We already had the AI winter before. The worst thing about it is the death of Symbolics. The latest AI resurgence, unfortunately, created nothing comparable to the legendary Lisp machines.
This AI Boom Will Also Bust
201–210 of 320 posts
Re: This AI Boom Will Also Bust
#202Recent AI progresses are a clear technological advancement. To meter them in terms of bay area biZZness meters is lame.
Re: This AI Boom Will Also Bust
#203There is a never ending confusion caused by the term "AI" to begin with. Term coined by John McCarthy to raise money in the 60's is really good at driving imagination, yet at the same time causes hype and over-expecations.
This field is notorious for its hype-bust cycles and I don't see any reason why this time would be different. There are obviously applications and advancements no doubt about it, but the question is do those justify the level of excitement, and the answer is probably "no".
When people hear AI they inevitably think "sentient robots". This will likely not happen within the next 2-3 hype cycles and certainly not in this one.
Check out this blog for a hype-free, reasonable evaluation of the current AI:
http://blog.piekniewski.info/2016/11/17/myths-and-facts-abou...
Re: This AI Boom Will Also Bust
#204Earlier quoted context omitted.
It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of sup…
>if there is no pixie dust that makes humans intelligent [...] then neural net breakthroughs [...] have the potential [...]
You're assuming neural nets are the right model.
Like a 19th century person saying, "if there is no pixie dust ... then eventually Newtonian mechanics will explain these unexpected wobbles we see in the planets' orbits."
Re: This AI Boom Will Also Bust
#205Earlier quoted context omitted.
It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.
This is the first time I'm hearing that computer scientists deride engineering as not-real-computing. Any references? It doesn't check out based on my background. In fact anecdotally, I've heard the reverse. I've heard EE and stat algorithms folks criticize CS ML/CV researchers for using algorithms as black boxes compared to the rigorous standards of EE/Stat (aka reviewer standards in IEEE Transactions in Information…
My understanding is that the grandparent comment was referring to _software_ engineers.
Re: This AI Boom Will Also Bust
#206I've read every comment in this thread and its filled mostly with peoples self congratulatory intellectual views. Nobody, not even Robin Hansen himself has given a good, detailed argument as to why the current progress in Machine learning will stop.
It's because that's just how things always work. Have you ever played one of those strategy games with a tech tree? Research A and it lets you research B, C, and D; research C and D and it lets you research E; etc? That's based on the way discoveries in the real world build on eachother. And in the real world, the research tree seems to be "lumpy". Think "agricultural revolution", "industrial revolution", etc. Someth…
you might enjoy a book called "the structure of scientific revolutions" by thomas s. kuhn. a historian of science who basically argued this. that book was the origin of the phrase "paradigm shift"
Re: This AI Boom Will Also Bust
#207[Disclosure: I work for a deep-learning company.] Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites. He is skeptical that deep learning and other forms of advanced AI 1)…
I think there are a lot of programmers try playing around with "deep learning" and it doesn't work for them. But they lack the knowledge necessary to make it work, such as calculus, statistics, signal processing theory, ect.
Re: This AI Boom Will Also Bust
#208Earlier quoted context omitted.
> I doubt you'll get that, because nobody thinks that progress in machine learning will stop. Robin Hansen is notoriously skeptical about the possibility that Deep Learning can make real gains. He for some reason thinks brain emulation is more likely to make large progress in AI. >An AI winter doesn't mean that progress stops. It doesn't completely stop, but progress would be at a snails pace. > The hype then dies do…
Brain emulation? I didn't realize people seriously thought that could be good for anything other than research and investigation. If we can successfully emulate the brain, it seems we would have necessarily acquired the knowledge needed to build models that are very powerful without having to exactly mimic the brain.
See Hanson's http://ageofem.com/
Re: This AI Boom Will Also Bust
#209Earlier quoted context omitted.
> Obviously it is hard to know when that magic moment will happen that some kind of general AI > is created that can learn in some sense similarly to how humans do. My every indication and > astonishment at the results that are being produced strongly suggests "at any moment". As an IT guy with a basic but solid neuroscience education (which isn't even needed for what I'm about to say): Yep, you are on the hype train…
My hype is different, because in my estimation we already have the hardware. You write: >As an IT guy with a basic but solid neuroscience education -- could you go ahead and take a few minutes (maybe will take you 5-10) to read through my above-referenced links referencing my previous discussion and tell me whether I'm correct in your estimation on the bottom-up aspect - i.e. the amount of computation that human neur…
If P=NP then we already have the hardware to crack RSA encryption.
The above sentence is true, but it has no bearing on anything.
Re: This AI Boom Will Also Bust
#210Earlier quoted context omitted.
> But there should be no ambiguity, with enough data. What? I'm sorry but this runs counter to everything in my experience, both professionally, and just casual very day experience. More data, helps to a point, but then there's diminishing returns, and it certainly doesn't eliminate the ambiguity. On the contrary, you discover diversity, and you still have a misclassification and perhaps even a harder data cleaning p…
Yeah that's why I called it philosophical, because the idea is a little more involved, shall we say. I'm not a god of this, so speculation ahead bewarned. In cases that aren't clear cut, you would also need contextual data like bigger actual physical area, or over time dimension, really any data point that can help narrow down what the thing is. It wouldn't just be pure deep learning stuff, it would be some kind of m…
This is literally a philosophical problem. It's called ontology. And no amount of data solves this problem, because ultimately it's a labeling problem, and the border between things is ill defined, and additional data doesn't help resolve labeling ambiguity, if anything it finds out just how ill defined the world actually is.
Think about it. Let's say you had a problem which was find the black squares. So you collect some data and you find that you have a whole bunch of squares that are on the blackness scale of 0.0, and bunch that are 0.1, and then there's one at 0.5. Is 0.5 black? Maybe not. What about 0.7? Maybe. What about 0.999? Probably, but is it? It's not 1.0. And if we say 0.9 and higher are black, why not 0.89? Even discounting measurement error, there's nothing that supports a threshold at 0.9 beyond, "Well, I think it should be this."