Agreed that narratives are important, and they are often being spun to be used against the common people. Agreed that AGI is probably being used to some degree as a marketing tactic, or at least being oversold or overhyped, especially without recognizing risks and issues enough.
On the other hand, I think we run the risk of over-simplifying when we try to put forward any one single narrative. We don't want to just replace a (false) narrative that AI will help us all with another, false, over-simplified narrative that AI is just a gimmick, or that AGI is not a buildable technology that is somewhere on the horizon—and possibly somewhat close, at that.
AGI is not a buildable Technology. And it is not close. AGI as defined in the article is a term that defines a machines usefulness by comparing it to human capabilities. It’s always been a nonsense. Comparing humans to computers to humans is idiotic. It’s like comparing a human farmer to a combine harvester. Machines especially LLMs are fast, can process in bulk and are dumb. They crunch floating point operations at massive scale. But. The “P” in GPT means pre-trained. They’re out of date by the time they’re deployed. They average, and predict next tokens. That’s it. There’s no path to “Data” from Star Trek here.
I'm not sure if there would be a very productive discussion to be had here, as it sounds like we have pretty fundamental disagreements that would be hard to unpack in a Substack comment section. But what is a human? A body driven by a brain and a nervous system. What is a brain? It is a biological machine that came about through the very, very long processes of evolution. Brains are machines.
Are brains comparable to AI programs? Well, functionally, of course they are. Just like we can compare a calculator to a human mathematician, even though their similarities are mainly only superficial, or algorithmic.
But I think you're questioning if they're comparable (or if they ever would be) on a more meaningful level. Here, again, you need to look at neuroscience research. There, you find that the current leading models of the most important large scale brain regions, like the visual cortex, are deep neural network programs, closely related to commercial AI programs.
Now, most neuroscientists do not agree that deep neural network models of the visual cortex or the language network are comparable to cortical simulations in their realism. But many do, and there is a very substantial body of evidence to support such an interpretation. I wrote a book about it.
Visual cortex models - computer vision is done by convolutional neural networks - CNNs - and they are classifiers. They’re not LLMs. CNNs are not proposed as a path to AGI as LLMs are. My award winning funded AI startup used CNNs.
CNNs are artificial neural networks, as are language models (usually on transformer architectures). Language models are the leading models for the language cortex amongst computational and cognitive neuroscientists. This is just the research; not a personal opinion. I encourage you to take a look. It's very interesting and indeed, has been groundbreaking to discover that these models wind up having extremely deep correspondences with large scale brain regions.
Agreed that narratives are important, and they are often being spun to be used against the common people. Agreed that AGI is probably being used to some degree as a marketing tactic, or at least being oversold or overhyped, especially without recognizing risks and issues enough.
On the other hand, I think we run the risk of over-simplifying when we try to put forward any one single narrative. We don't want to just replace a (false) narrative that AI will help us all with another, false, over-simplified narrative that AI is just a gimmick, or that AGI is not a buildable technology that is somewhere on the horizon—and possibly somewhat close, at that.
AGI is not a buildable Technology. And it is not close. AGI as defined in the article is a term that defines a machines usefulness by comparing it to human capabilities. It’s always been a nonsense. Comparing humans to computers to humans is idiotic. It’s like comparing a human farmer to a combine harvester. Machines especially LLMs are fast, can process in bulk and are dumb. They crunch floating point operations at massive scale. But. The “P” in GPT means pre-trained. They’re out of date by the time they’re deployed. They average, and predict next tokens. That’s it. There’s no path to “Data” from Star Trek here.
I'm not sure if there would be a very productive discussion to be had here, as it sounds like we have pretty fundamental disagreements that would be hard to unpack in a Substack comment section. But what is a human? A body driven by a brain and a nervous system. What is a brain? It is a biological machine that came about through the very, very long processes of evolution. Brains are machines.
Are brains comparable to AI programs? Well, functionally, of course they are. Just like we can compare a calculator to a human mathematician, even though their similarities are mainly only superficial, or algorithmic.
But I think you're questioning if they're comparable (or if they ever would be) on a more meaningful level. Here, again, you need to look at neuroscience research. There, you find that the current leading models of the most important large scale brain regions, like the visual cortex, are deep neural network programs, closely related to commercial AI programs.
Now, most neuroscientists do not agree that deep neural network models of the visual cortex or the language network are comparable to cortical simulations in their realism. But many do, and there is a very substantial body of evidence to support such an interpretation. I wrote a book about it.
Visual cortex models - computer vision is done by convolutional neural networks - CNNs - and they are classifiers. They’re not LLMs. CNNs are not proposed as a path to AGI as LLMs are. My award winning funded AI startup used CNNs.
CNNs are artificial neural networks, as are language models (usually on transformer architectures). Language models are the leading models for the language cortex amongst computational and cognitive neuroscientists. This is just the research; not a personal opinion. I encourage you to take a look. It's very interesting and indeed, has been groundbreaking to discover that these models wind up having extremely deep correspondences with large scale brain regions.