything, which nobody in ML claims it is. But it is the very clear answer to some things, and problems like license plate detection fall so unambiguously into that category that, quite frankly, if you think otherwise you’ve really disqualified yourself from the set of people who ought to be asked about these things. In my team, we don’t use deep learning for every problem. We don’t even use machine learning for every problem. I’m a Mathematician, and frankly I’m just not that immature. The fact is, ML is a great solution to a lot of problems, and Deep Learning is a particularly good solution to an ever-increasing category of problems. If you can’t tell the difference between such cases, then you should probably not conclude that everyone is doing it wrong, and instead conclude that you have more to learn.

AstralStorm 8y ago
A particularly bad solution that requires data of still huge amount and still has terrible failure modes. It is not yet ready, not until we can reason about those failure modes better than using highest end statistics.
- 0db532a0 8y ago
  I would be interested (serious) to hear what GP has to say about this in light of his own experience.
  - laingc 8y ago
    > A particularly bad solution You’re free to have this opinion. I don’t see how it could possibly be justified. > that requires data of still huge amount This is true for some problem spaces, but not true in general. If your exposure to Deep Learning is relatively casual, then I can see why you would think this. So while it’s not a totally unfair criticism, if you’re in a problem space wth lots of data, and you have a method that performs well under those circumstances, then you’ll have to do more work to convince me it’s a bad idea to use it. > and still has terrible failure modes. It is not yet ready, not until we can reason about those failure modes better than using highest end statistics. This just feels like parroting others’ criticisms. Yes, our primary method for understanding failure modes of stochastic function approximators are statistical, the same as they are for stochastic processes. Statistics is precisely what is used to rigorously describe behaviour in the aggregate that cannot be well explained in the particulate. It also ignores the fact that there is a huge amount of theory that is currently being developed around deep learning. You won’t see it linked on HN, and you likely won’t find many in the typical software engineering crowd who know about it (which is fine! - software engineers are highly skilled specialists, who should not be expected to closely follow the mathematical literature), but it does exist, and several of my friends who remained in acadaemia are building careers on developing it. As a general aside, I have to say that the glibness of the responses objecting to my comment really does speak volumes.
    - supermatt 8y ago
      look how far down your original comment is ranked. that’s what speaks volumes. i’m calling you out on your BS. your arrogance is unfounded.
supermatt 8y ago
On the contrary, people are absolutely desperate to show that ML isn't the ONLY answer to some things. This article shows how by following a number of logical steps (and explaining how the algorithms work), you can achieve numberplate recognition. These techniques have been used for DECADES. They work on machines your ML model likely wont fit on. In some cases they are faster. In some cases they are slower. In some cases they are more accurate, and in some cases they are not. If you think ML is the magic bullet to any task you've really disqualified yourself from the set of people who ought to be asked about these things.
- laingc 8y ago
  > which nobody in ML claims it is. > We don’t even use machine learning for every problem. Since your comment is in obvious bad faith, this is the only response you’ll get from me.
  - supermatt 8y ago
    you were claiming that number plate recognition is a ML task and if we don’t agree with you we don’t know what we are talking about. seems like you are the one commenting in bad faith. if you reread, you will see i used your own words, which may have confused you into thinking mine was the bad faith comment. good day.

by laingc 8y ago

AstralStorm 8y ago

A particularly bad solution that requires data of still huge amount and still has terrible failure modes. It is not yet ready, not until we can reason about those failure modes better than using highest end statistics.

0db532a0 8y ago
I would be interested (serious) to hear what GP has to say about this in light of his own experience.
- laingc 8y ago
  > A particularly bad solution You’re free to have this opinion. I don’t see how it could possibly be justified. > that requires data of still huge amount This is true for some problem spaces, but not true in general. If your exposure to Deep Learning is relatively casual, then I can see why you would think this. So while it’s not a totally unfair criticism, if you’re in a problem space wth lots of data, and you have a method that performs well under those circumstances, then you’ll have to do more work to convince me it’s a bad idea to use it. > and still has terrible failure modes. It is not yet ready, not until we can reason about those failure modes better than using highest end statistics. This just feels like parroting others’ criticisms. Yes, our primary method for understanding failure modes of stochastic function approximators are statistical, the same as they are for stochastic processes. Statistics is precisely what is used to rigorously describe behaviour in the aggregate that cannot be well explained in the particulate. It also ignores the fact that there is a huge amount of theory that is currently being developed around deep learning. You won’t see it linked on HN, and you likely won’t find many in the typical software engineering crowd who know about it (which is fine! - software engineers are highly skilled specialists, who should not be expected to closely follow the mathematical literature), but it does exist, and several of my friends who remained in acadaemia are building careers on developing it. As a general aside, I have to say that the glibness of the responses objecting to my comment really does speak volumes.
  - supermatt 8y ago
    look how far down your original comment is ranked. that’s what speaks volumes. i’m calling you out on your BS. your arrogance is unfounded.

supermatt 8y ago

On the contrary, people are absolutely desperate to show that ML isn't the ONLY answer to some things. This article shows how by following a number of logical steps (and explaining how the algorithms work), you can achieve numberplate recognition. These techniques have been used for DECADES. They work on machines your ML model likely wont fit on. In some cases they are faster. In some cases they are slower. In some cases they are more accurate, and in some cases they are not. If you think ML is the magic bullet to any task you've really disqualified yourself from the set of people who ought to be asked about these things.

laingc 8y ago
> which nobody in ML claims it is. > We don’t even use machine learning for every problem. Since your comment is in obvious bad faith, this is the only response you’ll get from me.
- supermatt 8y ago
  you were claiming that number plate recognition is a ML task and if we don’t agree with you we don’t know what we are talking about. seems like you are the one commenting in bad faith. if you reread, you will see i used your own words, which may have confused you into thinking mine was the bad faith comment. good day.