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He wanted thinkers in sociological endeavors to keep the abstracted “forms” of society and “formal” behavior of humans separate from concrete individual and actual behavior. That is to say, he believed sociology should not be concerned with the particular content and subject matter of social relationships but with the abstract configuration or manifestation of generalized or abstracted human relationships. For example, Simmel would have directed his students to study a given society and analyze such topics as competition, or distribution of labor as abstract forms within further abstracted “spheres” such as “economics,” or “religion,” and to not pay particular attention to the actual behavior of individuals interacting inside those spheres ==> forms. This latter activity, for Simmel, would properly be within the realm of other social scientists such as social psychologists, and (perhaps?) even psychiatrists.Here he is in his own words attempting to explain:“Geometric abstraction investigates only the spatial forms of bodies, although empirically these forms are given merely as the forms of some material content. Similarly, if society is conceived as interaction among individuals, the description of the forms of this interaction is the task of the science of society in its strictest and most essential sense.” [Quoted by Edies, Laura Desforth and Appelrouth, Scott, Sociological Study in the Classical Era: Text and Readings (2005, Pine Forge Press) at 246.]And here are a couple of teachers of sociology attempting to explain what that statement means:“To take a simple example, while a car tire, clock face, and nickel are different things or “contents” that serve different purposes (you wouldn’t want to look at your tire to find out what time it is), to the mathematician they all take the form of a circle and thus share spatial properties. On the other hand, although giving a wedding present, paying for music lessons, and volunteering at the local food coop are all motivated by different intentions or “contents,” to the sociologist they all take the form of an exchange relation and thus share interaction properties.” Edies and Appelrouth at 246.I like the analogy of looking at a river. For Simmel, the forms were the river as a concept and the banks that contained it. The contents would be the water, the flotsam in the water, and the waterlife that inhabited it.You should broaden your understanding of the many rivulets of thought that feed into a consideration of your question and others like it. You might start with the Edies and Appelrouth text referenced above, then consult the following:Coser, Ray A., Masters of Sociological Thought: Ideas in Historical and Social Context (Harcourt, Brace, Jovanovich 2012).Ray, Larry, Formal Sociology: The Sociology of Georg Simmel in Schools of Thought in Sociology, (Edward Elgar Pub, 1991).

Other animals are certainly intelligent. However, I think you mean, “why can’t they invent spaceships, understand how DNA works, etc?”. That’s because of how our brains are structured. The human brain is unique. While humans aren’t the biggest-brained animals on Earth (that title goes to elephants and whales), at 20–26 billion neurons, humans have the highest number of neurons in the cerebral cortex of any animal species. The human brain structure has helped us to survive for thousands of years.As an example, an elephant has 5.6 billion neurons in its cerebral cortex. In fact, the elephant has the highest number of neurons and the biggest brain of any land animal. An elephant brain weighs 10–11 lbs (4.54–4.99 kg) and contains 257 billion neurons, compared to 86 billion neurons and a weight of about 3 lbs (1.36 kg) for a human brain.But, despite having smaller brains, we have 26 billion neurons in our cerebral cortex. That’s about 4.6x as many neurons.Now, why is the cerebral cortex important? The cerebral cortex controls things like attention, perception, awareness, thought, memory, language, and consciousness.“In contrast, and building on the assumption that neurons are the basic information-processing units of the brain, we have proposed that it is not the degree of encephalization, but rather the combined absolute number of neurons in the cerebral cortex and cerebellum, regardless of brain or body size, that correlates best with cognitive abilities (Herculano-Houzel, 2009, 2011a).Compared to other primates, and to smaller-brained species such as rodents, the human brain has indeed a much larger number of neurons, both in the cerebral cortex and in the cerebellum (Herculano-Houzel, 2012); however, testing the hypothesis that absolute numbers of neurons correlate with cognitive abilities across species, including humans, requires determining the cellular composition of brains larger than the human brain.”It’s believed that, due to the larger number of neurons in the cerebral cortex, a human is more capable of engaging in abstract thought and more sophisticated cognitive processes. That may be correlated to human bipedalism. For example, humans are more likely to experiment and test hypotheses. We try to find ways to organize civilization. We actively consider how the world works and create abstract concepts and inventions like:Mathematics (Algebra, Calculus I/II/III, Statistics, Cartesian coordinate systems, etc)The sciences (meteorology, cardiology, neurology, astronomy, biology, geology, chemistry, taxonomy, paleontology, botany, thermodynamics, physics, etc)Sociopolitical concepts (democracy, monarchy, Divine Right of Kings, Manifest