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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

#311

Post by blaser-waffle »

entha_saava wrote: post #225
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…

It sounds like some buzzword speak. Most things that are heralded as ML are nothing but data science idiots from python schools applying some basic math transformations and overselling them.

I was going to say "most ML these days nothing but chains of if-else statements" but yeah same idea.

What's wrong with python?

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

#312

Post by Retric »

shadowmint wrote: post #293
TrackerFF wrote: post #278

Earlier quoted context omitted.

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…

If the business wanted to track the rate of failures and create predictive models about when things fail, or detect anomalous behaviour, that's what they would have set out with as the goal, and, perhaps, some ML model might have helped, but probably, it would've been too unreliable and any number of standard predictive models with well known characteristics would have been used instead. That's not what they wanted.…

Projects rarely end up doing what was planned when they started. As long as ML is solving real problems in practice, upper management will keep treating it as magic fairy dust to sprinkle around aimlessly.

It's all about how you package things. ML connected to an audio sensor could predict failure modes that are dificult to detect otherwise. Now that might not be was was asked for, but a win is a win.

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

#313

Post by bumby »

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…

I think both your post and the one you are responding to are correct.

I’ve experienced what the OP was alluding to...namely, it helps tremendously to start out with an understanding of the problem you’re trying to solve, more so in supervised learning. It’s incredibly frustrating to ask managers what business problem they are trying to solve only to be met with “We don’t know, that’s what we want the software to tell us.”

On the other hand, if they say “we want to predict machine failures” or “reduce maintenance downtime” now we have a lens in which to view the data.

If AI could do the magic as those managers hoped, they would be out of a job

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

#314

Post by code4tee »

In probably 99% of AI/ML use cases the AI/ML is basically just a commodity item and the real “expertise” comes from getting and preparing good datasets for analysis, and having a clear problem to solve. The strategy behind something like AWS SageMaker is based entirely around this idea.

The problem is that too many companies believed it was the opposite so they built and hired all these AI/ML “experts” that just wanted to “built models” but didn’t want to focus in the messy hard stuff like finding and cleaning data. Nearly all of these AI/ML “experts” inside companies were also broadly just applying off the shelf tools and algorithms, perhaps with a bit of ensembling, rather than actually building new AI/ML approaches.

As a result, the big investments inside most companies produced a flash and puff of smoke that got people briefly excited followed by a lot of money spent with little business value returned.

I’m a big believer in ML approaches, but in most cases companies need to be focusing in their data first against clear business problem and just use off the shelf tools for the rest. That’s good enough for nearly all needs.

There’s a big bubble at the moment with all these “AI/ML” teams that’s going to crash hard as businesses realize the above and reset to focus on stuff that works and generates tangible value for the business.

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

#315

Post by beckingz »

jcdentonunatco wrote: post #299
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…

Got bad news for you man. 16T in value doesn't come from ML. It comes from inflation and printing money out of thin air. The world has limited resources at the end of the day. When one industry does well, it's at the cost of a different industry.

We haven't even mined the moon yet. Plenty of gains to be made.

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

#316

Post by a1369209993 »

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…

> the possible MNIST digits are 0-9

Except - and this rather ties into your point - those are not the only possible digits; your network also has to deal with (ie reject) other possible digits such as "P", "E", "3̸̶", or "[Forlorn Sigil of Amon-Gül redacted]"[0], which look like, but are not, decimal digits.

0: https://www.youtube.com/watch?v=ajGX7odA87k

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

#317

Post by govg »

bildung wrote: post #306
MiroF wrote: post #295

Earlier quoted context omitted.

In the past three years we have dropped error rates by a factor of 3, I don’t really think that claim holds water. We have seen huge progress in a number of other fields as well. Anecdotally, voice recognition has definitely gotten way better as well.

Both voice recognition had first working research machines in the 1950s. Of course present models are way better than these, but fundamentally these are the same as those in the '50s, "just" with tremendously better hardware and algorithms. But there is zero intelligence in these, the models have no internal concept of language or the world around them. This will certainly produce many great specific solutions for sp…

There are tons of research projects that do exactly what you're talking about, initializing an agent with zero knowledge in an environment which just provide rules of discovery and reward. The agent then takes actions in that space and learns from its own experiences. It's just that it is hard to compare with humans, who have had the benefit of evolution over millions of years. A basic example is that of AlphaGo Zero[1], which learns to play a top level game by being given enough time and just the rules of the game. This is similar to how a child learns to walk, it is just that we only have the capability to model toy situations (a board game in this instance) right now, and access to harder instances (movement in the real world) will slowly come about. There are cases of robots being programmed to poke / move / pick objects to try and learn about their shapes[2], in case you are interested in another such example.

[1] - https://en.wikipedia.org/wiki/AlphaGo_Zero [2] - https://bair.berkeley.edu/blog/2019/03/21/tactile/

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

#318

Post by walleeee »

smeeth wrote: post #297
AlexandrB wrote: post #220

Earlier quoted context omitted.

> There's far more to do with ML and AI than self driving cars and shitty ad recommendations. Yeah, there's also shitty sentencing recommendations[1], new-age phrenology[2], and high-tech redlining[3]. I think your entire field needs to take a year off and take some ethics and philosophy courses before going any further. Otherwise we're all going to end up much worse off. [1] https://www.nytimes.com/2017/10/26/opinio…

This is a bit like telling the whole CS community in the 90s that we need a year off the internet to study ethics because people can use it for child porn, money laundering, and cybercrime. Should we have?

Parent's condescension drips a little heavy, but anyone building power tools for a species like ours ought to reflect a while on the potential consequences, imho

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

#319

Post by mjburgess »

tgv wrote: post #277
dreamcompiler wrote: post #216

Here we are in 1989 again. The cycle keeps repeating. A new advancement in computing power, networking, or algorithms means there's a new batch of low-hanging fruit for AI to pick, so we pick it. Investors say "What about the high-hanging fruit?" and we say "No problem. We just need a slightly longer ladder." Two years later everybody finally realizes the high-hanging fruit is on the moon.

My first AI teacher (even before '89) compared solving AI with neural nets to teaching pigs to fly by throwing them from a tower. Improvements come from building higher towers. There's a recent NLP model that was trained on a trillion words. It would take us 10,000 years to read or listen (no breaks, no sleep) to that many words. Problems like attention, and the relation between memory and sequential thinking haven't…

It isn't ever going to model our style of thinking. A "neural network" is just high-dimension linear regression; the idea it has anything to do with the brain is metaphorical nonesense.

No algorithm running on digital hardware can emulate the biological process of animal intelligence.

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

#320

Post by renewiltord »

reportgunner wrote: post #292
renewiltord wrote: post #65

It's limited but so effective! The other day a friend asked for photos of her sister (also a friend) that I had because it's her birthday and she wanted to make a collage. I just searched on Google Photos by her name and it found a bunch because of the face classification. That's some good shit.

How do you know you haven't missed any good pictures of your sister ?

You don’t but that’s okay. I have thousands of photos. I don’t need exhaustiveness. I need filtering.
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