3 ms·

Degree of (and physio mechanisms regulating) plasticity are brain-region specific. Motor cortex is using a plasticity framework that supports 'procedural' memo

Degree of (and physio mechanisms regulating) plasticity are brain-region specific. Motor cortex is using a plasticity framework that supports 'procedural' memory formation. This type of learning is relatively slow and relies on small incremental changes (for ML people, maybe this is similar to having your nn regularization parameter cranked way up). Other areas of cortex can support 'declarative' memory formation. For example you can learn things like the definition of a new word (lang), features of a novel object (vis), where you parked your car outside your new apartment (spatial/episodic), within just a few seconds. For the control of prosthetic limbs, there is perhaps no better brain-computer interface than motor cortex. Despite its relatively laborious procedural-type learning framework, limb movement is precisely what PMC has been optimized for. Since we've been digging around PMC for a while and have managed to make these electronic prosthetics work well, it's only natural to see what other tasks we can perform with those PMC-embedded electrodes. We should however keep in mind, though, the human brain has a wondrous variety of subregions optimized via evolution to perform all manner of computations, to support some subtype of behavior or mental capacity, with varying degrees of task-specificity. All I'm really saying is that... we will likely find out that a PMC-computer interface is garbage at doing some of the things we imagine should be possible, like typing words into a text box using thoughts. While it's likely possible (motor cortex could think about moving virtual fingers typing on a keyboard, and the computer interface could pick up on these signal patterns), there is another set of brain regions better suited for this task that could be tapped-into. But yes, "Some Brain Regions Cling to Old Habits When Learning New Tricks".

tmuir 9y ago
"Other areas of cortex can support 'declarative' memory formation. For example you can learn things like the definition of a new word (lang), features of a novel object (vis), where you parked your car outside your new apartment (spatial/episodic), within just a few seconds." Can you provide other examples of this phenomenon? It seems like the ones you put forth here are all examples of relying on previous experience, which seems more on the "habitual" end of the spectrum, which is the articles claim in the general case. I can learn new word's definitions because I've spent a significant amount of time learning definitions to all sorts of words throughout my life. Thus, the process is very familiar to me, and is a habit. I have learned over the course of my life that novelties are by definition outliers. Just like all animals with a survival instinct, I've learned that strange things can be dangerous, so it is an effective heuristic to expect danger from strange things. Another heuristic I favor is that the best way to defang danger is to understand it enough to avoid its mechanisms. I have learned over the course of my driving experience that keeping track of where I parked my car is crucial to locating the car again in the future. Thus, through experience, I know that it is an important piece of information to remember. These three examples seem to fit perfectly well within the hypothesis of the article. They aren't new tricks. They are by definition the application of old tricks.
goldenkey 9y ago
So essentially, the holy grail of a purely "abstraction of an abstraction of an abstraction ad infinitum" AI is mostly a pipe dream? Everyone loves to hope that everything that matters can be built from [insert object here]. Physicists want strings. Programmers want s-expressions or combinators or [choose your poison]. And AI researchers want the post-TLU artificial neuron model to learn it all.. I have always thought that machine learning is the less likely path to artificial human-level intelligence. It makes more sense to create a universe. I know your laughing now - but please hear me out. A digital universe can be merely a bunch of cells on a grid with specified rules for state-space updates. Cellular automata are the most well known example ie. Game of Life. If we look at the requirements for evolution and the requirements for information stability and movement, we can then proceed to create a 2d universe, tinker enough until we get life. Information stability and movement is mostly just the entities of matter and energy, and their conversion and total conservation. The life may not be all that remarkable at first but we run the universe more cycles, throw more compute at it, and we can push the clock forward quite a large amount. The trick of course, is not to try to make OUR universe. Too much information. Too much compute power required. Too much space...likely without life -- so wasted resources for our goal. And lastly, too many layers - even going from bosons and fermions we have: Protons/Neutrons/Electrons/Atoms/Molecules/Amino Acids/Proteins/RNA/DNA/Single Cells/Multicellular Life Forms/Mammals/Primates/Humans. The last 3 could be considered as evolution - so not all have to be present at the same time. However, all the other abstractions are a real dimensionality crusher. Are they totally necessary for an intelligent being? I doubt it. Once we find the local cluster of life we are interested in, in our digital universe, we could even do procedural generation instead of full state-space updates. So we can speed up the evolution of our target even quicker. There's nothing I haven't said here but here's my blog post about this idea if you're interested in reading more: https://scrollto.com/life-a-universe-simulation/ https://scrollto.com/life-a-universe-simulation/
- pcnix 9y ago
  You might find this paper interesting, if you haven't heard of it already: https://www.simulation-argument.com/simulation.html https://www.simulation-argument.com/simulation.html

