Answers.Body: <p>The biologically evolved and fine-tuned brain is not just a network of neurons that learn in a straightforward short-duration way, but a highly dynamic, self-organizing, and emergent system. There are feedback loops, parallel processing, and localized computations that all combine to create a vastly powerful system of learning and decision-making. Consciousness, formal understanding, emotions, and executive functions such as high-level planning are also key aspects of cognition that traditional neural networks don't yet model effectively, and these aspects might play an important role in human intelligence.</p>
  <p><a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)" rel="nofollow noreferrer">Active learning</a> where a system seeks out its own data or makes decisions based on prior experiences is a much more efficient and much computationally lower intense mechanism that has yet to be fully integrated into current ML algorithms, and this brain-machine gap might explain why humans learn so efficiently in real-world environments. Recently there's a new neuro-cognitive principle called <a href="https://www.ox.ac.uk/news/2024-01-03-study-shows-way-brain-learns-different-way-artificial-intelligence-systems-learn" rel="nofollow noreferrer">prospective configuration</a> with its corresponding newly developed mathematical model showing in computer simulations that models employing this prospective configuration can learn faster and more effectively than ANNs in tasks that are typically faced by animals and humans in nature.</p>
  <blockquote>
  <p>The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks... There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.</p>
  </blockquote>
  
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Answers.CreationDate: 2024-12-12T21:25:20.103Z
Answers.LastActivityDate: 2024-12-12T21:25:20.103Z
Answers.UserDisplayName: cinch
Body: <p>I've arrived at a very puzzling conundrum.  When I first looked into AI, machine learning, neural networks and the like, I was under the impression they haven't matched the brain's capability because we didn't have the algorithm or processing ability that the brain has.</p>
  <p>However, what I've found in my research is that especially in neural networks, there has been an enormous effort to base the original learning algorithm on neurologically realistic mechanisms.  The neural network itself is derived from the McCulloch-Pitts mathematical model of a whale neuron, which became the first perceptron.  The insight in McCulloch-Pitts' original paper is that the summing and activation function of a neuron is equivalent to an AND clause in a boolean formula, and consequently capable of representing simple concepts.  Other attempts at neurological realism are Hebbian networks, spiking networks, and recurrent networks.</p>
  <p>In all these efforts, the consistent pattern is the following:</p>
  <ol>
  <li>rigorous attempt to create computational learning algorithm from neurological mathematical model</li>
  <li>algorithm does not work very well, so it is modified until it does work very well</li>
  <li>end result is an algorithm that has no similarity at all to the original neurological model</li>
  </ol>
  <p>In general, the more an algorithm matches what we know about the brain, the worse it works.  Example, Hebbian learning, spiking networks, and recurrent neural networks.  Conversely, the better an algorithm works, the less it functions according to any known brain based mechanism.  Example, backpropagation and the transformer architecture.</p>
  <p>Consequently, modern research into effective learning algorithms no longer seeks to copy mechanisms in the brain.  There is still some degree of seeking inspiration, but literal translations of brain mechanism into algorithms is no longer considered a source of effective algorithms.  This indicates that the brain uses mechanisms that are inferior to the learning algorithms we have invented.</p>
  <p>This would be all fine and dandy if our computer algorithms could dominate human minds in every field.  However, we've only been able to create dominant algorithms within very narrowly defined fields.  The broader the field, the less dominant.  Additionally, whenever we do have a dominant algorithm, it is essentially cheating, by using enormous amounts of training data, processing, and power that the human mind does not seem to use.  Whatever mechanism the human mind uses, it seems to be both vastly more efficient and effective than our best algorithms.</p>
  <p>And thus I arrive at a weird discontinuity.  Our brains seems to use very inferior learning mechanisms.  Our effective algorithms are much, much better in terms of mechanism, yet they cannot match the capability of the human mind.  So, how is it possible that the human mind, built upon the vastly inferior mechanisms of the brain, yet vastly outperforms our best algorithms?  Do we have any kind of theory that addresses this bizarre gulf in our knowledge?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T19:27:32.473Z
Comments.Text: Interesting question! Do you have anything to substantiate your claim that *"In general, the more an algorithm matches what we know about the brain, the worse it works."*?
Comments.UserDisplayName: anderstood
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-12T20:02:21.447Z
Comments.Text: Added examples to substantiate my claim: Hebbian learning, recurrent neural networks, backpropagation, and transformer architecture.
Comments.UserDisplayName: yters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-13T00:11:07.133Z
Comments.Text: This question makes some assumptions, which might not be right, and unclear statements. For example, you say "Our effective algorithms are much, much better in terms of mechanism", what do you mean by mechanism here? Why do you assume that our brain has vastly inferior mechanism, if by mechanism you mean procedure to achieve intelligence? Note that our brains are not good at doing something that computers are very good at, e.g. calculation. Our brains are good at many different tasks and can specialise but still are able to be decent at many many different tasks.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-13T00:11:54.520Z
Comments.Text: Another questionable assumption is "there has been an enormous effort to base the learning algorithm on neurologically realistic mechanisms". I don't think that's true nowadays. There are some people that still try to create algorithms based on how our brain works, like Numenta (at least they used to do that a few years ago, now I don't know), but most research nowadays is not inspired by our brains.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-13T00:17:24.583Z
Comments.Text: @nbro right, modern research is focused on effectiveness, not neurological parity.  However, the past is a different story, such as neural networks being based on McCulloch-Pitts model of a neuron.  But, this supports my point, that the reason modern research doesn't seek neurological parity is because that leads to ineffective algorithms.  As for ineffective mechanisms in the brain, this is a corollary to the observation that research seeking effective algorithms ignores and significantly diverges from neurological parity.
Comments.UserDisplayName: yters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-13T00:25:06.790Z
Comments.Text: Ok, can you please edit your post to clarify that and fix the "questionable" assumptions/statements. Thanks.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-13T21:33:13.800Z
Comments.Text: Even if we grant the assumption that the basic mechanisms of the brain are inferior to some of the ones in modern neural nets, the brain has had a few hundred million years to do model selection over said inferior class of models, and it might take a bit of time for the better model classes to catch up.
Comments.UserDisplayName: Ray
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-14T14:12:11.190Z
Comments.Text: @Ray given the update takes a generation instead of nanoseconds, a million years is not as long as it appears.  Additionally, evolution is another natural process where the algorithmic corollary does not work at all like the biological inspiration, since the mechanics of the biological version are so ineffective.
Comments.UserDisplayName: yters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-16T16:05:03.313Z
Comments.Text: @yters An epoch of training takes a *lot* longer than nanoseconds. The exact duration depends on the training set size and the size of the network, but generally will be measured in seconds.  A back of the envelope estimate: if we assume 20 year generations over 500 million years, that's 25 million generations. If each epoch takes 1 second, that's about 300 days of training. If we instead take a generation to be 1 day (which would have been closer in the single-cell days (probably even less than that)), that's 6000 years of training. You're right about evolution being less efficient, though.
Comments.UserDisplayName: Ray
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-16T17:57:17.877Z
Comments.Text: @Ray exactly.  Very slow updates using an extremely noisy hill climber with very poorly defined fitness function compared with exact backpropagation with 64 bit fidelity and precise fitness functions.  No comparison.
Comments.UserDisplayName: yters
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-18T11:51:27.110Z
Comments.Text: I’m voting to close this question because it makes too many questionable assumptions and it's not easy to address the problems without certain clarifications or fixes
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-18T22:31:18.873Z
Comments.Text: Goodbye poor question, I tried my best.
Comments.UserDisplayName: yters
Tags: <machine-learning><neuroscience><computational-theory-of-mind>
Title: Since computer based learning is completely unlike how the brain learns, do we have a mathematical theory of brain based learning?
UserDisplayName: yters
ThreadText: # Since computer based learning is completely unlike how the brain learns, do we have a mathematical theory of brain based learning?
  
