This AI Boom Will Also Bust • Hacker News Board

Re: This AI Boom Will Also Bust

#211

Post by kodisha » Sun, Dec 04, 2016, 12:37 AM UTC

So, imagine that in 2011, 5 years ago, you approach some VC and say:

"Hey, we are building this VR hardware and games for it, we would need ~1M to finish it".

I think that there is high chance that you would get some weird looks, and possibly few remarks how that is a "dead technology, tried once, and obviously failed".

And then, fast forward couple of years there is whole industry around VR, jobs, hardware, software, the whole eco system.

You only need one strong player in a field, and suddenly everyone and your neighbour kid is doing it.

Re: This AI Boom Will Also Bust

#212

Post by c3534l » Sun, Dec 04, 2016, 12:45 AM UTC

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.

This and the above comment kind of says my thoughts on the matter. I think there is a very real emerging field that combines mathematics, traditional computer science, and AI into a big family resemblance of quantitative methods and research, but that we're still in the early stages and we're still sorting things out. I remember hearing someone on hacker news joke that when he got out of school people called him a statistician, then people started saying he was a data miner, and now he sells himself as a data scientist, but he hasn't actually done anything differently apart from just keeping up with the field. Sometimes it feels as if we're just playing with words and people get excited for advances and what it means for the future, but the reality is people just keep plodding along make incremental progress, finding new ways to leverage the tools they have, and continuing to publish papers. For all of the tech industry's worship of "disruptive innovation" and massive changes, the reality is that we've been developing and refining our quantitative abilities and figuring out automation since the industrial revolution. AI may become less exciting to people 5 years from now, but we will still continue doing what we've been doing.

Re: This AI Boom Will Also Bust

#213

Post by dmfdmf » Sun, Dec 04, 2016, 1:17 AM UTC

felippee wrote: post #203

There 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 i…

Thanks. This is why I love HN for finds like this, after poking around a bit this site looks like a really good blog that doesn't hype AI nor deny it which is congruent with my views.

Re: This AI Boom Will Also Bust

#214

Post by logicallee » Sun, Dec 04, 2016, 1:28 AM UTC

xamuel wrote: post #209

logicallee wrote: post #196

Earlier quoted context omitted.

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.

don't you think it would have a lot more bearing if you had 7 billion devices nonchallantly walking around cracking RSA every day using the same or less hardware? (but we couldn't reverse-engineer them, because they were obfuscated in biology)?

The fact that they weren't reverse-engineered (yet) would still have huge bearing on everything.

By 7 billion samples I mean the humans walking around. Your analogy with an RSA crack is fundamentally different beccause biology doesn't do it in 3 pounds of grey goo in seven billion different bodies already.

so you would have to come up with an analogy that uses something we cant use, to say, okay fine it exists and fine, we have the hardware to also do it, but the former doesn't have any bearing on us doing the latter.

Re: This AI Boom Will Also Bust

#215

Post by mwfunk » Sun, Dec 04, 2016, 1:41 AM UTC

I'm very curious to what degree there even is an AI boom right now, vs. AI and machine learning going through a phase as the buzzwords du jour used in corporate PR. People have doing all sorts of fascinating things with machine learning for decades, and (for example) Google has been arguably an AI-focused company from day one.

In the tech press recently, I keep hearing how every huge tech company needs to have some sort of AI strategy going forward, so they don't miss out on an industrywide windfall, or even become irrelevant because they didn't hop on the AI bandwagon.

I suspect that there are a few more people working in AI nowadays than we're doing so 10 years ago, but that quite a bit of the narrative surrounding AI in the press is some combination of corporate marketing and journalists eager to have something to write about.

I'm not saying AI isn't important, rather that it's an important field that's only a little more important than it already was 10 years ago. The difference seems to be how often it pops up in PR and tech journalism vs. 10 years ago. Just a theory of course; I would love to know what the reality is.

Re: This AI Boom Will Also Bust

#216

Post by [deleted] » Sun, Dec 04, 2016, 1:45 AM UTC

throw_away_777 wrote: post #64

kristjankalm wrote: post #30

Earlier quoted context omitted.

This is a false dichotomy. Both OLS regression and, say, random decision forest regression have the same objective (predict values) and achieve it with similar means (build a generative model / function). They solve the same problem. Contrastingly, assembler and python are broadly aimed at completely different use cases. Broadly, whether you should move from OLS to random forest regression = SNR increase / increase i…

It is actually much easier to apply a random forest (or really gradient boosted decision tree, which almost strictly dominates random forests) than a linear regression. Decision tree methods require far less data preprocessing than linear regression, because the model is able to infer feature relationships. Obviously if your features are linearly related to your target than linear regression is much more viable.

[deleted]

Re: This AI Boom Will Also Bust

#217

Post by nomailing » Sun, Dec 04, 2016, 1:47 AM UTC

lucidrains wrote: post #173

logicallee wrote: post #161

Earlier quoted context omitted.

No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.) IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869 ) 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 s…

absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167

Interesting. The concept seems to be very similar to this one: https://arxiv.org/abs/1611.01578

Re: This AI Boom Will Also Bust

#218

Post by swordswinger12 » Sun, Dec 04, 2016, 1:48 AM UTC

shas3 wrote: post #191

sidlls wrote: post #110

Earlier 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…

The prof of my applied ML class this semester jokingly refers to ML research's approach to statistics as "punk stats", which I find to be pretty accurate.

Re: This AI Boom Will Also Bust

#219

Post by mmkx » Sun, Dec 04, 2016, 1:49 AM UTC

zamalek wrote: post #200

One of two eventualities exist: * The article is correct and the current singularity (as described by Kurzweil) will hit a plateau. No further progress will be made and we'll have machines that are forever dumber than humans. * The singularity will continue up until SAI. So help them human race if we shackle it with human ideologies and ignorance. There is no way to tell. AlphaGo immensely surprised me - from my pers…

It's already happening. The world became hyper efficient. The hackers behind "Trumpbots" made money on the prediction markets. Understanding that mechanic makes me almost certain the singularity arrived after his election.

Re: This AI Boom Will Also Bust

#220

Post by brudgers » Sun, Dec 04, 2016, 1:50 AM UTC

Most firms that think they want advanced AI/ML really just need linear regression

That's how AI always looks in the rearview mirror. Like a trivial part of today's furniture. Pointing a phone at a random person on the street and getting their identity is already in the realm of "just machine learning" and my phone recognizing faces is simply "that's how phones work, duh" ordinary. When I first started reading Hacker News a handful of years ago, one of the hot topics was computer vision at the level of industrial applications like assembly lines. Today, my face unlocks the phone in my pocket...and, statistically, yours does not. AI is just what we call the cutting edge.

Open the first edition of Artificial Intelligence: A Modern Approach and there's a fair bit of effort to apply linear regression selectively in order to be computationally feasible. That just linear regression is just linear regression these days because my laptop only has 1.6 teraflops of GPU and that's measley compared to what $20k would buy.

The way in which AI booms go bust is that after a few years everybody accepts that computers can beat humans at checkers. The next boom ends and everybody accepts that computers can beat humans at chess. After this one, it will be Go and when that happens computers will still be better at checkers and chess too.