This article could be improved by starting off stating what some examples of Predictive AI is, as they did with Generative AI.
Generative AI is overrated, long live old-school AI
91–100 of 192 posts
Re: Generative AI is overrated, long live old-school AI
#92The real innovation will come one someone uses a Generative AI to make something, and then use a predictive AI to rate it's accuracy, making it go again until it passes the predictive AI. Basically a form of adversarial training/generation.
Isn't this exactly how GANs work already?
Yes. But from I've seen no one has applied it to the latest Generative AIs.
Re: Generative AI is overrated, long live old-school AI
#93Earlier quoted context omitted.
This is just wrong. Also, even if a LLM could do that, so could a shell script, without the risks involved in using "AI" for it, or for now the ridiculous external dependence that would involve. I wonder if in 10 years people will be stuck debugging Rube-Goldberg machines composed of LLM api calls doing stuff that if-statements can do, probably cobbled together with actual if-statements
Sure, maybe you can use a shell script, but now the AI assistant can write it based on your verbal/text description, and then the assistant can also run it for you after you’ve checked it. What your are saying is: “why use the washing machine, if I my clothes are even cleaner when I wash them myself - I also spend less detergent and less water”. You are free to keep doing your laundry by hand. But I bet most people p…
I think you're fighting an uphill battle because of what you picked to defend here - shell scripts are very easy to write, and I have a hard time imagining a future where someone tells an LLM,
"Write me a shell script that runs run_control.py with the speed argument set to one hundred."
to get,
"./run_control.py --speed 100"
Re: Generative AI is overrated, long live old-school AI
#94Seems like the person who wrote the blog works in "classical" deep learning. So do I, so here's the fairest take I can come up with: "AI" has for recent memory been a marketing term anyway. Deep learning and variations have had a good run at being what people mean when they refer to AI, probably overweighting towards big convolution based computer vision models. Now, "AI" in people's minds means generative models. Th…
Spot on. I work with deep learning systems in industrial control, and generative models are simply ill-suited for this sort of work. Wrong tool for the job. But neither the traditional nor generative models are "AI" in the sense that normal people think when they hear "AI".
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Re: Generative AI is overrated, long live old-school AI
#95Re: Generative AI is overrated, long live old-school AI
#96Earlier quoted context omitted.
IMO, it has been underhyped. We're seeing things with LLMs that a decade ago I'd say was multiple decades out, if not more. We're just years into generative approaches. And I think we'll more combinations of methods used in the future. The goal of AI has never been to build an all knowing perfect system. It has also never been to replicate the way the human brain works. But its been to build an artificial system that…
> Can we build something that knows everything that has been documented and can also synthesize and infer all of that data at a level of a very smart human The word "know" is doing some heavy lifting there, as is "synthesize" and "infer".
By "know" I meant has access to. This is a very "database" sense of the word "know".
Now "infer" and "synthesize" I meant the standard human definition of "synthesize" and "infer". In my interactions with relatively bright people, they really expect ChatGPT to be able to synthesize text at the level of a very sharp HS/college student. They don't want simple regurgitation of a text or a middel school analysis -- they want/expect ChatGPT to analyze nuance, and pull in its vast database to make connections to things that maybe aren't apparent at first glance.
The bar has raised so high so quickly -- it's crazy.
Re: Generative AI is overrated, long live old-school AI
#97Earlier quoted context omitted.
Spot on. I work with deep learning systems in industrial control, and generative models are simply ill-suited for this sort of work. Wrong tool for the job. But neither the traditional nor generative models are "AI" in the sense that normal people think when they hear "AI".
To me what’s exciting about Chat/GPT type of tech, is that they can be the “coordinators” of other models. Imagine asking an AI assistant to perform a certain industrial control task. The assistant, instead of executing the task “itself”, could figure out which model/system should perform the task and have it do it. Then even monitor the task and check it’s completion.
The central question is that a controller is assumed to be specifiable and thus formally verifiable through model checking in principle.
With a neural network you have a black box and for example with ChatGPT it doesn't even have a specification. It turns the verification process upside down.
Re: Generative AI is overrated, long live old-school AI
#98Earlier quoted context omitted.
What is not transparent in the cross-entropy loss used in a large number of deep nets?
I think there was a breakdown in communication here. If I train a classic deep net as a classifier and there are 5 possible classes, it will only ever output those 5 classes (unless there's a bug). With ChatGPT, for example, it could theoretically decide to introduce a 6th class - what I would call an alien failure mode, even if you explicitly told it not to. I think formally / provably constraining the output of LLM…
Formal proof is problematic because English has no formal specification. Some people are working on this, it's a nascent area bringing formal methods (model checking) to neural network models of computation. But it's an interesting fundamental issue that arises there, if you can't even specify the design intentions then how do you prove anything about it.
Re: Generative AI is overrated, long live old-school AI
#99I’m not sure I understand a definition of AI that doesn’t include the ability to generate things.
> I’m not sure I understand a definition of AI that doesn’t include the ability to generate things. It depends how you define "generate." For example, is software that controls a robot arm generating anything? I guess it's generating the movements of the arm. But when people use the term "generative" with regards to machine learning models right now, they generally mean content—e.g. text or images for consumption.
Generative has a more technical meaning than that.
Generative AI is essentially the opposite of a classifier. You give it a prompt that could mean many different things, and it gives you one of those things. A robotic arm could use generative AI, because there are many different sets of electrical signals that would result in success for, say, catching a ball.
Classification is an example of a non-generative AI in that there is only 1 correct answer, but it still requires machine learning to acquire the classification function.
Re: Generative AI is overrated, long live old-school AI
#100Earlier quoted context omitted.
Isn’t most of the mathematics of AI old, as in really old? Regression, both linear and logistic are from the mid 1800s to early 1900s. Neural networks, at least the basics are from around 1950. What has really changed is the engineering, the data volume and the number of fields we can apply the mathematics to. The math itself (or what is the basis of AI) is really old.
backpropagation didn't get solved until the '80s, weirdly. before then people were using genetic algorithms to train neural networks. and it was only in the last decade that the vanishing gradients problem was tamed. my impression is that ML researchers were stumbling along in the mathematical dark, until they hit a combination (deep neural nets trained via stochastic gradient descent with ReLU activation) that worke…
Right, and the practice of neural networks has significantly overshot the mathematical theory. Most of the aspects we know work and result in good models have poorly understood theoretical underpinnings. The whole overparamiterized thing for example, or generalization generally. There's a lot that "just works" but we don't know why, thus the stumbling around and landing on stuff that works