Destiny, capitalism, communism, socialism, economics, etc)Philosophical thoughts and writings (see Voltaire, John Locke, Rene Descartes, Langston Hughes, Malcolm X, Sun Tzu, and so on. The Art of War, Declaration of Independence, Declaration of the Rights of the Man and of the Citizen of 1789, etc)Advanced technology and software (smartphones, PCs, TVs, internet, social media, etc)Ground transportation and aviation (cars, trucks, SUVs, pickups, airplanes, space shuttles,Humans are more likely to put more thought into how to arrange society and understand the world around them. We’re more intuitive and are more likely to form and apply abstract concepts in real life than an elephant or any other animal is. We’re very inquisitive and curious animals. We really put our imaginations to work. That’s thanks to the way our brains are structured. The ability to consider abstract concepts is the one thing that distinguishes humanity.To sum it up, an elephant might think,“Hmm, I wonder how I can get that delicious orange hanging on this tree? Let me stand on this log and get it with my trunk. Or, I could knock it down. Ah, there we go! Now, I can eat.I’ll pass that onto my daughters. That way, they can learn how to survive and live long lives!”A killer whale might think,“I wonder how I can raise my calves to eat fish? I’ll train them to swim through the water and use their sharp teeth. Yes, this tactic works great! That’s how you hunt for fish.That’ll help them survive. Now, I’ll go play with them just for fun”.A human might think,“How does lightning occur? Based on the results of this experiment, it seems that there is a positive electrical charge on the ground and a negative electrical charge in the cloud.The charges get attracted to each other, and it seems to create a stepped leader, otherwise known as a lightning strike, sort of like how you’ll get zapped when you drag your feet on a rug and then touch a metal doorknob. Interesting! I bet there’s a lot of energy in electricity!”Hey, speaking of energy, this has potential. Maybe we could harness the power of electricity to turn on a lightbulb. Maybe we could use electric currents to power a car engine, a TV, a computer, and who knows what else? Just gotta figure out how to convert a fuel into electricity. Maybe fossil fuels will do it.Okay, we’ve gotta come up with a cleaner way of generating electricity. This is polluting the planet, and fossils don’t regenerate fast enough. Can water or solar power do the trick? What about geothermal energy? Or, biomass? We’re gonna have to figure this out.”In other words, humans are big-brained, super-intelligent apes.Now, to be fair, an elephant lives in a different environment from humans. An elephant brain helps it perform very different tasks, probably much better than a human brain could. As one example, an elephant has a better spatial memory than a human does. That helps an elephant recall, for example, the exact location of a watering hole that’s >12 miles away, months after it has visited it. I can’t do that. I doubt most people could. That’s what helps an elephant survive in the African savanna.Fun fact: Elephants and killer whales, for that matter, are known to have local dialects, depending on the region. They’re also capable of distinguishing between different human voices and sometimes make a conscious effort to imitate human voices. A beluga whale was once recorded imitating human voices, same with an Asian elephant (maybe as a way to communicate with us? Who knows?)Another fun fact: The elephant’s extinct cousin, the mammoth, was also a highly intelligent creature. Maybe being huge and having a large proboscis and large tusks is correlated with having a sophisticated, complex brain. Hmmm, maybe someone could test that correlation? (see what I mean? Only a human would think about what I just said)The Human Brain in Numbers: A Linearly Scaled-up Primate Brainhttps://pubmed.ncbi.nlm.nih.gov/24971054/

Recent findings offer insight into the neural circuitry of some of the building blocks that contribute, if not lead, to consciousness. Needless to say, the emerging picture is far from complete.Summary of findings (updated references with additional notes at end of this answer that I keep adding as new findings are published)Experimental observations of mammals including humans have revealed a navigation circuitry that uniquely encodes memory of a visited location. Neurons called “place cells” uniquely encode memory of locations.Figure 1 [1,2,3,4,5,6,19,21]The encoding of this memory is quite rich - it is not limited to just a memory of the location but the entire spatiotemporal context , that is, the sensory and motivational experience at that location (landmarks, objects, people, smell, reward etc.) including the temporal sequence of that experience.Figure 2 [1,7,8]While the encoding is rich in terms of the entire context being encoded, the encoding of each concept (e.g. the Simpsons, Jennifer Aniston) that is also stored in the same anatomical region where locations are stored is sparse, in that only a small set of neurons encode the memory of a concept. For example, a picture of "the Simpsons" will cause the firing of a small set of neurons. This memory, while sparse, is multimodal - a written or spoken word of “the Simpsons” , will, in addition to neurons that process auditory input, also cause the firing of the same set of neurons that fired when seeing a picture of the Simpsons. This memory is also invariant to some degree - different pictures of the Simpsons in different sizes and viewing angle will still cause the same set of neurons to fire. These sparse clusters of neurons that represent concepts are also linked to or intersect with related concept clusters, creating a semantic graph of neuron clusters. For example, some of the ”Jennifer Aniston” neurons may also fire when shown a picture “Lisa Kudrow” (a co-actor in a series "Friends"). Figure 3,Figure 6 [9]The encoding of memory at a location is not just a sequence of events/concepts, but also the time of the events and the intervals between them. An internally generated flexible timer mechanism stores the temporal order of events along with the time intervals (seconds to minutes) between them – the details of this timer are being studied.