A Year in Computer Vision
themtank.org
themtank.org
I can come up with handful of completely malevolent uses for facial recognition in no time flat. Automated weapons, increased surveillance, targeted marketing.
The only good use I can come up with is spotting terrorist. But they are already the one group of people who seem to have the luxury of occasionally using masks.
Involvement in this stuff could very well yield more problems with your personal conscience than what Mikhail Kalashnikov has ever had. "Someone will do it anyhow" Yes. But you can drive up the price and availability of malevolence by personally avoiding it.
I can come up with a handful of benevolent uses for facial recognition in no time flat.
1.) Automated ID systems for supermarkets. Walk into a store, store knows who you are. No checkout needed. Just walk out.
2.) Security for venues/work/etc. No more backstage passes getting copied - computer vision knows who's allowed where.
3.) Emergency response after a disaster. Automatically notify loved ones if you've been detected alive in a news feed.
4.) Voting systems. Got a stadium full of people? Just have them raise their hands - computer vision does a tally along with ID'ing who voted for what.
5.) Guest lists / ticketing. Show up to a venue, walk in. No ticket needed. Venue knows who's if you're allowed in. No more lost tickets.
6.) FaceID for the iphoneX. I'm personally loving it on mine.
Like many new technologies, it can be used for both good and bad. Harnessing nuclear reactions can be used to generate massive amounts of electricity, or to level cities. What's important is that it's regulated, not that it's banned.
Of course, nobody will ever use this feature as a mean of enhancing their "targeted marketing" nor creating a "targeted pricing" strategy. For me, this is a big NO-NO and should go straight to the malevolent usage list.
But if there's only one walk-out supermarket available, and it does price discriminate, and it's not too outrageous, I'm in. Waiting in line for checkout is very bothersome for me, and I like my job very much, so I'll pay more than my hourly rate at work to get out of each hour of line.
(extra bothersome if there's untargeted marketing blaring through some speakers while I wait, 1984-style. But I already avoid those supermarkets)
Nobody likes price discrimination, but there are advantages. Poor people pay much, much less. It's ironic that we're always coming up with inefficient measures full of collateral damage to ease thing up on the poor, but we'll rail against price discrimination like it's Salem.
And from the point of view of the customer, it would be the most convenient thing ever.
That seems to blatantly exploitative on so many levels, with so much potential for abuse, that I have to assume someone will implement it within a decade. Probably Amazon.
All of the rest could have been done decade ago if people would simply accept RFID implant. And most people are very skeptical of that, because in almost all of your examples the tech could be used for evil too.
Would you accept free RFID implant? If the only price was that the RFID fingerprint would be completely public.
I worry that, as we already see with NSA, GHCQ and friends, governments won't be quick to regulate a huge source of information that they can tap, and a source of technology that they can use for themselves. Recent history would seem to indicate that governments see the potential for state use of these techniques, but overlook the much wider scope for criminal uses of them. (E.g: Reliable facial recognition and pervasive tracking seems like it would make undercover police work much harder.)
Having a stockpile of nuclear weapons feels like an innately dangerous thing, whereas having a computer system tracking everyone for auguries of pre-crime is light-hearted enough that it's the central plot premise of a major TV show. Many politicians are who are rightly wary of the destructive power of nuclear technology may not see the same dangers in computer vision or pervasive surveillance.
Sorry kinda OT from malovent/benevolent route, but is FaceID fast? Or rather, is it just as fast as the normal way of unlocking with finger?
Do you have to tilt your phone to your face or is there some kinda wide angle lens? I frequently unlock my phone laying flat on my desk
7) FaceID for guns.
Imho, it should be mandatory, and it could be a solution in the gun control discussion.
Police would probably hate it, because having your side arm malfunction in dark alley in life and death situation is probably something you want to avoid.
Organized crime would probably love it. Manufacture of unmarked firearms is not that tricky. Fresh market for illegal goods is suddenly created, as now it's not only the criminals who want these, but also survivalists and all kinds of paranoid people. If you run the numbers of murders with illegal guns/ amount of illegal guns vs. murders with legal guns / amount legal guns, you will find that illegal firearms are roughly 10X or 20X more likely to be used in homicide.
Regarding the other points (companies and organized crime loving this), you can also look at other countries where guns are illegal. Practice shows there are less fatal incidents there.
