131–140 of 200 posts
At Persyst we use neural networks for EEG interpretation. Our latest version has human-level performance for epileptogenic spike detection. We are now working on bringing the seizure detection algorithm to human-level performance.
Earlier quoted context omitted.
So this is a prototype and not really added value yet.
I don't understand why this comment is unpopular since the GP is phrased in such a fashion that you only notice that they talking hypothetically if you read it carefully. I don't think there's anything wrong the GP's achievement or post (it's all interesting stuff) but if something has not yet been implemented, it's worth nothing since there is "many a slip 'tween cup and the lip"
https://skillsmatter.com/skillscasts/9105-detecting-antisoci...
(We're still beginners as will be apparent from the video but it's proving useful so far. I should note, we do have 'proper' data scientists too, but they are mostly working on audience analysis/personalisation).
FinTech: Credit risk modeling. Spend prediction. Loss prediction. Fraud and AML detection. Intrusion detection. Email routing. Bandit testing. Optimizing planning/ task scheduling. Customer segmentation. Face- and document detection. Search/analytics. Chat bots. Sentiment analysis. Topic analysis. Churn detection.
I can imagine that fin tech will love it. Everything will go wrong one day in the future and no one will know the reason.
I think a lot of the real benefits from ML "at work" is more in just cleaning of data and running through the gauntlet of simplest regressions (before jumping onto something more magical whose outputs and decision making process you can't exactly explain to someone). I would classify something like this blog post as ML, would you? http://stackoverflow.blog/2016/11/How-Do-Developers-in-New-Y...
When people talk about the growth (or sometimes 'excess') of ML solutions these days, I always wonder about this. A basic linear regression probably isn't ML, a backprop neural net clearly is, but somewhere between the two is a very fuzzy line between "statistics and data cleaning" and "actually machine learning". I think a lot of people have just pushed the ML angle of an already-reasonable approach to tie into that…
Off topic: somehow the people not doing data cleaning because it is so boring and ML techniques do not need clean data end up with the worst overfitting I have ever seen.
We use ML to model complex interactions in electrical grids in order to make decisions that improve grid efficiency, which has been (at least in the short term) more effective than using an optimizer and trying to iterate on problem specification to get better results. Generally speaking, I think if you know your data relationships you don't need ML. If you don't, it can be especially useful.
Earlier quoted context omitted.
So is this like the Amazon "feature" where I buy a coffee table on Amazon, then I get suggested to buy a coffee table EVERY DAY for 3 months. Literally row after row of coffee table? Because there must be a big pool of people who buy 1 coffee table buying more coffee tables immediately after?
It's a hard problem to determine the repeat purchase cadence of a product. At one end of the bell curve you have items re-purchased frequently, e.g. diapers or grocery, and on the other end you have items that are rarely repurchased. I haven't looked at coffee tables specifically, but I know when I've looked at home products in the past I've been surprised at how frequently people will buy two large items, e.g. TVs o…
My dream is that I go to Amazon.com and there are a ton of different unrelated products that people who purchase similar things as me buy. So if I only buy "buy it for life" kitchen equipment, it doesn't show me the most popular but crappy version of something, it shows me the one that I'd actually purchase.
Such an easy problem with suuuuuch a difficult solution though. Not to mention the obvious privacy concerns there.
Oh well, I know that they have good people working on the solution, and no chance I could do it better :p
(www.queckt.com is anyone's interested)
Without AI/ML, we wouldn't have a product.
We're a computer vision company, we do a lot of product detection + recognition + search, primarily for retailers, but we've also got revenue in other verticals with large volumes of imagery. My co-founder and I both did our thesis' on computer vision. In our space, the recent AI / ML advances have made things possible that were simply not realistic before. That being said, the hype around Deep Learning is getting pr…