I have a crazy idea: Make a system that allows one to scan their face, and OPT-OUT OF ALL FACIAL RECOGNITION.
But then that system would have to delete your face too! Great, now the only solution left for the robots is to kill all humans...
We can’t trust AI systems built on deep learning alone
31–40 of 133 posts
Re: We can’t trust AI systems built on deep learning alone
#32One key question is whether symbolic AI is the right model of the world. It underperforms vector based AI on many specific tasks. But human experts heavily reply on it to communicate with each other. If symbolic AI is not the right model, P vs NP problem might be just irrelevant. Human philosophy is full of craps. We will lose a lot of beliefs.
Elon will be right, we will abandon human languages, and connect through a cable in the brain. Everyone will relearn every thing from NN.
If symbolic AI is the right model, but difficult to build algorithmically. Vector based model just help to make it faster and better. Then we humans are fine. We simply proxy the lower level optimization to AI. Our functionalities will be shifted just like what happened when engine was invented hundreds of years ago.
Re: We can’t trust AI systems built on deep learning alone
#33Has work been done to formally prove general AI can't arise from deep learning? I can't help but feel its an assumption being made by those that prefer classical research.
Re: We can’t trust AI systems built on deep learning alone
#34Earlier quoted context omitted.
> As much I'm hoping there'll be a breakthrough in AGI I think it probably won't be one breakthrough, but several, over decades. Personally, I'm pretty happy that AGI is taking a long time to materialize. We likely won't see a "fast takeoff scenario" (the computer is learning at a geometric rate !!1). It will likely happen gradually over years (progressively more intelligent, more aware computer systems), and we may…
A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.
Things like automatic transmissions and fuel injection aren't particularly incremental.
Re: We can’t trust AI systems built on deep learning alone
#35So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?
I've asked myself the same. I think of it this way: we can imagine that, in the set of all possible solutions to a particular problem, a subset of those are too hard for humans because they are solved with systems too large for a human to have designed in a reasonable amount of time, or the systems themselves contain pieces that are composed in some previously unrealized way. Of that subset, there may be solutions whose pieces and compositions are well-understood. In these cases (perhaps not exclusively), it might be possible for us to begin understanding any rationale.
edit: after noticing this other hacker news article (https://news.ycombinator.com/item?id=21107706), I wanted to add that this line of thinking is applicable to understanding programs and proofs written by humans as well. Programs and proofs can be well-understood when their pieces, and the way those pieces compose, are well-understood. When the pieces, e.g. lemmata in a proof, are large or hard to decompose, the proof (i.e. the solution to a problem) is harder to verify and understand.
Re: We can’t trust AI systems built on deep learning alone
#36I started reading Rebooting AI last night. I think that Marcus and Davis (so far in the book) take a reasonable approach by wanting to design robust AI. Robust AI requires general real world intelligence that is not provided by deep learning. I have earned over 90% of my income over the last five or six years as a deep learning practitioner. I am a fan of DL based on great results for perception tasks as well as soli…
Making 90% of your income off of this tech over the last n years is different than that tech being successful. I work at a very large company that is trying to use ML and AI in all kinds of places. The trend I am seeing is that most of that effort is falling flat, really flat, in fact. They have success in places where regular algorithms would also succeed, but just having regular developers design matching systems and do pretty basic statistics isn't sexy in terms of marketing, so they hush it all up and pretend that ML and neural nets and things are the only way forwards. I don't think it is. Our problem is that we have too many ETL robots who aren't very intelligent people and not very forward-thinking themselves!
The cutting edge NLP stuff just showcased at my company was pretty lame, too. I barely saw any statistically significant results at all and yet they rather unscientifically proclaim success because they got any effect at all. Some of what we do in our field doesn't matter because it comes down to whether a customer got a 2nd call back and got converted to some minor sale or added to a program for them. It's throw away and creates good will at conferences and talks. We make a big deal out of it.
We are spending hundreds of millions on projects, trying to save money on generating leads, reducing interactions with customers and vendors through staffed phone banks, and so on. My company has hired all kinds of academics and research type people and has given them titles of "Distinguished this" and "Principal that" and honestly there's not that much to show for it, maybe zero direct outcomes so far. What galls me the most is in all the conferences and demos they are showing off things like High School robotics vehicles and AI parlor tricks and astonishingly little has translated into the business we do. Meanwhile, there are people in the company who do know how to reduce costs and get more done and have outstanding outcomes, but their techniques are not sexy and thus unimportant to the PT Barnum MBAs running our company. I'm sure that's true most everywhere, of course.
These Principals and Distinguisheds all keep proclaiming success while cashing fat paychecks. Meanwhile this year, our stock has had a tough go of it, so I'm curious whether these attempts will continue. The market takes no prisoners. Sure we get a lot of mileage out of looking cool for the recent grad crowd purposes of recruiting--kids want sexy, cool tech projects to work on and words like "insurance" turn them off, so there's that, I guess.
My take on that is that it won't be long before all those new recruits will figure out they got bait and switched pretty bad and that they aren't going to get to work on any of this sexy ML and AI stuff anymore than I am in my role. I got lured in by Data Science (because PhD), which just shows how gullible I am, but at least some of that traditional statistical modeling is having an impact here and there. The problem again is that even that is overblown by a couple of orders of magnitude! In my project, we're simply trying to get more real-time data out to people who need it without having to call in to get it and that is ridiculously difficult because of all the systems we try to knit together and how overall terrible our data quality is. And now my boss wants to build out an "analytics engine" to capture some of this sexy ML and AI stuff. It leads me to believe that the people involved are most interested in getting promoted and not much more.
Anyways, it is cool tech, but American taxpayers and people who are forced to buy our products are paying for it and I rather think they would prefer to spend their money in some better fashion.
Re: We can’t trust AI systems built on deep learning alone
#37So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?
A lot of DL is about teaching computers to solve problems that are easy for humans (like driving and recognizing your grandmother) but for which humans have a tough time explaining how they do it. The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work. We're not there yet but progress has been made. Eventual…
> The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work.
I'd say that's a goal for some people -- for those whose goal is to figure out how the brain works, rather than constructing a more ideal and powerful GI. Remember the brain is great at some things, but laughable at others -- such as a "7 +/- 2" items in short term memory, inability to immediately retain rote knowledge after one instance and in great numbers, etc. It's the merging of the fuzzy, goal-directed behavior of the mind, in conjunction with its ability to effect the "real world", and the super-human memory and computational capabilities of computers that makes possible future GAIs that are so powerful and possibly scary.
Re: We can’t trust AI systems built on deep learning alone
#38Earlier quoted context omitted.
A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.
Things like automatic transmissions and fuel injection aren't particularly incremental.
As someone who once got stuck in an intersection after flooding a '79 Monte Carlo's carburetor while deciding to go on red, I'm with you on fuel injection. But I could see the counter-argument that something that makes an experience nicer is not radical innovation. That old junker got me where I need to go for a while.
Re: We can’t trust AI systems built on deep learning alone
#39The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
This argument completely ignores anything statistical, which is another limiting factor. It reminds me of the difference in bandit research and full RL research. RL researchers are fine throwing a thousand years of experience at their algorithm, because it can be simulated. Meanwhile people using bandits in the real world care about statistical efficiency (learning lots with little data), and it's reflected in the research. Most decisions aren't made with a huge abundance of data (most of us aren't google or facebook).
Re: We can’t trust AI systems built on deep learning alone
#40We cannot trust NI (natural intelligence) systems built on mom-and-pop having sex alone