I think I read about three articles like this per day.
Still haven't seen anyone actually using machine learning in any practical application yet, anywhere. Lots of neat toy demos, no real work seems to be getting done.
Still haven't seen anyone actually using machine learning in any practical application yet, anywhere. Lots of neat toy demos, no real work seems to be getting done.
While currently more academic, it also has tremendous promise in healthcare diagnostic tasks [5,6]. It has also been used in economics applications such as measuring the global impact of fishing [7] and in poverty prediction in third world countries [8].
[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.26....
[2] https://www.infoworld.com/article/2907877/how-paypal-reduces...
[3] https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering
[4] https://en.wikipedia.org/wiki/Netflix_Prize
[5] https://jamanetwork.com/journals/jama/fullarticle/2588763
[6] https://www.nature.com/articles/nature21056
It is not just practical, it is a huge industry.
Speech recognition on my phone hasn't improved much since Dragon in the 90s. I'd argue the tech today is worse because it never works without a network connection, so it basically never works unless you're in a big city.
Google translate is a running joke. I don't think that I'm allowed to comment further on Google's use of machine learning without violating NDAs, but manually fiddling with weights until you get the magic number you want is not machine learning.
Again, not really seeing any benefit. Lots of hype though.
The main businesses which seems to truly depend on ML (other than maybe FICO) is that of tech journalist and PR dweeb.
Just off the top of my head, 3D/HD mapping companies like HERE rely on DL-based image recognition to recognize objects at reasonable levels of accuracy -- totally infeasible to do manually at scale.
Also, one of the triggers of the renaissance of NNs is that it was shown to outperform traditional computer vision techniques around the early 2010s. USPS does handwriting recognition every single day -- maybe not with DL -- but definitely with some ML algorithm (I was at a talk given by one of the originators of said algorithm).
ML is more than just DL. If take the definition of ML encompass to statistical learning -- which it traditionally does -- production ML deployments is extremely pervasive, from industries as varied as finance to chemical manufacturing.
The exact FICO algorithm is proprietary, but algorithms of its ilk are pervasive. As you know, at least two other companies (Transunion and Experian) also have their own algorithms. And credit scoring algorithms are among the least sophisticated ML deployments.
I'm not able to talk about the ML models I work on, but they're in production and the business relies on them.
I guess I'm not seeing the finer points of the argument -- as it stands without further qualification or refinement, it does not seem to be a valid conclusion.
I'll say it again: no company depends on machine learning, other than, maybe Fair Isaac. Many use such things. They don't depend on ML. IMO the trend is in the other direction; many companies will begin to realize what they've spent on models isn't worth the returns.
For example, you don’t see too many of the most successful or long-term quant finance products touting machine learning or AI. They do use these techniques (sometimes directly for trading, sometimes only for auxiliary problems), but don’t make a big deal out of it.
I worked previously in both adtech & quant finance, and have seen products where ML is the absolute core of the whole thing, yet it wasn’t marketed that way (even recently). I’ve also seen products fail to gain market traction due to overly aggressive ML/AI branding that customers (mostly other enterprise business marketing teams) found alienating and overly complex.
https://www.computerworld.com/article/3321138/did-ibm-put-to...
IBM is going around selling this to corporations and agencies as a solution to pretty much everything that involves data analysis.
(And yes I know reality isn't a game with a small finite move set, simple deterministic rules, and perfect information.)
Most of the algo traders on the stock market are running ML now, and that's the quintessential results oriented environment
Internet ads are auctioned off in realtime to ad buyers who use ML to value impressions based on user profile data, demographics etc.
Anything involving prediction or classification is likely to already have ML solutions in development or production.
I work on big databases with gazillions of rows that need to be classified etc and hand-made rules-based approaches win every time.
And I still read this stuff on HN.