Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

This AI Boom Will Also Bust

overcomingbias.com

Post reply on HN
Print view View original

151–160 of 320 posts

Re: This AI Boom Will Also Bust

#151

Post by komaromy »

robotresearcher wrote: post #112
toomuchtodo wrote: post #21

Earlier quoted context omitted.

> the people who'll be in trouble with be those without formal education to back them up. The people who can't hack it are those who'll be in trouble. Tech has never much been the place where credentials are necessary. Don't specialize and saddle yourself with years of college debt if you're unsure of the field's long term prospects.

Almost every good place pays their CS PhD students enough to squeak by without getting into debt. I wish this was more widely known. And companies like Google are pretty keen on academic credentials. They've assembled what must be one of the largest collections of PhDs in history.

> Almost every good place pays their CS PhD students enough to squeak by without getting into debt. I wish this was more widely known.

Right. The debt problems that people have after PhDs are more often due to their undergrad loans sitting around for 4-7 years while they were earning enough to subsist and not more.

Re: This AI Boom Will Also Bust

#152

Post by ffwd »

jonathankoren wrote: post #142
ffwd wrote: post #116

Earlier quoted context omitted.

But there should be no ambiguity, with enough data. Maybe that means there will always be ambiguity, but maybe it doesn't, especially not with man-made things and complex natural objects, and also if you can contextualize the data over time and 'geographically', there is more 'signal' there to differentiate

> 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 memory and data store of already classified objects and contexts. In the ultimate end, ALL of it would be sparse, but classify perfectly just that one thing it is built to classify. And if that doesn't work, several sparse things combined would result in one unique thing. On the sparsity matrix wikipedia page there is an example of balls with a string through them, this would correspond to the data being the balls and the systems we build (or alternatively unsupervised learning methods for finding new strings), whatever they may be, would be the strings (assuming all the strings are actual informational and correct to natural world). But you need the balls to begin with etc. Since all of this information should be in the natural world by its own, and also accessible to us

Re: This AI Boom Will Also Bust

#153

Post by sangnoir »

dTal wrote: post #90
dheera wrote: post #39

Earlier quoted context omitted.

And before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough.…

Stereo vision is obviously highly effective in biology as it has independently evolved a great many times. Time-of-flight may be poised for a renaissance, but it scales badly and is active, not passive. Stereo vision, and its big brother light fields, are far more general and are certainly not going to "die out".

> Stereo vision is obviously highly effective in biology as it has independently evolved a great many times

This argument sounds like a second cousin of the "chemicals are bad, but if it is natural it is good" argument. Just because it evolved in nature doesn't mean it's optimal, or it's the best system under massively different constraints. And who knows what evolution would have thrown up after a few more billion years.

Re: This AI Boom Will Also Bust

#154

Post by bunderbunder »

RushAndAPush wrote: post #109
bunderbunder wrote: post #94

Earlier quoted context omitted.

I doubt you'll get that, because nobody thinks that progress in machine learning will stop. An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to pr…

> 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…

I don't know that I'm positing any sort of formal theory, just describing a pattern that's been going on for decades now. I'd suggest that unless something fundamental changes, there's a reasonable chance that it will keep on going that way.

Progress in machine learning has never been at a snails pace. What has happened at times is that the futurists of the world stop making breathless pronouncements about machine learning, and that creates a public perception that things are going slowly.

Case in point: Deep learning really isn't anything revolutionary or new. I first started noticing papers about stuff that now falls under the "deep learning" catchphrase about 15 years ago. Not because it started then, but because that's when I started reading that sort of thing. During that time, there's been relatively constant, steady progress being made. But you wouldn't know it unless you had been following the literature, which not many people do. And all of this happened at a time when everyone was convinced that neural nets were dead and support vector machines were the way of the future.

As far as Robin Hansen's skepticism about deep learning getting us to true artificial intelligence, meh. A technology doesn't need to make Ray Kurzweil's eyes roll back in his head for it to be useful.

Re: This AI Boom Will Also Bust

#155

Post by wildmusings »

Until the taboo on talking about consciousness is broken and we seek to understand what role this incredible phenomenon plays in human cognition, there will be no progress towards the holy grail: true general purpose AI. That is my falsifiable prediction.

Re: This AI Boom Will Also Bust

#156

Post by [deleted] »

nightski wrote: post #95
bunderbunder wrote: post #66

Earlier quoted context omitted.

Simple regression has a lot going for it beyond simply being a simpler model. For example, it produces models that are easy to interpret. That is an enormous advantage if you're looking to use data science to help drive strategic decisionmaking. My personal suspicion is, in a market full of people who are using ever more sophisticated algorithms to ratchet up their customer conversion classifiers' F1 scores by .001 p…

I think calling linear regression simple is misleading. While the algorithm is simple, interpreting the results and not falling into one of the many traps is quite difficult!

[deleted]

Re: This AI Boom Will Also Bust

#157

Post by lucidrains »

jpatokal wrote: post #149
logicallee wrote: post #138

Earlier quoted context omitted.

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…

I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle . I think the author rests their case...

just toys? https://en.wikipedia.org/wiki/Deep_learning#Applications

Re: This AI Boom Will Also Bust

#158

Post by cma »

dheera wrote: post #39
pesenti wrote: post #11

When I was at Watson this is the first thing I told every customer: before you start with AI are you already doing the more mundane data science on your structured data? If not, you shouldn't go right away for the shiny object. This said I still believe the article is mistaken in its evaluation of potential impact (and its fuzzy metaphore of pipes). Unstructured or semi-structured or dirty data is much more prevalent…

And before you do mundane data science on your structured data, you should figure out if there is a better way to get cleaner raw data, more data, as well as more accurate data. For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough.…

Any links to your papers? How much better have things gotten over e.g. Kinect v2, which was pretty bulky and power hungry, and had active cooling? I'd imagine it has gotten a lot better.

Re: This AI Boom Will Also Bust

#159

Post by kevinchen »

ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

> I feel like this post is essentially someone from the first group criticizing the last group, saying their methods are not applicable to him.

Probably more accurate to say that it's the first group criticizing others in the first group who try to act like people in the third group. Data scientists who use deep learning for everything, when a more interpretable model would do just as well.

Re: This AI Boom Will Also Bust

#160

Post by cma »

sidlls wrote: post #110
ma2rten wrote: post #101

I think this field is suffering from some confusion of terminology. In my mind there are three subfields that are crystallizing that each have different goals and thus different methods. The first one is Data Science. More and more businesses store their data electronically. Data Scientists aim to analyze this data to derive insights from it. Machine Learning is one of the tools in their tool belt, however often they…

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's also suffering from hype.

I'm imagining someone tweeting at Robin, several years back:

"Good Prediction Markets expert says: Most firms that think they want advanced Prediction Markets really just need linear regression on cleaned-up data."

;)

Post reply on HN