Comment by mm321 | Hacker News Reader

Comment by mm321 | Hacker News Reader

Forget about self driving cars - the real killer application of deep learning is mass surveillance - there are big customer for that (advertising, policing, political technology - we better get used to the term) and its the only technique that can get the job done.

I sometimes think that there really was no AI winter as we got other technologies that implemented the ideas: SQL Databases can be seen as an application of many ideas in classical AI - for example its a declarative language for defining relations among tables; you can have rules in the form of SQL stored procedures; actually it was a big break (paradigm shift is the term) in how you deal with data - the database engine has to do some real behind the scenes optimization work in order to get a workable representation of the data definition (that is certainly bordering on classical AI in complexity).

these boring CRUD applications are light years ahead in how data was handled back in the beginning.

The BBC recently requested information about the use of facial recognition from UK police forces. Those that use facial recognition reported false positive rates of >95%. That led some to abandon the systems, others just use it as one form of pre-screening. Mass surveillance with facial recognition is nowhere near levels where it can be used unsupervised. And that's even before people actively try to deceive it.

For advertising, I'm also not sure if there's been a lot of progress. Maybe it's because I opted out too much but I have the feeling that ad targeting hasn't become more intelligent, rather the opposite. It's been a long time that I've been surprised at the accuracy of a model tracking me. Sure, targeted ads for political purposes can work very well but are nothing new and don't need any deep learning nor any other "new" technologies.

Where I really see progress is data visualisation. Often dismissed it can be surprisingly hard to get right and tools around that (esp for enterprise use) have developed a lot over recent years. And that's what companies need. No one's looking for a black-box algorithm to replace marketing, they just want to make some sense of their data and understand what's going on.

> Those that use facial recognition reported false positive rates of >95%.... Mass surveillance with facial recognition is nowhere near levels where it can be used unsupervised.

Is this deep neural networks with the latest technologies?

While yes, deep learning isn't going to solve everything, we'll probably see significant changes in the products available as this technology discovered the past few years makes into the real world.

Most scanners that do OCR and most forms of facial recognition isn't using deep neural networks with transfer learning, YET.

This is not to say that discoveries will continue, winter is probably coming :)

Aha, yeah I saw this in the news - pretty classic use of people horribly misunderstanding statistics and/or misrepresenting the facts. Let's say one person out of 60 million has sauron's ring. I use my DeepMagicNet to classify everyone, and get 100 positive results. Only one is the ringbearer, so I have a 99% error rate. Best abandon ship.

I do think of myself that I can read statistics. They didn't mention it as a false positive rate, that's what I interpreted. I don't have the article here but they said that the system gave out ~250 alerts, of which ~230 turned out to be incorrect. It didn't specify at all how big the database of potential suspects was. The number of scanned faces was ~50-100k each time (stadium). Nevertheless, the 230/250 is the issue here, simply because it destroys trust in the system. If the operator already knows that the chance of this being a false alarm is ~5%, will they really follow up all alerts?

95% false positive rate is extremely good for surveillance, as the cost of false positive is low (wasted efforts by the police). That means for every 20 people the police investigate, one is a target.

They didn't mention arrests but I think the database used was much larger. Probably all persons of interest. The Met (London Police) has been using experts in face recognition for years and their hit rate is much higher. Those people can also identify a high number of suspects on random CCTV while the software was used in stadiums where people tend to remain at one place for most of the time.

So 95% false positive can be better than nothing but it's nowhere near what trained policemen can do.

I disagree. Even if there are a number of big customers for mass surveillance, self driving cars fundamentally changes the platform of our economy for everyone.

That sounds like US-centric PoV. For large portion of (most?) Europeans self-driving won't change much in their lives, definitely not anything major.

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