41–50 of 192 posts
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As the old joke goes, "AI" is anything that doesn't work yet. Once an "AI" system becomes reliable, we quickly take it for granted and it no longer seems impressive or interesting. It's just a database. Or an image classifier. Or a chatbot.
Magic is just science we don’t understand yet.
I 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…
Was this ever in doubt? This has been the case forever (even before "AI"), and I thought it was well-established. The fidelity of the model is the core problem. What "AI" is really providing is a shortcut that allows the creation of better models.
But no model can ever be perfect, because the value of them is that they're an abstraction. As the old truism goes, a perfect map of a terrain would necessarily be indistinguishable from the actual terrain.
I mean, the only thing GPT does is predict the next word, which makes it not so different from a compression algorithm. And diffusion models (the image generating stuff) are essentially fancy denoisers.
Depending on how you assemble the big building blocks, you get generation or you get prediction.
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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?
I 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…
Without sensing/experiencing the world, there is no truth.
The only truth we can ever truly know, is the present moment.
Even our memories of things that we “know” that happened, we perceive them in the now.
Language doesn’t have a truth. You can make up anything you want with language.
So the only “truth” you could teach an LLM, is your own description of it. But these LLMs are trained on thousands or even million different versions of “truth”. Which is the correct one?
"So has generative AI been overhyped? Not exactly. Having generative models capable of delivering value is an exciting development. For the first time, people can interact with AI systems that don’t just automate but create an activity of which only humans were previously capable." Good answer but I feel that most users/people do not understand the difference between generative and predictive machine learning and tha…
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 learn -- and AGI specifically to be able to give the appearance of human learning.
I feel like we've turned this corner where the question now is, "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 fact that this has become the new bar is IMO one of the biggest tech changes in history. Not the biggest, but up there.
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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.
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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.
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
Earlier quoted context omitted.
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.
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
In other words, old-school expert systems.
Is there a fundamental difference? I mean, the only thing GPT does is predict the next word, which makes it not so different from a compression algorithm. And diffusion models (the image generating stuff) are essentially fancy denoisers. Depending on how you assemble the big building blocks, you get generation or you get prediction.