  (Asked by: username_0 on 2024-12-12)
  
  I've arrived at a very puzzling conundrum. When I first looked into AI, machine learning, neural networks and the like, I was under the impression they haven't matched the brain's capability because we didn't have the algorithm or processing ability that the brain has.
  
  However, what I've found in my research is that especially in neural networks, there has been an enormous effort to base the original learning algorithm on neurologically realistic mechanisms. The neural network itself is derived from the McCulloch-Pitts mathematical model of a whale neuron, which became the first perceptron. The insight in McCulloch-Pitts' original paper is that the summing and activation function of a neuron is equivalent to an AND clause in a boolean formula, and consequently capable of representing simple concepts. Other attempts at neurological realism are Hebbian networks, spiking networks, and recurrent networks.
  
  In all these efforts, the consistent pattern is the following:
  
  1. rigorous attempt to create computational learning algorithm from neurological mathematical model
  2. algorithm does not work very well, so it is modified until it does work very well
  3. end result is an algorithm that has no similarity at all to the original neurological model
  
  In general, the more an algorithm matches what we know about the brain, the worse it works. Example, Hebbian learning, spiking networks, and recurrent neural networks. Conversely, the better an algorithm works, the less it functions according to any known brain based mechanism. Example, backpropagation and the transformer architecture.
  
  Consequently, modern research into effective learning algorithms no longer seeks to copy mechanisms in the brain. There is still some degree of seeking inspiration, but literal translations of brain mechanism into algorithms is no longer considered a source of effective algorithms. This indicates that the brain uses mechanisms that are inferior to the learning algorithms we have invented.
  
  This would be all fine and dandy if our computer algorithms could dominate human minds in every field. However, we've only been able to create dominant algorithms within very narrowly defined fields. The broader the field, the less dominant. Additionally, whenever we do have a dominant algorithm, it is essentially cheating, by using enormous amounts of training data, processing, and power that the human mind does not seem to use. Whatever mechanism the human mind uses, it seems to be both vastly more efficient and effective than our best algorithms.
  
