91–100 of 106 posts
I work in a company where ML has made a considerable difference to our bottom line (search component of an e-commerce site). When I joined the company, search was so bad, it was easier to just use google and include 'inurl:' to actually find products on the site. Now, years later, the builtin search actually gives you what you're looking for better than google does. (This is important because if you can't find someth…
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The irony is that there are techniques for dealing with noisy/mislabeled/bad data (e.g. gold loss correction [0], errors-in-variables models [1]), but that stuff isn't "sexy" and not enough practitioners know about it. 0: https://arxiv.org/abs/1802.05300 1: https://en.m.wikipedia.org/wiki/Errors-in-variables_models
Is this any more than fancy outlier detection (genuinely asking, this is not my field). i.e. if the majority of data fed to a system is bad, will it work?
Outlier detection detects data points that look different from your existing data in some way; that "lie outside" what is usual. Sometimes the assumption is that outliers are generated by an entirely different process from the rest of the data. It's important not to conflate "outliers" with legitimate data points that happened to fall at the tail ends of the data distribution.
The techniques I listed attempt to actually compensate for some known or estimable level of badness in the data. For example, gold loss correction (GLC) essentially estimates the probability of misclassifying a data point, and uses that to adjust the output of the model.
I work in a company where ML has made a considerable difference to our bottom line (search component of an e-commerce site). When I joined the company, search was so bad, it was easier to just use google and include 'inurl:' to actually find products on the site. Now, years later, the builtin search actually gives you what you're looking for better than google does. (This is important because if you can't find someth…
Does ML really work for you here vs elastic search or other full text search?
It's a structural issue caused by the way wealth creation works for majority of people in tech. Job hopping, trendy frameworks in CV, "high-impact" projects done ASAP, etc. No one wants to do boring, slow pace work with lots of planning, reflection and introspection. And why would they do it? These kind of jobs are usually worst paid. We, the practitioners, have every economic incentive to go the other route. The pro…
I saw a large organization which was the epitome of this -- Executive Directors would propose ambitious ML projects, Directors would create plans and teams, Managers would execute on budgets, create more detailed plans, and then...someone actually needed to do the work. Because of the length of the effort, the annual compensation would already have been handed out and the EDs, Directors, Managers had already "extract…
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Maybe people using Google should start to apply some common sense and not believe everything at face value. Nevertheless, the examples you cite are extremes that affect only few people. So you would rather have no internet search engines at all, so that those problems could be avoided? Isn't that a bit like saying cars are crap because people die in accidents? Maybe there are just upsides and downsides to most new te…
“Google should not use badly trained beta ML to guess which person in the world with this name is a serial killer” is not “there should be no search engines.” Google was very successful with the latter for a long time before they started in on the former.
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So why not go back to some old version of it? I don't think "memory consumption" is necessarily a good indicator, because sometimes using more memory is a sign of good optimization. Also how is the memory consumption if you turn off all modern features?
> So why not go back to some old version of it? Because the old version doesn't work due to DRM/it depending on a remote API version that's no longer available/it's just flat out unavailable/etc... > Also how is the memory consumption if you turn off all modern features? It's cute you think you /can/ turn off the modern features in a lot of today's garbage.
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I saw a large organization which was the epitome of this -- Executive Directors would propose ambitious ML projects, Directors would create plans and teams, Managers would execute on budgets, create more detailed plans, and then...someone actually needed to do the work. Because of the length of the effort, the annual compensation would already have been handed out and the EDs, Directors, Managers had already "extract…
That sounds truly awful. Not necessarily surprising —- but could you give us some clues as to which company this was so that we can avoid working there?
It's a term we often hear, that implies there is "good" and "bad" data.
A dataset can have errors in labeling, be very small, be unbalanced, but all that can be managed with the proper methods.
THE biggest problem is when you training data does not correspond to the production use-case.
It's not that the dataset is "bad", it's just that the problem you're solving with your ML algorithm trained on that data does not correspond to the problem you're trying to solve.
The most "perfect" ML algorithm trained on the most "perfect" dataset for self-driving cars for example (for detection, segmentation of objects or whatever) made the US will have problems when the cars drive in an other country. Your MNIST-trained NN will have problems in a country where numbers are written slightly differently. Some people will put pictures of cats in your car model classification software. Pictures taken on a smartphone by your users will be different than your dataset scrapped on the web.
There is no bad data, just badly used data. And most of the work (and the most interesting part IMO) in ML is to identify, quantify and neutralize biases in models and differences between the data you have and the data the production system will work with.
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I saw a large organization which was the epitome of this -- Executive Directors would propose ambitious ML projects, Directors would create plans and teams, Managers would execute on budgets, create more detailed plans, and then...someone actually needed to do the work. Because of the length of the effort, the annual compensation would already have been handed out and the EDs, Directors, Managers had already "extract…
That sounds truly awful. Not necessarily surprising —- but could you give us some clues as to which company this was so that we can avoid working there?
Was even in one "hype" startup that began this way almost immediately.