The only thing that facial recognition would help in my country would be those cases when licensed gun collector gets robbed and his guns taken by organized criminals. In that situation it would only slow down the illegal use of those guns. Therefore it would possibly ramp the price up to a point where manufacture of illegal guns becomes profitable.
Problems this would solve e.g.:
- Children using their parents' gun at schools
- People using multiple guns (e.g. Las Vegas shooting)
Children using their parents guns is pretty neatly tackled here in my country by mandatory gun safes.
A person at the shooting ground unlocks your guns with their FaceID. Then you can shoot with them using your own FaceID for, say, 30 minutes. If time runs out, you ask the guy at the shooting range to unlock them again.
It's an application of the two-man rule, [1].
Elephant hunters should be required to use low caliber revolvers, so the elephant has a better chance of killing them.
I wouldn't be surprised if most proponents of this tech have no real-world firearms training or experience.
I was recently approached to be a head of a self-driving car project; when I learned it was for military vehicle, I left the conversation. Maybe it would have been used for peaceful purposes occasionally (natural disasters), but I don't want to have my creation drive on its own somewhere in Middle-East and kill everyone around. It is an international project as well with the approval of leading governmental institutions to make it more fun and to give you some food for thought...
But anything that removes personnel from the front line usually makes transgression easier. The national will to fight becomes less important and wars are dubbed just as "operations". Perfect example would be the MQ-1 Predator.
I think you made very honorable choise. Good luck on your career.
Psychology researchers try to justify deep, compelling theories of human behavior with small experiments that are hard to reproduce.
Deep Learning experiments are comparatively easy to run and reproduce because the field has both a common set of performance benchmarks and a large community of people implementing cutting-edge ideas in common frameworks. The harder part is building useful theory on top of those experiments.
This article gives some evidence: [1], quoting:
> researchers were a bit perplexed when actress and director Kristen Stewart [2] appeared as an author on a machine learning paper.
[1] https://techcrunch.com/2017/01/19/kristen-stewart-co-authore...
Of course, you can do fancy things with neural networks without fully understanding how they work, or why they behave like they do. But my impression is that common applications, like image classification, are quite well-understood now.
Can anyone else recommend other equally broad works worth looking at?
Pair that will exceeding amounts of data being collected from/posted by users, you're got a great catalyst for major companies to be interested, which in turn lead to more research and refinement of old ideas.
12 years ago I was working for a machine vision startup with a Prof. two post-docs, a sales guy, and me as the only paid employee.
Now, I'm more likely to be working in a team of dozens, if not hundreds of machine vision researchers and developers.
Quite a change.
Pace of innovation is generally increasing as more and more complex stuff becomes commoditized. But also we forget just how recent a lot of things we take for granted really are.
Give it 10 years and AI will feel just as common and boring as a laptop does today. Remember how excited you were in the 1990s when you upgraded a new PC.
Microprocessor 1971 - Personal computer 1976 - mobile phone 1983 - smartphone 1996.
Your most recent date, smart phones, warrants more specific correction since it's so recent history: Nokia Communiator 1996, i-mode phones 1999, Ericsson R380 2000, Danger Hiptop 2002, Blackberry 2002. 2007 in smartphone history is marked by the introduction of the iPhone 1, 11 years after the first smartphones, but it didn't become popular quickly and didn't have apps etc. In 2013, smartphone sales surpassed feature phone sales.
Since 1947, when it was invented.
I must have been thinking of those glass things that came before transistors and filled a similar purpose then
One exciting topic I did not see mentioned (maybe I missed it) is image denoising. Excellent paper here - https://arxiv.org/abs/1608.03981. The CNN proposed in the paper is really compute intensive but the denoised images are really amazing.
ImageNet 2017 has happen since then[1] (small incremental progress), but lots of progress in things like super resolution.
I'd suggest anyone interested in jumping into building computer vision models should check out the excellent free MOOC on fast.ai
Capsules do require much fewer parameters, they generalize 10-20% better to new viewpoints, they are much more robust to adversarial examples and can better recognize overlapping objects; but, on the other hand, capsules currently require much more training data to achieve the same performance, even though in theory (if they would actually learn inverse graphics) they should require less data, and they add a lot of expensive additional structure (roughly 10X). I am rather pessimistic about whether the approach will lead us anywhere; it seems sub-optimal to model all possible child-parent configurations explicitly. That has a quadratic nature to it and my hunch is that can be done sub-linearily.