Everything net
Everything relevant to the thought
... In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. ...
... In this work, we explore the validity and potential application of this seemingly intuitive link between consciousness and intelligence. ...
... We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. ...
... With this insight, we turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. ...
... We believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function. ...
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... [https://www.inc.com/bill-murphy-jr/how-emotionally-intelligent-people-use-send-a-bible-rule-to-become-remarkably-more-memorable.html](https://www.inc.com/bill-murphy-jr/how-emotionally-intelligent-people-use-send-a-bible-rule-to-become-remarkably-more-memorable.html) ...
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... Newtonian mechanics is also governed by algebraic equalities, so it is symmetric, telling us that f=ma is as valid as a=f/m. Where causality comes in is when we venture to model the 1/2 https://t.co/AmgYUOenjq ...
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... Yes, I know the HODLers see it as a buying opportunity, and they could be right — not doing price predictions, just trying to think this through 1/ First: crypto faithful comparing this to "crypto winter" of 2017-18, which was comparable in percentage terms. ...
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... In the Austrian theory, a recession is a process of resource re-allocation from capital-intensive to capital-light industries. Society needs to produce fewer factories and machine tools and more beer and pairs of pants (or whatever). ...
... In the Austrian theory, it’s important that central banks don’t interrupt this process by pushing interest rates back down to unnaturally low levels, because that interferes with this necessary re-allocation process. Ok so let’s think about 2006 to 2009. ...
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... It's hard to imagine higher-level cognitive faculty without some form of hierarchical information processing. ATB proposed that such a hierarchy corresponds to the hierarchy of objects in the real world. This might be a bit too speculative. Columns learn from prediction errors. ...
... They can predict raw sensory inputs; they can also predict signals by other columns produced from sensory inputs. Thus, learning can happen when there are raw sensory input prediction errors as well as when there are other column signal prediction errors. ...
... Learning in columns can easily be hierarchical - naturally, models (or knowledge) learned from them are hierarchical. That being said, there is no reason to believe that the hierarchy is a neat pyramid with clear-cut division between layers. ...
... Any column can learn from any other columns as long as their signals are useful. It's just that learning, and thus models, can happen orders away from raw input signals. ...
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... The controlled hallucination process is also a hypotheses-prediction-observation correction learning process. Brains can learn brand new concepts by generating new hypotheses about the world, making predictions about the sensory input, and correcting according to observation (actual sensory input) ...
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... Suggested in [Being You](https://www.anilseth.com/being-you/), the perception process is a top-down "controlled hallucination." The brain predicts conceived "things" it will see and then "the sensory input" caused by them. ...
... Then the brain verifies or corrects the conceived things with the sensory input it actually receives. ...
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... It enables the learning cycle of observations -> hypotheses -> predictions -> correct with observations. This can be argued for [philosophically](https://www.themind.net/hypotheses/W2wRBi5mSeGueEYevUjMzw) and [neuroscientifically](https://www.themind.net/hypotheses/M4p8C9lOTRu8ipf5zGtEJA). ...
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... They learn by predicting incoming raw sensory inputs as well as classification signals from other columns. One column's prediction error helps its own learning, as well as other columns' learning when the prediction was made with input from their classification signals. ...
... **ChatGPT**: Your hypothesis describes a process that is similar to how the brain is thought to process and classify sensory information. Cortical columns, which are groups of neurons in the cerebral cortex, are thought to play a role in processing and classifying sensory information. ...
... They do this by receiving input from sensory organs and then making predictions about what this input represents. These predictions are then compared to the actual input, and any discrepancies between the two are used to update the column's understanding of the sensory information. ...
... It is also thought that cortical columns communicate with each other, sharing information about the sensory input they have received and the predictions they have made. ...
... This can help to improve the overall accuracy of the predictions made by the brain, as different columns may have different strengths and areas of expertise in processing different types of sensory information. ...
... Overall, your hypothesis captures some of the key principles of how the brain processes and classifies sensory information, and it is generally consistent with current understanding of this process. ...
... However, it is important to note that the brain is a complex and multifaceted organ, and our understanding of how it works is still evolving. There may be additional factors and processes at play that are not captured in your hypothesis. ...
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