If an individual were temporarily infirm, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have dipped. This means that memories are not just important, but the relays to make them available. It is possible to define human memory not just by types but by specifications of relays for availability, such that those for reasoning at some point may be different from those from planning or those for learning from those from doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just by the memory present. Relays can also be used to explain how Large language models [LLMs] work. LLMs were trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to and outputs from LLMs can also be assumed to be relays across data. Simply put, LLMs work because they are able to relay predictively, with correct matches for several use cases. Though there are still weaknesses of these relays—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. AGI can be defined as relay equivalents to those in the human mind that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. The internet already has more information than any individual’s brain. However, AGI may be as good as relays that use the information for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features. Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features in sets, in clusters of neurons, across the central and peripheral nervous systems. Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spots, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly mechanize what is learned and experienced. Ranking how close to human intelligence AGI or ASI may come may depend on their parallel relays. AGI would not be a measure of outcomes but a measure of relays since outcomes may be great yet deficient. For example, LLMs are excellent at predictions but lack correction skills and knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about AGI are: Can it have control and subjectivity? In the human mind, control, free will, or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck, and so forth. There are functions that cannot be controlled, like the bronchioles and so forth, at least directly, like speaking, waving, and so on. Subjectivity is attachment, personalization, or self-association, away from detachment, depersonalization, or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same sets of signals that mechanize functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience for the self to present—they are also subcategories where relays get to and determine their intensities. It could be possible that rows within high-dimensional vectors become aspects of control, and some columns become aspects of subjectivity. For AGI, control and subjectivity would be data that can be used alone and has to be relayed for availability, unlike with humans. Simply, AGI would use data to control data and use data to have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature, The new NeuroAI, stating that, "Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience." There is a recent paper in Nature, Reservoir-computing based associative memory and itinerancy for complex dynamical attractors, stating that, "A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously, along with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary." If an individual were temporarily infirm, it is possible to assume that most of the memory the individual has might be intact, but the relays to make the memories available may have dipped. This means that memories are not just important, but the relays to make them available. It is possible to define human memory not just by types but by specifications of relays for availability, such that those for reasoning at some point may be different from those from planning or those for learning from those from doing. Simply, outputs from human memory are sometimes a result of reach or relays, not just by the memory present. Relays can also be used to explain how Large language models [LLMs] work. LLMs were trained on vast amounts of internet data. This training can be assumed to be relays. Prompts to and outputs from LLMs can also be assumed to be relays across data. Simply put, LLMs work because they are able to relay predictively, with correct matches for several use cases. Though there are still weaknesses of these relays—like confabulations, hallucinations, slops in generated videos and images, and so forth—the better the relays or math functions that are developed for digital memory, the closer they might come to human intelligence. might come Artificial general intelligence [AGI] or artificial superintelligence [ASI] could get closer with improved relays across digital memory, in parallel to relays that result in intelligence in the human mind. AGI can be defined as relay equivalents to those in the human mind that define human intelligence. Relays may also make and specify extensions like—levels of control and base subjectivity. The internet already has more information than any individual’s brain. However, AGI may be as good as relays that use the information for similarities to human intelligence, not just the presence of information. This is similar to the human mind, where presence [of memory] does not mean available [in an instance]. The human mind is theorized to have functions and features . Though functions are shaped by relays, features are dominated by relays. Functions can be categorized into memory, feeling, emotion, and modulation of internal senses. Functions have several subcategories including—intelligence, planning, reasoning and so forth. Functional instances are determined by how they are graded or qualified by features. Features are types of relays that bring those functions to use, including their intensities. The human mind is theorized to be the collection of all the electrical and chemical signals with their interactions and features in sets, in clusters of neurons, across the central and peripheral nervous systems. theorized to have functions and features electrical and chemical signals with their interactions and features Features [or relays] include pre-/prioritization, old and new sequences, early splits, principal spots, thick/thin sets, volume changes, diameter constant spaces, and others. These relays are postulated to directly mechanize what is learned and experienced. Ranking how close to human intelligence AGI or ASI may come may depend on their parallel relays. AGI would not be a measure of outcomes but a measure of relays since outcomes may be great yet deficient. For example, LLMs are excellent at predictions but lack correction skills and knowledge of consequences. They are cool at several types of pattern-matching but seem to be unidirectional in several outputs, which is not the case for the human mind. Two of several questions about AGI are: Can it have control and subjectivity? In the human mind, control, free will, or intent can be defined as functions that can be intentionally adjusted, like chewing, sitting, turning the neck, and so forth. There are functions that cannot be controlled, like the bronchioles and so forth, at least directly, like speaking, waving, and so on. Subjectivity is attachment, personalization, or self-association, away from detachment, depersonalization, or dissociation. Control [or intent] and subjectivity [or self] are postulated to be mechanized in the same sets of signals that mechanize functions. While control and subjectivity may not be described as standalone functions or information—such that something must often be controlled and there has to be an experience for the self to present—they are also subcategories where relays get to and determine their intensities. It could be possible that rows within high-dimensional vectors become aspects of control, and some columns become aspects of subjectivity. For AGI, control and subjectivity would be data that can be used alone and has to be relayed for availability, unlike with humans. Simply, AGI would use data to control data and use data to have subjectivity, conceptually. Conceptual brain science could be useful in grading LLMs to AGI, becoming another direction for computational and theoretical neuroscience to shape progress in AI. There is a recent editorial in Nature , The new NeuroAI , stating that, "Overall, the extent of the role of neuroscience in AI research, and that of AI in neuroscience research, remain open questions for the future. However, these two fields are deeply linked, and the exchange of ideas between them continues to evolve. The pursuit of more powerful artificial neural systems in leading AI research labs, particularly those affiliated with tech companies, is currently focused on engineering. This pursuit emphasizes further scaling up of complex architectures such as transformers, rather than integrating insights from neuroscience." Nature The new NeuroAI There is a recent paper in Nature , Reservoir-computing based associative memory and itinerancy for complex dynamical attractors , stating that, "A neural network capable of memorizing and retrieving multiple memory states is also a multifunctional neural network. When different states are recalled, the neural network exhibits distinct dynamical behaviors. The idea of multifunctional RNNs has been discussed previously, along with different forms of implementation. In particular, in the index-based approach, an index parameter is introduced to modulate the functionality of the neural networks and store different states with different index values, but recalling the memorized states was a challenge, for which control may be necessary." Nature Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

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Evaluation: What is the Neuroscience of Superintelligence, AGI—and LLMs?

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