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This AI Boom Will Also Bust

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Re: This AI Boom Will Also Bust

#191

Post by shas3 »

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 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 Theory, IEEE Trans Sig Proc, majority of stat journals). Now obviously there are a vast number of CS researchers who are competent at stat-level rigor. But there are also a large number of ML researchers (applied ML?) in CS departments who use algorithms as black boxes.

Re: This AI Boom Will Also Bust

#192

Post by nairboon »

wildmusings wrote: post #155

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.

Could you elaborate on that consciousness taboo?

Re: This AI Boom Will Also Bust

#193

Post by taeric »

shmageggy wrote: post #167
taeric wrote: post #60

Earlier quoted context omitted.

If there is a curse of our industry, it is almost willful ignorance of just how hard the physical engineering fields are. The simple things with pipes are simple. Yes. However, to think we haven't made advances, or have no more to make, is borderline insulting to mechanical engineers and plumbers. Ironically, deep learning will likely help lead to some of those advances.

> However, to think we haven't made advances, or have no more to make Not what I was saying at all. My point was that pipes are used to transport something from point A to point B, and that regardless of what advances we make, they are still going to be used for that purpose, and that this is unlike the situation with AI.

My apologies for twisting your point, then. I confess I do not think I see it, still. :(

Pipes do much more than just transport from a to b. Though, often it is all a part of that. Consider how the pipes of your toilette work. Sure, ultimately it is to get waste out of your house. Not as simple as just a pipe from a to b, though. You likely have a c, which is a water tank to provide help. And there are traps to keep air from sewage getting back in.

Basically, the details add up quick. And the inner plumbing for such a simple task are quite complicated and beyond simple pipes.

So, bringing it back to this. Linear algorithms are actually quite complicated. So are concerns with moving all of the related data. And that is before you get to things that are frankly not interpretable. Like most deep networks.

Re: This AI Boom Will Also Bust

#195

Post by notahacker »

jbrambleDC wrote: post #146
WhitneyLand wrote: post #127

This article is tries to be right about something big, by arguing about things that are small and that do not necessarily prove the thesis. Notice now you can cogently disagree with the main idea while agreeing with most of the sub points (paraphrasing below): 1) Most impactful point: The economic impact innovations in AI/machine learning will have over the next ~2 decades are being overestimated. DISAGREE 2) Subpoin…

another point is that Linear Regression IS Machine/statistical Learning. Sure its been around for more than 100 years before computation, but regression algorithms are learning algorithms. Arguing for more linear regression to solve a firms problems, is equivalent to arguing for machine learning. Now, if instead he wanted to argue that the vast majority of a businesses prediction problems can be solved by simple algo…

If we're classing linear regression as machine learning and agreeing it's a representative example of the type of simple algorithm that's most likely to benefit firms, I think it probably helps his point rather than harming it. It's a technique that's been around for ages, is far from arcane knowledge and every business has had the computing capability to run useful linear regressions on various not-particularly-huge datasets in a user-friendly GUI app for at least a couple of decades now.

For the most part they haven't run those regressions at all, and where they have, they haven't been awe-inspiringly successful in their predictions, never mind so successful the models are supplanting the research of their knowledge-workers.

Re: This AI Boom Will Also Bust

#196

Post by logicallee »

Noseshine wrote: post #183
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…

> 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 neural nets can likely be doing, and how it compares to server farms with fast interconnects today?

I'm not an expert in neuroscience so your feedback might be helpful there.

Re: This AI Boom Will Also Bust

#197

Post by michaelt »

kd0amg wrote: post #135
revelation wrote: post #100

Earlier quoted context omitted.

Yet humans produce terrible depth data.

Terrible for what purpose? Humans seem pretty good at throwing things to each other and catching them. I'm very bad at coming up with a good numeric estimate of linear size. As a fencer, I could never tell you how many inches between me and my opponent, how long his or my arms are, how tall he is, etc. I could definitely tell you which parts of our bodies are within reach of each other's arm extension, fleche, lunge,…

Terrible for the purpose of proving a general-purpose stereo machine vision system is practical.

The distance at which a stereo vision system can capture precise depths depends on the distance between eyes, and the eyes' angular resolution. Human depth perception works well for things within about 10m, but when you get out to 20-40m humans get a lot less info from stereo vision.

When you get to that distance, humans seem to have a whole load of different tricks - shadows, rate of size change, recognising things of known size, perspective and so on. You can see a car and know how far it is even without stereo vision, because you know how big cars are, and how big lanes and road markings are. You can even see two red lights in the distance at night and work out whether they're the two corners of a car, or two motorbikes side-by-side and closer to you.

On the other hand, your basic general-purpose stereo machine vision system doesn't try to understand what it's looking at - you just identify 'landmarks' that can be matched in both images (high contrast features, corners etc) and measure the difference in angle from the two cameras. This is relatively simple and easy to understand!

For tasks that humans can do that involve depth perception of things more than ~40m away - flying a plane, for example, where most things are more than 40m away if you're doing it right! - nice simple stereo vision can't get the job done, because humans are actually using their other tricks.

Of course, despite this limitation stereo vision comes up a lot in nature - it's still a beneficial adaption, because most things in nature that will kill you do so from less than 10m away :)

Re: This AI Boom Will Also Bust

#198

Post by orthoganol »

vonnik wrote: post #123

[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)…

> that will prove him wrong

I'm skeptical of claims about a one-shot learning silver bullet, unless people are talking about something different from how it has been classically presented, .e.g. Patrick Winton's MIT lectures. Yes, you can learn from a few examples, but only because you've imparted your expert knowledge, maintain a large number of heuristics, control the search space effectively, etc. There's a lot of domain-specific work required for each system, so I consider it more an approach of classical AI and not something that figures out everything from the data alone, like deep learning.

But again, maybe people are talking about something different than my above description when they talk about one-shot learning today. Either way, I don't think having to rely on a lot of domain specific knowledge is necessarily a bad thing.

Re: This AI Boom Will Also Bust

#200

Post by zamalek »

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 perspective the singularity is happening, but there is no telling just how far it can go. AlphaGo changed my perspective of Kurzweil from a lunatic to someone who might actually have a point.

Where the line is drawn is "goal-less AI," possibly the most important step toward SAI. Currently, all AI is governed by a goal (be it a goal or a fitness function). The recent development regarding Starcraft and ML is ripe for the picking, either the AI wins or not - a fantastic fitness function. The question is, how would we apply it to something like Skyrim: where mere continuation of existence and prosperity are equally as viable goals (as-per the human race). "Getting food" may become a local minimum that obscures any further progress - resulting in monkey agents in the game (assuming the AI optimizes for the food minimum). In a word, what we are really questioning is: sapience.

I'm a big critic of Bitcoin, yet so far I am still wrong. The same principle might apply here. It's simply too early to tell.

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