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Sort of a content-free article, but the headline is an interesting bold claim that conjures a lot of thoughts: - I know enough machine learning to be dangerous

Sort of a content-free article, but the headline is an interesting bold claim that conjures a lot of thoughts: - I know enough machine learning to be dangerous, but I'm hardly ever asked to use it. I designed a Bayesian classifier for my own startup around 6 years ago, analyzing political donor networks. I've completed the Stanford ML course. Back in college I did a math minor, so I'm comfortable with linear algebra, calculus, etc. I'm pretty comfortable with statistics of both kinds. But my bread-and-butter is freelance web development . . . and I'm not really even sure how to find work doing more MLy things. - I've read over and over that the most time-consuming part of ML work is data collection & cleanup, and that matches my own experience. It is the same thing that killed so many data warehouse projects in the 90s. You don't need a Ph.D. to do it, but it is a tough and costly prerequisite. So it seems like you'll need non-ML programmers even for specifically ML projects. - In a similar vein, Google has written about the challenges of "operationalizing" machine learning projects.[1] Having a little experience collaborating with a team doing an ML project, where they did the ML engine and I did the user-facing application, I can say that many ML experts are not experts in building reliable, production-ready software. - Will there ever be a Wordpress of machine learning? If there is, the author will be rich, but you won't need a Ph.D. to operate it. But because ML requires hooks into your existing systems, I don't know if this will ever happen. What will happen I think is plugins to existing e-commerce systems for product recommendation or other off-the-shelf ML-powered features. These already exist, but I assume they will become more prevalent and powerful over time. In any case, the mainstreaming of ML for business will be inversely correlated with the expense to implement it, which suggests it will be easier and easier for non-expert developers to use (and misuse). EDIT: Added the (now-)third bullet point I forgot before. [1] https://research.google.com/pubs/pub43146.html https://research.google.com/pubs/pub43146.html

vidarh 10y ago
> and I'm not really even sure how to find work doing more MLy things. I think this is key - prospective clients won't ask for it because they don't understand where it could be used, and they won't understand the heavy ML methods. An approach there would be to pitch things like improving search results using a bayesian classifier applied to analytics data as a cheap upgrade when quoting other work. Until people are used to even the basic statistical approaches they won't be ready to invest in something more drastic.
sdenton4 10y ago
Re, Off the shelf: I saw a nice presentation a while back which asserted that you get a certain boost to your solution just throwing scikit.learn at the problem. After that, you spend time/money on either better, customized ml, or on feature engineering. Today, you very quickly get to a place where feature engineering is the better place to put effort. So there's some off the shelf ml, which is pretty great, but the problem of automatic feature selection - especially if new features need to be plumbed into a system - is still insufficiently solved. We've got systems for paring down the set of available features, but our systems have no idea how to ask for new features. That's essentially domain research, after all, and probably a 'strong ai-complete' problem.
Bartweiss 10y ago
> I've read over and over that the most time-consuming part of ML work is data collection & cleanup, and that matches my own experience. It is the same thing that killed so many data warehouse projects in the 90s. You don't need a Ph.D. to do it, but it is a tough and costly prerequisite. So it seems like you'll need non-ML programmers even for specifically ML projects. Yeah, this is a very good point. I trained a simple RNN to play games a while back, and the "RNN" part was something anyone with a decent math background could self-teach pretty quickly. There are lots of tricks and speedups and complications I could have added, but the baseline product was pretty easy. The "trained" part, though. That took huge effort, and research, and tinkering. Building up a proper dataset with usable input features was 90%+ of my effort. Most of it was too specific to standardize in any useful way, so working with an off-the-shelf system for the learner wouldn't have saved any decent amount of effort.
cr0sh 10y ago
> Will there ever be a Wordpress of machine learning? It certainly isn't at the same level, but Keras (imho) comes real close to being such a thing. Perhaps it, coupled with something like an expanded version of the TensorFlow playground, might make a power but easy-to-approach tool. That said, even with Keras you still need an understanding of what the parts are for and how to "hook them up".

vidarh 10y ago

> and I'm not really even sure how to find work doing more MLy things. I think this is key - prospective clients won't ask for it because they don't understand where it could be used, and they won't understand the heavy ML methods. An approach there would be to pitch things like improving search results using a bayesian classifier applied to analytics data as a cheap upgrade when quoting other work. Until people are used to even the basic statistical approaches they won't be ready to invest in something more drastic.

sdenton4 10y ago

Re, Off the shelf: I saw a nice presentation a while back which asserted that you get a certain boost to your solution just throwing scikit.learn at the problem. After that, you spend time/money on either better, customized ml, or on feature engineering. Today, you very quickly get to a place where feature engineering is the better place to put effort. So there's some off the shelf ml, which is pretty great, but the problem of automatic feature selection - especially if new features need to be plumbed into a system - is still insufficiently solved. We've got systems for paring down the set of available features, but our systems have no idea how to ask for new features. That's essentially domain research, after all, and probably a 'strong ai-complete' problem.

Bartweiss 10y ago

> I've read over and over that the most time-consuming part of ML work is data collection & cleanup, and that matches my own experience. It is the same thing that killed so many data warehouse projects in the 90s. You don't need a Ph.D. to do it, but it is a tough and costly prerequisite. So it seems like you'll need non-ML programmers even for specifically ML projects. Yeah, this is a very good point. I trained a simple RNN to play games a while back, and the "RNN" part was something anyone with a decent math background could self-teach pretty quickly. There are lots of tricks and speedups and complications I could have added, but the baseline product was pretty easy. The "trained" part, though. That took huge effort, and research, and tinkering. Building up a proper dataset with usable input features was 90%+ of my effort. Most of it was too specific to standardize in any useful way, so working with an off-the-shelf system for the learner wouldn't have saved any decent amount of effort.

cr0sh 10y ago

> Will there ever be a Wordpress of machine learning? It certainly isn't at the same level, but Keras (imho) comes real close to being such a thing. Perhaps it, coupled with something like an expanded version of the TensorFlow playground, might make a power but easy-to-approach tool. That said, even with Keras you still need an understanding of what the parts are for and how to "hook them up".