[10,11,12,18,23]This storage of experience can then be reactivated when a person later recalls that experience, even when not in the location where the experience occurred. The same neurons that fired when the experience was first encoded, fire again when free recall is done. This recall is made possible by internally traversing these memories, without the need for any external sensory input. The actual mechanism of this internally initiated playback or recall of encoded memories is being studied - it has been observed in humans and rats.Figure 4 [13,14,20,22,24]Evidence indicate this ability to mentally replay past experiences may be the same mechanism used to plan for the future (e.g. places to go back to find food, remember how long it is that seeds were stored away so as to decide if they are spoilt). Figure 5 [15,16]The neuronal mechanism of humans to "mentally travel" completely independent of any spatial context is not known yet (e.g. abstract logical thought process to derive a mathematical proof by a recall of first principles/axioms). It is also not known if the “navigation based circuitry” is involved in encoding abstract memories that do not have an anchoring location, though there is speculation that the navigation circuitry may be part of a more general engine for memory.[17]The representation of a location and the experience at that location, combined with the circuitry to navigate this memory or state space without any external sensory input enables free recall, mental travel, and future planning – perhaps some of the signature elements of what constitute consciousness.FiguresFigure 1. Place cells mapping in two different enviroments (a) Stepwise increase of grid spacing at successive dorsoventral levels of medial entorhinal cortex. Spatial autocorrelograms for four example cells (one per dorsoventral module). (b) Remapping of hippocampal place cells in two environments Memory, navigation and theta rhythm in the hippocampal-entorhinal system, Nature, 2013Figure 2 Two forms of navigation and their relationship to semantic and episodic memory (a) Path integration (also known as dead reckoning) is based on self-referenced information by keeping track of travel distances (time elapsed multiplied by speed) and direction of turns. Calculating translocation relative to the start location allows the animal to return to the start along the shortest (homing) path. (b) Map-based navigation is supported by the relationships among visible or otherwise detectable landmarks. A map is constructed by exploration (path integration). (c) Episodic memory is 'mental travel' in time and space referenced to self. (d) Semantic memory is explicit representation of living things, objects, places and events without temporal or contextual references. Semantic knowledge can be acquired through multiple episodes with common elements. We hypothesize that the evolutionary roots of episodic and semantic memory systems are the dead reckoning and landmark-based forms of navigation, respectively. Memory, navigation and thetarhythm in the hippocampal-entorhinal system, Nature, 2013Figure 3. Example of a neuron that with multimodal invarianceConcept cells: the building blocks of declarative memory functions, Nature Neuroscience, 2012Figure 4. Activation and recall of single neuron of an episode of the Simpsons. A single-unit in the right entorhinal cortex was activated during viewing and recall of an episode from the TV series The Simpsons. (A) Cell responses to a selection of 48 different episodes (movie clips) presented to the patient in three different viewing sessions (parts 1 to 3). For each clip, the corresponding raster plots (six trials, order of trials is from top to bottom) and post–stimulus time histogram (500-ms bins) are given. Vertical dashed lines indicate clip onset and offset (5 s apart); 5-s blank periods were presented occasionally within groups of successive clips and were used to calculate the baseline firing rate, denoted by a gray horizontal line. Red boxes indicate sustained responses. (B) Trial-by-trial response of the neuron. Order of clips is for the purpose of illustration; more intervening clips separated successive Simpsons clips in the actual experiment. Spike raster plot and instantaneous firing rate (spike train convolved with a Gaussian of the full width at half maximum of 1200 ms) are displayed together. (C) Free-recall session that followed the third viewing session (part 3). (Bottom) Sound amplitude of patient voice; (top) a spike raster plot and instantaneous firing rate; gray dashed line denotes the average firing rate during the recall session + 3 SD; numbered dots denote onset time of verbal report of recall events, corresponding to clip numbers in (A). Note the distinct elevation of firing rate just before the patient reported the recall of the Simpsonsclip (red arrow). (D) A 50-s window around the Simpsons recall event [blue area in (C)]. Patient’s words are below the bottom panel. Note that the cell’s firing rate rose significantly above baseline 1500 ms before onset of verbal report of the Simpsons clip and returned to baseline after more than 10 s.Internally generated reactivation of single neurons ... [Science. 