  And thus I arrive at a weird discontinuity. Our brains seems to use very inferior learning mechanisms. Our effective algorithms are much, much better in terms of mechanism, yet they cannot match the capability of the human mind. So, how is it possible that the human mind, built upon the vastly inferior mechanisms of the brain, yet vastly outperforms our best algorithms? Do we have any kind of theory that addresses this bizarre gulf in our knowledge?
  
  **Tags:** machine-learning, neuroscience, computational-theory-of-mind
  > **Comments:**
  > **username_1**: Interesting question! Do you have anything to substantiate your claim that *"In general, the more an algorithm matches what we know about the brain, the worse it works."*?  
  > **username_0**: Added examples to substantiate my claim: Hebbian learning, recurrent neural networks, backpropagation, and transformer architecture.  
  > **username_2**: This question makes some assumptions, which might not be right, and unclear statements. For example, you say "Our effective algorithms are much, much better in terms of mechanism", what do you mean by mechanism here? Why do you assume that our brain has vastly inferior mechanism, if by mechanism you mean procedure to achieve intelligence? Note that our brains are not good at doing something that computers are very good at, e.g. calculation. Our brains are good at many different tasks and can specialise but still are able to be decent at many many different tasks.  
  > **username_2**: Another questionable assumption is "there has been an enormous effort to base the learning algorithm on neurologically realistic mechanisms". I don't think that's true nowadays. There are some people that still try to create algorithms based on how our brain works, like Numenta (at least they used to do that a few years ago, now I don't know), but most research nowadays is not inspired by our brains.  
  > **username_0**: @nbro right, modern research is focused on effectiveness, not neurological parity.  However, the past is a different story, such as neural networks being based on McCulloch-Pitts model of a neuron.  But, this supports my point, that the reason modern research doesn't seek neurological parity is because that leads to ineffective algorithms.  As for ineffective mechanisms in the brain, this is a corollary to the observation that research seeking effective algorithms ignores and significantly diverges from neurological parity.  
  > **username_2**: Ok, can you please edit your post to clarify that and fix the "questionable" assumptions/statements. Thanks.  
  > **username_3**: Even if we grant the assumption that the basic mechanisms of the brain are inferior to some of the ones in modern neural nets, the brain has had a few hundred million years to do model selection over said inferior class of models, and it might take a bit of time for the better model classes to catch up.  
  > **username_0**: @Ray given the update takes a generation instead of nanoseconds, a million years is not as long as it appears.  Additionally, evolution is another natural process where the algorithmic corollary does not work at all like the biological inspiration, since the mechanics of the biological version are so ineffective.  
  > **username_3**: @yters An epoch of training takes a *lot* longer than nanoseconds. The exact duration depends on the training set size and the size of the network, but generally will be measured in seconds.  A back of the envelope estimate: if we assume 20 year generations over 500 million years, that's 25 million generations. If each epoch takes 1 second, that's about 300 days of training. If we instead take a generation to be 1 day (which would have been closer in the single-cell days (probably even less than that)), that's 6000 years of training. You're right about evolution being less efficient, though.  
  > **username_0**: @Ray exactly.  Very slow updates using an extremely noisy hill climber with very poorly defined fitness function compared with exact backpropagation with 64 bit fidelity and precise fitness functions.  No comparison.  
  > **username_2**: I’m voting to close this question because it makes too many questionable assumptions and it's not easy to address the problems without certain clarifications or fixes  
  > **username_0**: Goodbye poor question, I tried my best.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2024-12-12. Score: 1 | Accepted answer
  
  The biologically evolved and fine-tuned brain is not just a network of neurons that learn in a straightforward short-duration way, but a highly dynamic, self-organizing, and emergent system. There are feedback loops, parallel processing, and localized computations that all combine to create a vastly powerful system of learning and decision-making. Consciousness, formal understanding, emotions, and executive functions such as high-level planning are also key aspects of cognition that traditional neural networks don't yet model effectively, and these aspects might play an important role in human intelligence.
  
  [Active learning](https://en.wikipedia.org/wiki/Active_learning_(machine_learning)) where a system seeks out its own data or makes decisions based on prior experiences is a much more efficient and much computationally lower intense mechanism that has yet to be fully integrated into current ML algorithms, and this brain-machine gap might explain why humans learn so efficiently in real-world environments. Recently there's a new neuro-cognitive principle called [prospective configuration](https://www.ox.ac.uk/news/2024-01-03-study-shows-way-brain-learns-different-way-artificial-intelligence-systems-learn) with its corresponding newly developed mathematical model showing in computer simulations that models employing this prospective configuration can learn faster and more effectively than ANNs in tasks that are typically faced by animals and humans in nature.
  
  > The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks... There is currently a big gap between abstract models performing prospective configuration, and our detailed knowledge of anatomy of brain networks. Future research by our group aims to bridge the gap between abstract models and real brains, and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.
  
  
  ---
  
  