4 ms·

I think the author means a non-ML expert interest in using ML. For example a geologist who wants to apply ANNs her field data

by bagrow 10y ago

AndrewKemendo 10y ago
Well that's my point. A non-ML expert (your geologist) would need to find a SME to apply ML to their problem. The author seems to distinguish between these two roles rather than pushing the non-ML person into becoming a ML SME. Said geologist, would likely run into the problems I describe above if looking for that ML SME.
- mindcrime 10y ago
  I think everybody is kinda talking around each other here. In this context, the geologist is the SME. The "subject matter" is the geological application, not Machine Learning. The whole point of this document is advice for people who are SME's in some domain, but not ML experts, but who are working on applying ML in their domain. This report is targeted to groups who are subject matter experts in their application but deep learning novices. Of course it goes without saying that, in a ideal world, you'd have both domain SME's and ML experts working on the problem together, but given that we don't live in an ideal world, the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning. And for all the stuff we can say about the difficulty of applying DL and the need for experts, the reality is that you don't actually always need a "deep learning expert". Let's say your problem just happens to be similar to, say, handwritten digit recognition. Given that anybody can download DL4J, go through the tutorials, and get a network going in an hour or two that gives something like 98%+ accuracy, there's a good chance that our "geologist who's a DL novice" can create a network that will yield useful results. Maybe getting the last couple of percentage points of accuracy out will require a "real" DL expert, but hell, depending on the scenario, that might not even be needed.
  - AndrewKemendo 10y ago
    I see what you're saying and upon re-reading I think it's ambiguous which SME is being referenced. I guess it depends on who the audience is, and I think we both assume the audience is a non-ML SME. the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning. If that is in fact the case, I would argue that this document is not really giving that user the best starting point - though they get points for trying. As a practitioner perhaps I am biased, and our applications are on the boundaries of solved problems (though include some reasonably solved CV solutions) - however I would argue that unless your application is strictly the simplest and can use off the shelf solutions, for example simply implementing Google Cloud Vision API or the Microsoft Cognitive Computing API - you're going to need someone with years of study/practice with ML to get to a good outcome.
    - zodiac 10y ago
      It is on the face of it a pretty weird phrase ("subject matter"), but I've always seen it used to mean what mindcrime means. The phrase is also used in general non-ML specific programming (eg among a team of people building a mobile app for a bank, the "subject matter expert" might be a non-programmer who has years of experience working for banks). It's also disambiguated by the author saying "subject matter expert for your application" (application being an application of machine learning, eg to radiology, geology...), and saying "subject matter experts in their application but deep learning novices" in the paper's abstract.
    - jph00 10y ago
      I've seen dozens of examples of people with less than a year of ML experience get great outcomes from deep learning. It's no longer true that this is such an exclusive field.

I think the author means a non-ML expert interest in using ML. For example a geologist who wants to apply ANNs her field data

by bagrow 10y ago

I think the author means a non-ML expert interest in using ML. For example a geologist who wants to apply ANNs her field data

AndrewKemendo 10y ago

Well that's my point. A non-ML expert (your geologist) would need to find a SME to apply ML to their problem. The author seems to distinguish between these two roles rather than pushing the non-ML person into becoming a ML SME. Said geologist, would likely run into the problems I describe above if looking for that ML SME.

mindcrime 10y ago
I think everybody is kinda talking around each other here. In this context, the geologist is the SME. The "subject matter" is the geological application, not Machine Learning. The whole point of this document is advice for people who are SME's in some domain, but not ML experts, but who are working on applying ML in their domain. This report is targeted to groups who are subject matter experts in their application but deep learning novices. Of course it goes without saying that, in a ideal world, you'd have both domain SME's and ML experts working on the problem together, but given that we don't live in an ideal world, the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning. And for all the stuff we can say about the difficulty of applying DL and the need for experts, the reality is that you don't actually always need a "deep learning expert". Let's say your problem just happens to be similar to, say, handwritten digit recognition. Given that anybody can download DL4J, go through the tutorials, and get a network going in an hour or two that gives something like 98%+ accuracy, there's a good chance that our "geologist who's a DL novice" can create a network that will yield useful results. Maybe getting the last couple of percentage points of accuracy out will require a "real" DL expert, but hell, depending on the scenario, that might not even be needed.
- AndrewKemendo 10y ago
  I see what you're saying and upon re-reading I think it's ambiguous which SME is being referenced. I guess it depends on who the audience is, and I think we both assume the audience is a non-ML SME. the point here - at least as I read it - is to help the non-ML-expert folks get started applying Deep Learning. If that is in fact the case, I would argue that this document is not really giving that user the best starting point - though they get points for trying. As a practitioner perhaps I am biased, and our applications are on the boundaries of solved problems (though include some reasonably solved CV solutions) - however I would argue that unless your application is strictly the simplest and can use off the shelf solutions, for example simply implementing Google Cloud Vision API or the Microsoft Cognitive Computing API - you're going to need someone with years of study/practice with ML to get to a good outcome.
  - zodiac 10y ago
    It is on the face of it a pretty weird phrase ("subject matter"), but I've always seen it used to mean what mindcrime means. The phrase is also used in general non-ML specific programming (eg among a team of people building a mobile app for a bank, the "subject matter expert" might be a non-programmer who has years of experience working for banks). It's also disambiguated by the author saying "subject matter expert for your application" (application being an application of machine learning, eg to radiology, geology...), and saying "subject matter experts in their application but deep learning novices" in the paper's abstract.
  - jph00 10y ago
    I've seen dozens of examples of people with less than a year of ML experience get great outcomes from deep learning. It's no longer true that this is such an exclusive field.