Thanks for writing this up, it helps to read somebody else’s take on this interview.
My thought after listening to this talk is that it’s even worse (“worse” from an AI Risk perspective) than Hawkins implies because the brain relies on one or more weird kludges that we could probably easily improve upon once we figured out what those kludges are doing and why they work.
For example, let’s say we figured out that some particular portion of a brain structure or some aspect of a cortical column is doing what we recognize as Kalman filtering, uncertainty quantification, or even just correlation. Once we recognize that, we can potentially write our next AIs so that they just do that explicitly instead of needing to laboriously simulate those procedures using huge numbers of artificial neurons.
I have no idea what to make of this quote from Hawkins, which jumped to me when I was listening and which you also pulled out:
“Real neurons in the brain are time-based prediction engines, and there’s no concept of this at all” in ANNs; “I don’t think you can build intelligence without them”.
We’ve had neural network architectures with a time component for many many years. It’s extremely common. We actually have very sophisticated versions of them that intrinsically incorporate concepts like short-term memory. I wonder if he somehow doesn’t know this, or if he just misspoke, or if I’m misunderstanding what he means.
We’ve had neural network architectures with a time component for many many years. It’s extremely common. We actually have very sophisticated versions of them that intrinsically incorporate concepts like short-term memory. I wonder if he somehow doesn’t know this, or if he just misspoke, or if I’m misunderstanding what he means.
I assume you’re talking about LSTMs and similar. I think you are misunderstanding what he means. I assumed he was referring to this:
If the neuron is triggered to fire (due to the first type of synapses, the ones near the cell body), and has already been prepared by a dendritic spike, then it fires slightly sooner, which matters because there are fast inhibitory processes, such that if a neuron fires slightly before its neighbors, it can prevent those neighbors from firing at all.
In other words, the analogy here might not be LSTMs, but a multithreaded program where race conditions are critical to its operation :(
EDIT: Maybe not, actually; I missed this part: “This allows networks of neurons to do sophisticated temporal predictions”. My new guess is that he’s referring to predictive processing. I assume self-supervised learning is the analogous concept in ML.
Hmm, it’s true that a traditional RNN can’t imitate the detailed mechanism, but I think it can imitate the overall functionality. (But probably in a computationally inefficient way—multiple time-steps and multiple nodes.) I’m not 100% sure.
Thanks for writing this up, it helps to read somebody else’s take on this interview.
My thought after listening to this talk is that it’s even worse (“worse” from an AI Risk perspective) than Hawkins implies because the brain relies on one or more weird kludges that we could probably easily improve upon once we figured out what those kludges are doing and why they work.
For example, let’s say we figured out that some particular portion of a brain structure or some aspect of a cortical column is doing what we recognize as Kalman filtering, uncertainty quantification, or even just correlation. Once we recognize that, we can potentially write our next AIs so that they just do that explicitly instead of needing to laboriously simulate those procedures using huge numbers of artificial neurons.
I have no idea what to make of this quote from Hawkins, which jumped to me when I was listening and which you also pulled out:
We’ve had neural network architectures with a time component for many many years. It’s extremely common. We actually have very sophisticated versions of them that intrinsically incorporate concepts like short-term memory. I wonder if he somehow doesn’t know this, or if he just misspoke, or if I’m misunderstanding what he means.
I assume you’re talking about LSTMs and similar. I think you are misunderstanding what he means. I assumed he was referring to this:
In other words, the analogy here might not be LSTMs, but a multithreaded program where race conditions are critical to its operation :(
EDIT: Maybe not, actually; I missed this part: “This allows networks of neurons to do sophisticated temporal predictions”. My new guess is that he’s referring to predictive processing. I assume self-supervised learning is the analogous concept in ML.
Hmm, it’s true that a traditional RNN can’t imitate the detailed mechanism, but I think it can imitate the overall functionality. (But probably in a computationally inefficient way—multiple time-steps and multiple nodes.) I’m not 100% sure.