tmuir 9y ago

"Other areas of cortex can support 'declarative' memory formation. For example you can learn things like the definition of a new word (lang), features of a novel object (vis), where you parked your car outside your new apartment (spatial/episodic), within just a few seconds." Can you provide other examples of this phenomenon? It seems like the ones you put forth here are all examples of relying on previous experience, which seems more on the "habitual" end of the spectrum, which is the articles claim in the general case. I can learn new word's definitions because I've spent a significant amount of time learning definitions to all sorts of words throughout my life. Thus, the process is very familiar to me, and is a habit. I have learned over the course of my life that novelties are by definition outliers. Just like all animals with a survival instinct, I've learned that strange things can be dangerous, so it is an effective heuristic to expect danger from strange things. Another heuristic I favor is that the best way to defang danger is to understand it enough to avoid its mechanisms. I have learned over the course of my driving experience that keeping track of where I parked my car is crucial to locating the car again in the future. Thus, through experience, I know that it is an important piece of information to remember. These three examples seem to fit perfectly well within the hypothesis of the article. They aren't new tricks. They are by definition the application of old tricks.

goldenkey 9y ago

So essentially, the holy grail of a purely "abstraction of an abstraction of an abstraction ad infinitum" AI is mostly a pipe dream? Everyone loves to hope that everything that matters can be built from [insert object here]. Physicists want strings. Programmers want s-expressions or combinators or [choose your poison]. And AI researchers want the post-TLU artificial neuron model to learn it all.. I have always thought that machine learning is the less likely path to artificial human-level intelligence. It makes more sense to create a universe. I know your laughing now - but please hear me out. A digital universe can be merely a bunch of cells on a grid with specified rules for state-space updates. Cellular automata are the most well known example ie. Game of Life. If we look at the requirements for evolution and the requirements for information stability and movement, we can then proceed to create a 2d universe, tinker enough until we get life. Information stability and movement is mostly just the entities of matter and energy, and their conversion and total conservation. The life may not be all that remarkable at first but we run the universe more cycles, throw more compute at it, and we can push the clock forward quite a large amount. The trick of course, is not to try to make OUR universe. Too much information. Too much compute power required. Too much space...likely without life -- so wasted resources for our goal. And lastly, too many layers - even going from bosons and fermions we have: Protons/Neutrons/Electrons/Atoms/Molecules/Amino Acids/Proteins/RNA/DNA/Single Cells/Multicellular Life Forms/Mammals/Primates/Humans. The last 3 could be considered as evolution - so not all have to be present at the same time. However, all the other abstractions are a real dimensionality crusher. Are they totally necessary for an intelligent being? I doubt it. Once we find the local cluster of life we are interested in, in our digital universe, we could even do procedural generation instead of full state-space updates. So we can speed up the evolution of our target even quicker. There's nothing I haven't said here but here's my blog post about this idea if you're interested in reading more: https://scrollto.com/life-a-universe-simulation/ https://scrollto.com/life-a-universe-simulation/

pcnix 9y ago
You might find this paper interesting, if you haven't heard of it already: https://www.simulation-argument.com/simulation.html https://www.simulation-argument.com/simulation.html