The author might be missing the fact that generative models can be used for "old-school" prediction tasks, with quite outstanding results. Their power does not only lie in their ability to _generate_ new data, but to _model_ existing data.
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Generative AI is overrated, long live old-school AI
31–40 of 192 posts
Re: Generative AI is overrated, long live old-school AI
#32>investors have become only interested in companies building generative AI, relegating those working on predictive models to “old school” AI. If that is the definition of old school AI, I wonder how symbolic AI should be named.
> If that is the definition of old school AI
It is not. Symbolic, deductive reasoning engines have the same claim to being old-school AI as predictive statistic models.
Re: Generative AI is overrated, long live old-school AI
#33>investors have become only interested in companies building generative AI, relegating those working on predictive models to “old school” AI. If that is the definition of old school AI, I wonder how symbolic AI should be named.
how about "useless with no successes of note" AI?
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Re: Generative AI is overrated, long live old-school AI
#34>investors have become only interested in companies building generative AI, relegating those working on predictive models to “old school” AI. If that is the definition of old school AI, I wonder how symbolic AI should be named.
how about "useless with no successes of note" AI?
I hope you've never used the power grid or parcel shipping, as those are heavily optimized using symbolic AI.
Re: Generative AI is overrated, long live old-school AI
#35I m not sure it's overrated, but the concerns are very real. We love the model because it speaks our language as if it's "one of us", but this may be deceiving, and the complete lack of model for truth is disturbing. Making silly poems is fun but the real uses are in medicine and biology, fields that are so complex that they are probably impenetrable to the human mind. Can Reinforcement learning alone create a model…
I am not so sure,
there seems to be accumulating evidence that "finding the optimal solutions" means (requires) building a world model. Whether it's consistent with ground truth probably depends on what you mean by ground truth.
Given the hypothesis that the optimal solution for deep learning presented with a given training set, is to represent (simulate) the formal systemic relationships that generated that set, by "modeling" such relationships (or discovering non-lossy optimized simplifications),
I believe an implicit corollary, that the fidelity of simulation is only bounded by the information in the original data.
Prediction: a big enough network, well enough trained, is capable of simulating with arbitrary fidelity, an arbitrarily complex system, to the point that lack of fidelity hits a noise floor.
The testable bit of interest being whether such simulations predict novel states and outcomes (real world behavior) well enough.
I don't see why they shouldn't, but the X-factor would seem to be the resolution and comprehensiveness of our training data.
I can imagine toy domains like SHRDLU which are simple enough that we should be able to build large models well enough already to "model" them and tease this sort of speculation experimentally.
I hope (assume) this is already being done...
Re: Generative AI is overrated, long live old-school AI
#36after the era of low hanging fruits of generative AI will be over I'm sure there will be a return to other approaches
Re: Generative AI is overrated, long live old-school AI
#37The author might be missing the fact that generative models can be used for "old-school" prediction tasks, with quite outstanding results. Their power does not only lie in their ability to _generate_ new data, but to _model_ existing data.
The biggest issue with using them in this way is how alien the failure modes are. Interpretable models with transparent loss functions are easy to grok. How LLMs might fail on a classic task is (afaict right now) difficult to predict.
What is not transparent in the cross-entropy loss used in a large number of deep nets?
Re: Generative AI is overrated, long live old-school AI
#38I’m not sure I understand a definition of AI that doesn’t include the ability to generate things.
You can use AI to validate things, i.e. to check that they conform to some specification.
You may twist the language to say that they are generating a list of validations and errors, but even then it's definitely a different use case than merely creating new items.
Re: Generative AI is overrated, long live old-school AI
#39Seems 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".
I'm curious about your work, because I worked on something similar during my grad school. What kind of applications in industry do you use deep learning systems for? Process control?
Re: Generative AI is overrated, long live old-school AI
#40Seems 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".
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.