Is it just me, or is AI never capitalized in TFA?
Every time I encountered the word while reading it was like a cache miss for my brain...
An understanding of AI’s limitations is starting to sink in
51–60 of 403 posts
Re: An understanding of AI’s limitations is starting to sink in
#52These days, you can translate text by pointing your phone at it and taking a picture. Thirty years ago, this would have been unambiguously AI, because it would have been not only impossible, but stupid impossible like something out of a soft SF novel where little self-flying robots deliver stuff to your house, or you can ask a computer a question in a natural voice and reasonably expect a civil, natural-language answ…
a.k.a. "the AI effect", Larry Tesler (mis)quoted by Douglas Hofstadter.
Re: An understanding of AI’s limitations is starting to sink in
#53The 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, humidity etc sensors to existing machines and collect the data and send it back.
The management thought we'd be able to apply machine learning on the data to get "business insights" from the all-powerful machine. They didn't know what these insights might be, just that ML/AI would generate them and therefore make the business a fuck-ton of money because AI generated novel new "business insights" that no one had thought of before and so transform the business. They thought it was just a magic box that would generate unbounded magic answers for their needs by passing in just some temperature and humidity readings or whatever, and then it would tell them they need to make more brown bread and less bagels in the North East region etc.
In reality, as I understand it, currently ML/AI requires us to know what the possible answers can be before we even begin training the network. So the classic example is it needs to know that the possible MNIST digits are 0-9, or that you are looking for one of 100 image classes etc.
You cant train a network with the MNIST digits, and then have that network tell you what shares to buy or sell.
Sure you can lop off the final layer and repurpose some of the middle layers, but you still need to train it to classify the inputs into categories you define up front. It won't give you a novel answer that you have not trained it for.
... at least that is how I understand it. Things may have changed over the past 5 years or so.
That said, I do agree there have been some cool things lately like machine vision etc. I don't think it will be that huge an industry though - it feels like a lot of it is largely just commoditised now (which is good) and it will be just like any other library you pick - like picking a UI framework for a web app. Just pick up a pre-trained network from modelzoo and get on with your real business requirements for 99% of people using ML, while the other 1% (at FAANGs et al) and academia churn out new models.
Re: An understanding of AI’s limitations is starting to sink in
#54I'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…
Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?
"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.
Re: An understanding of AI’s limitations is starting to sink in
#55Earlier quoted context omitted.
Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. Same for ML powered solutions to data management - I don't want to say enough to identify anything. My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans. And for perspective, AlphaGo…
since you’re working on the field : i’m still under the impression that modern ML (neural network in particular) are not producing any science, nor are they producing any level of understanding of the phenomenon it’s trying to model. And that as such, we can’t provide a reliable estimate of the limitations of the produced model, other than by feeding it tons of inputs and measure the result. It would be like trying t…
Some of this would be considered a feature by companies. It's more defensible to have unknowable AI deciding to do illegal things than programmers hard coding illegal things. Which really boggles my mind. When my kid does something illegal I'm held liable. When an ML algorithm programmed by a team of people does, nothing we can do about that!
Re: An understanding of AI’s limitations is starting to sink in
#56The 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…
Things may have changed over the past 5 years or so.
Things may have changed over the past 5 weeks or so with GPT-3.
Re: An understanding of AI’s limitations is starting to sink in
#57Like others are saying the progress towards AGI isn’t great but each individual subdomain is seeing great advances all the time. Object detection, facial recognition and NLP with GPT are much better than they were a few years ago. Each of these can provide business value to a certain degree, but I would agree that something resembling AGI holds the most business value. For this to happen all of the pieces have to be put together somehow - right now research focuses on specific subdomains and improves the SOTA on them. Once someone figures out how to make everything work together, it could mean a second, much larger wave of AI. So the question is, when will that happen?
Re: An understanding of AI’s limitations is starting to sink in
#58What’s the next big thing after deep learning?
That's a very good question. Robot manipulation in unstructured situations is still not very good. (See the videos of the DARPA humanoid challenge) "Common sense", defined as the ability to predict the consequences of actions and to use that to plan, hasn't progressed much in years. Machine learning doesn't seem to have helped much with either, so far. Those are key areas for doing physical things in the real world.…
Moravec's paradox
Low-level seriomotor intelligence requires extreme computational resources.
https://towardsdatascience.com/why-math-is-easy-for-ai-but-g...
Re: An understanding of AI’s limitations is starting to sink in
#59We have also been watching these machine learning models for 6 months:
- increase the volatility in virtually every financial market they touched
- be exploited by adversarial learning networks to amplify funded propaganda as news
- use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines
These non-linear "function approximators" have absolutely unpredictable and insane non-linear behavior where learned information was non-existent or sparse.
God help us all if one of these artificial intelligence devices is driving the road and sees a red stop sign that is a square, rather than a hexagon.
Re: An understanding of AI’s limitations is starting to sink in
#60"The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input." I think this gets to the core of what is still a limitation of current technologies. Venturing into the unknown is still a deeply relevant task that seems unlikely to be replaced by computers anytime soon.
This is changing. In natural language processing just in the past couple weeks OpenAI wrote about their GPT3 model which can learn some tasks remarkably quickly, after only 1 or 2 examples. That model has extreme compute requirements, but it shows strong progress on performing never before seen tasks. There is still some steam left in the current deep learning boom.
https://arxiv.org/abs/2005.14165