2008]Figure 5. Cell assembly sequence, space and time tracking (a) During physical travel, successive assemblies of neurons (1 to n) respond sequentially owing to the changing constellation of environmental landmarks and/or proprioceptive information from the body (top). During mental travel, sequential activation is supported by self-organized patterning[14] Memory, navigation and theta rhythm in the hippocampal-entorhinal system, Nature, 2013Figure 6. Semantic graph made up of overlapping sparse collections of concept cellsConcept cells: the building blocks of declarative memory functions, Nature Neuroscience, 2012References1. Neural Activity in Human Hippocampal Formation Reveals the Spatial Context of Retrieved Memories, Science, 29 November 20132. Grid Cells and Neural Coding in High-End Cortices, Neuron, October 30, 20133. Space Bats: Multidimensional Spatial Representation in the Bat, Science, November 20134. Direct recordings of grid-like neuronal activity in human spatial navigation, Nature, 20135. What are the major differences between grid cells and place cells?6. A map of visual space in the primate entorhinal cortex, Nature, 20127. Context Prediction Analysis and Episodic Memory, Behavioral Neuroscience, October 20138. The global record of memory in hippocampal neuronal activity, Nature, 1999 9. Concept cells: the building blocks of declarative memory functions, Nature Neuroscience, 2012 10. What makes us tick? Functional and neural mechanisms of interval timing, Nature, 200511. Time finds its place in the Hippocampus, Neuron, 2013 12. Integrating what and when across the primate medial temporal lobe,Science,2011 13. Internally Generated Reactivation of Single Neurons in Human Hippocampus During Free Recall, Science, 200814. Internally generated cell assembly sequences in the rat hippocampus, Science, 200815. Memory, navigation and theta rhythm in the hippocampal-entorhinal system, Nature, 201316. The evolution of episodic memory, PNAS, 201317. Homeostatic regulation of memory systems and adaptive decisions, Hippocampus, 201318. A neural substrate in the human hippocampus for linking successive events, PNAS, 201019. Grid Cells and Neural Coding in High-End Cortices, Neuron, 201320. Hippocampal place cells, context, and episodic m... [Hippocampus. 2006]21. Path integration in mammals, Hippocampus, 200422. Play it again: reactivation of waking experience and memory, Cell, 201023. Theta Phase Precession in Hippocampal Neuronal Populations and the compression of temporal sequences,Hippocampus,199624. Retrieving Memories via Internal context Requires the Hippocampus ,Journal of NeuroScience,2004Updated additional referencesA recent lecture (Feb 2017) on the neuroscience of consciousness. Has many references embedded in the video that may be of value.2. 15 April 2018. A recent talk by Yoshua Bengio, where he discusses the notion of a “consciousness prior” which is a low dimensional representation layered on top of a high dimensional substrate (created from sensory input), with an attention mechanism that focusses on a particular subset of the low dimensional representations. The inspiration for experimenting with this form of architecture was drawn from our current understanding of consciousness - where we can only consciously remember about seven things despite the enormous memory capacity of our brain. It seems to imply while we have rich high dimensional substrate of memory/experience extracted from sensory input, layered on top of this, is perhaps a low dimensional layer with an attention mechanism focussing on a subset of them.Verbatim summary of his talk on disentangled representationsOne of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition.We review earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data. We argue that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning.We propose two priors which could be used by an agent acting in its environment in order to help discover such high-level disentangled representations of abstract concepts. The first one is based on the discovery of independently controllable factors, i.e., in jointly learning policies and representations, such that each of these policies can independently control one aspect of the world (a factor of interest) computed by the representation while keeping the other uncontrolled aspects mostly untouched.This idea naturally brings fore the notions of objects (which are controllable), agents (which control objects) and self. The second prior is called the consciousness prior and is based on the hypothesis that our conscious thoughts are low-dimensional objects with a strong predictive or explanatory power (or are very useful for planning). A conscious thought thus selects a few abstract factors (using the attention mechanism which brings these variables to consciousness) and combines them to make a useful statement or prediction. In addition, the concepts brought to consciousness often correspond to words or short phrases and the thought itself can be transformed (in a lossy way) into a brief linguistic expression, like a sentence.Natural language could thus be used as an additional hint about the abstract representations and disentangled factors which humans have discovered to explain their world. Some conscious thoughts also correspond to the kind of small nugget of knowledge (like a fact or a rule) which have been the main building blocks of classical symbolic AI.This, therefore, raises the interesting possibility of addressing some of the objectives of classical symbolic AI focused on higher-level cognition using the deep learning machinery augmented by the architectural elements necessary to implement conscious thinking about disentangled causal factors.3. What is Yoshua Bengio's new "Consciousness Prior" paper about?4. The consciousness prior Yoshua Bengio5. What is consciousness, and could machines have it?6. 