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An understanding of AI’s limitations is starting to sink in

economist.com

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Re: An understanding of AI’s limitations is starting to sink in

#281

Post by bregma »

p1esk wrote: post #255
wpietri wrote: post #164

Earlier quoted context omitted.

In communication, meaning is a collaboration between writer and reader. The writer does their best to convey something; the reader does their best to understand. There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data. That's pretty different from communication; it's more a process of internal generation of notions and explanations that could later be conveyed…

a process of internal generation of notions How is this "knowledge extraction from data" process different?

Immanuel Kant would call the former "rendering a synthetic judgement" and David Hume termed the latter revealing the a priori knowledge. Plato would call both simply giving substance to the forms.

It seems none of this is something new. It's just that you no longer need a good education to learn about old ideas; you can come up with them on your own.

Re: An understanding of AI’s limitations is starting to sink in

#282

Post by foldr »

hamsterbooster wrote: post #187
tomp wrote: post #101

Earlier quoted context omitted.

Aside from possibly Google, all of these products / services would have just as much, if not more, value without any AI beyond basic statistics.

Many AI systems are used in the backend to increase revenue. Netflix has a very complex recommendation algorithm based on deep learning/statistics. Amazon uses a lot of machine learning to optimize transportation (NP-hard problem!) , sales, etc..

>Netflix has a very complex recommendation algorithm based on deep learning/statistics

And yet, it's harder to find anything to watch than it was when they'd just let you browse by genre.

Re: An understanding of AI’s limitations is starting to sink in

#283

Post by jpttsn »

robbrown451 wrote: post #99
lambdatronics wrote: post #71

Earlier quoted context omitted.

Yes, but you're missing the point. In 1950's sci-fi, those marvels were possible because there was imagined to be something like a general artificial intelligence behind the technology. We've achieved narrow AI, but the perception is that in order to get it, we would already have general AI, which is why people are disappointed.

Whether 50s sci-fi imagined or implied other things is irrelevant to the question of whether or not the current capability described (point camera at sign, get translation) qualifies as AI. The point is we have current things that are quite amazing, and would at one time have been considered to be the sort of thing that only an AI would be able to do, and yet we keep moving the goalposts. As if AI is defined as "that…

But was it the “capability” that qualified as AI in the 50s? Or was the capability just one example of what the AI could do?

Suppose we said we’ve invented Jesus because we’ve invented ways to walk on water and turn it into wine.

Re: An understanding of AI’s limitations is starting to sink in

#284

Post by discjockeydom »

spaetzleesser wrote: post #75
mrfusion wrote: post #32

What’s the next big thing after deep learning?

There should be something like an X prize for a recycling device. Take a trash pile, sort through it and get things ready for recycling. This would solve a big environmental and would deliver progress in robotics, vision and AI. I'd be more excited about this than self driving cars.

https://zenrobotics.com

Re: An understanding of AI’s limitations is starting to sink in

#285

Post by nextos »

deepnotderp wrote: post #192
nextos wrote: post #36

Earlier quoted context omitted.

I guess once the field starts mixing probabilistic, deep and logic approaches we will see a bit of progress towards AGI. This is one example of where things might be heading: https://science.sciencemag.org/content/350/6266/1332

This has been on my watchlist for a while. The problem is the inablity of these techniques to scale to larger problems. That being said, once that happens, it could be revolutionary.

DARPA threw some money into the problem, and stuff advanced from toy problems to things that are still small but often useful.

See e.g. pyro.ai is already working on non-trivial datasets.

Ultimately, I'm convinced it's a hard compilers / static analysis / abstract interpretation problem. That requires lots of resources and a decade or two of work.

Re: An understanding of AI’s limitations is starting to sink in

#286

Post by rpastuszak »

YeGoblynQueenne wrote: post #262
mattlondon wrote: post #53

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humid…

This is a good explanation of how things work and things haven't changed much in the past 5 years. What changes the most is the amount of data and computing power ("compute") that are used in training neural nets. Architectures are also tweaked slightly although they continue to be largely based on the two architectures that started the current deep neural net boom, Long-Short Term Memory Networks (LSTMs) and Convolu…

> but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches).

That makes me particularly sad. In a utilitarian sense, the ideal situation would involve new approaches being democratised. Instead, we're ending up with walled gardens.

The current situation isn't dissimilar to what has happened to the Web Platform during past 5-10 years. And, similar predictions are made wrt VR and AR (both seem to attract different types of investors).

Re: An understanding of AI’s limitations is starting to sink in

#287

Post by dr_dshiv »

mattlondon wrote: post #53

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humid…

To look at a beautiful reframing of AI as a tool and not as a quasi-other agent, please enjoy this page: https://nooscope.ai/

Re: An understanding of AI’s limitations is starting to sink in

#288

Post by coverup »

TrackerFF wrote: post #278
mattlondon wrote: post #53

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humid…

That's actually something ML is incredibly useful at, when it comes to machines with sensors - failure prediction / anomaly detection, etc. In the industry, (preventive) maintenance takes up a pretty huge chunk of resources. It's something techs need to do often, and it's often a laborious task, but it's obviously done to reduce downtime. So the business insight, as they like to call it, is to reduce costs tied up to…

Yep, you and the user you're replying to are both right in different ways. One thing's for sure - machines don't generate "insights" on their own.

Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making.

In a supervised learning problem, the range of possible outputs is already known, meaning the model output will never be categorically different from what was in the training data. The knowledge obtained is meaningful as long as the training labels are meaningful, but it can never be new.

Unsupervised learning doesn't have a notion of "training data" but that means an unsupervised model's output requires additional interpretation in order to be meaningful. It is possible to uncover new structures and identify anomalies in new ways, but this knowledge isn't meaningful until someone comes in and interprets it.

Applied to the specific example where sensor data is used to try to generate insights about machine functionality: Either you can only predict the types of failures you've already seen, or you can identify states you've never seen but you wouldn't know whether they mean the system is likely to fail soon or not.

It's the Roth/401(k) tradeoff. For model output to be useful, someone must pay an interpretation tax. The only choice is whether it is paid upon insight deposit or withdrawal.

Re: An understanding of AI’s limitations is starting to sink in

#289

Post by mellosouls »

0xBABAD00C wrote: post #198
archsurface wrote: post #54

Earlier quoted context omitted.

"Hi AlphaGo, let's play tic tac toe". Consider me bored of it all. We're not close to anything. The emphasis is on "artificial", not "intelligence". It should be renamed Glorified Calculation. There are some neat tricks, writing your first neural net is great fun, but we're using the word intelligence with spectacular liberty. I'll keep an eye open for something inspiring, but I've seen nothing of the sort yet.

Then you're not looking hard enough

Proof of AGI progress would be a significant enough achievement (given there hasn't been any for decades) that it wouldn't require looking hard - but please share any links to it you have.

Nobody else seems able to, especially the AI ("more power and money needed! we're just around the corner!") blowhards.

Re: An understanding of AI’s limitations is starting to sink in

#290

Post by bryal »

neals wrote: post #33
mtgp1000 wrote: post #11

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…

Honest curiosity: do you have some examples of interesting applications? Large and small?

I'm not much into machine learning myself, but I must admit that ML has been invaluable for good voice recognition and good voice synthesis. Writing programs to interpret speech accurately and synthesizing a "natural" sounding voice by hand is practically impossible, but the perfect application for ML.
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