20 Jan 2019 An informative conversation by Alex Friedman with Prof. Tomaso Poggio where he responds to the question “will intelligent systems of the future ultimately need to be conscious?”7. 8 July 2019Two recent videos - covering work by Jeff Hawkin’s recent work.Those of us who read Jeff Hawkin's book (On intelligence 2004) may think his interview with Lex Fridman is a rehash of the same ideas and skip it(I almost did). It has useful new information. Worth watching to the very end - entire 2+ hours of it. His team has made some findings recently some of which (at least the core ideas) is very likely(directly or indirectly) to be part of the next wave of AI in some form. Even if it doesn’t it would still be a contribution to how our brains work. Take for instance the very simple fact ( this has been known for a while) that every single organ's wiring in our brains has both sensory and motor connections - we learn and create world models concurrently by sensing and acting upon sensed input using the motor connections. Our current state of art models like Convnets or even the recent XLNets/BERT are all just about doing one half of what our brains do - just sensing and not both sensing and acting in a continuous way in a changing environment. Even though sensory and motor connections in each organ may make our brains look like a reinforcement learning model, our brain is more like a very smart RL model - we learn not to crash a car without even crashing a car once unlike current models that have to crash a car, at least in simulation, many many times, to learn not to crash. His teams work suggest small individual functional units in our neocortex act as mini models that have both sensory and motor connections in addition to some position information cells (like grid cells - there is some literature evidence suggestive of grid cells like code in neocortex) for reference position (this position could capture relative physical position in 3d space or could represent position in an abstract thought sequence). Each functional unit creates its own model of the world(e.g. auditory version of a word we hear along with the visual version of it when we watch a video) and all of them vote to create our percept of the world. Other contributions his group has made recently is the demonstrating the power of sparsity in simulations - both sparse inputs and sparse weights - that not only disentangles features but make it robust to noise (accuracy of current deep learning models drop rapidly with increased noise). There seems to many insights in their latest work that seem to intersect with Yoshua Bengio's work on sparse disentangled representations and Jeff Hintons capsule networks ( capsules have striking commonality to Jeff Hawkings work on cortical columns as self contained processing units with position information in a reference frame built in using grid cell like cells potentially) The philosophical questions in the end are just gravy to the already highly informative interview - but that also adds to making the whole video one of the best interviews by far by Lex.The interview with Lex FridmanThis is a more detailed dive into their work on cortical columns functioning as self-contained models with grid like cells functioning as reference point in some some space (physical or abstract logical space).This video goes into detail of the function of individual cells (pyramidal neurons) and the functioning of different layers, including the speculation of grid cell like elements being present in each column. There is some recent fMRI work that is suggestive of such gird cell like elements in cortex, but this is still remains highly speculative.Mapping of a non-spatial dimension by the hippocampal/entorhinal circuitRepresentation of space in the brainA map of visual space in the primate entorhinal cortex.Human entorhinal cortex represents visual space using a boundary-anchored gridOrganizing conceptual knowledge in humans with a gridlike codeSpace and Time: The Hippocampus as a Sequence Generator11 December 2019A recent talk by Prof. Yoshua Bengio where he expands on his work on the consciousness prior (see video above by Prof. Bengio). Essentially current machine learning models are good at fast unconscious learning (system 1 tasks) and lack the ability to perform slow (sequential system 2 tasks) conscious reasoning that our brains do. His hypothesis is that the brain has a large substrate of unconscious learning and attention mechanism facilitates a very low dimensional representation (sparse factor graph) to picked from that high dimensional space which then also plays a role in subsequent system 1 computation. Language is a bridge between system 1 and system 2 in that it is a low dimensional representation and is used to verbalize unconscious thoughts.—————-20 Jan 2020Machine learning models inspired by conscious task solving are an active area of research now. Deep learning beyond 2019