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working memory slots (and types), a good size of visual field, resolution of color in terms of Just Noticeable Differences, and so on. If your algorithm doesn’t even try to “render” its knowledge in some information-rich format, then it may lack the internal representations needed to really “understand”. Put another way: imagine that your experience is like a Holodeck. Ask the question of what is the lower bound on the computational throughput of each sensory modality and their interrelationships. Then see if the algorithm you think can “understand” has internal representations of that kind at all. Steel-manning algorithmic concerns involves taking a hard look at the number of degrees of freedom of our inner world-simulation (in e.g. free-wheeling hallucinations) and making sure that there are implicit or explicit internal representations with roughly similar computational horsepower as those sensory channels. I think that this is actually an easy constraint to meet relative to the challenge of actually creating sentient machines. But it’s a bare minimum. You can’t let yourself be fooled by a lookup table. In practice, the AI researchers will just care about metrics like accuracy, meaning that they will use algorithmic systems with complex internal representations like ours only

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text:
if it computationally pays off to do so! (Hanson in Age of EM makes the bet it that it is worth simulating a whole high-performing human’s experience; Scott points out we’d all be on super-amphetamines). Me? I’m extremely skeptical that our current mindstates are algorithmically (or even thermodynamically!) optimal for maximally efficient work. But even if normal human consciousness or anything remotely like it was such a global optimum that any other big computational task routes around to it as an instrumental goal, I still think we would need to check if the algorithm does in fact create adequate internal representations before we assign sentience to it. Thankfully I don’t think we need to go there. I think that the most crucial consideration is that we can rule out a huge class of computing systems ever being conscious by identifying implementation-level constraints for bound experiences. Forget about the algorithmic level altogether for a moment. If your computing system cannot build a bound experience from the bottom up in such a way that it has meaningful holistic behavior, then no matter what you program into it, you will only have “mind dust” at best. What We Want: Meaningful Boundaries In order

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text:
to solve the boundary problem we want to find “natural” boundaries in the world to scaffold off of those. We take on the starting assumption that the universe is a gigantic “field of consciousness” and the question of how atoms come together to form experiences becomes how this field becomes individuated into experiences like ours. So we need to find out how boundaries arise in this field. But these are not just any boundary, but boundaries that are objective, frame-invariant, causally-significant, and computationally-useful. That is, boundaries you can do things with. Boundaries that explain why we are individuals and why creating individual bound experiences was evolutionarily adaptive; not only why it is merely possible but also advantageous. My claim is that boundaries with such properties are possible, and indeed might explain a wide range of puzzles in psychology and neuroscience. The full conceptually satisfying explanation results from considering two interrelated claims and understanding what they entail together. The two interrelated claims are: Topological boundaries are frame-invariant and objective features of physics Such boundaries are causally significant and offer potential computational benefits I think that these two claims combined have the potential to explain the phenomenal binding/boundary problem (of course assuming

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text:
you are on board with the universe being a field of consciousness). They also explain why evolution was even capable of recruiting bound experiences for anything. Namely, that the same mechanism that logically entails individuation (topological boundaries) also has mathematical features useful for computation (examples given below). Our individual perspectives on the cosmos are the result of such individuality being a wrinkle in consciousness (so to speak) having non-trivial computational power. In technical terms, I argue that a satisfactory solution to the boundary problem (1) avoids strong emergence(D. Chalmers 2006), (2) sidesteps the hard problem of consciousness(D. J. Chalmers 2007), (3) prevents the complication of epiphenomenalism, and (4) is compatible with the modern scientific world picture. And the technical reason why topological segmentation provides the solution is that with it: (1) no strong emergence is required because behavioral holism is only weakly emergent on the laws of physics, (2) we sidestep the hard problem via panpsychism, (3) phenomenal binding is not epiphenomenal because the topological segments have holistic causal effects (such that evolution would have a reason to select for them), and (4) we build on top of the laws of physics rather than introduce new clauses to account for

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what happens in the nervous system. In this post you’ll get a general walkthrough of the solution. The fully rigorous, step by step, line of argumentation will be presented elsewhere. Please see the video for the detailed breakdown of alternative solutions to the binding/boundary problem and why they don’t work. Holistic (Field) Computing A very important move that we can make in order to explore this space is to ask ourselves if the way we think about a concept is overly restrictive. In the case of computation, I would claim that the concept is either applied extremely vaguely or that making it rigorous makes its application so narrow that it loses relevance. In the former case we have the tendency for people to equate consciousness with computation in a very abstract level (such as “resource gathering” and “making predictions” and “learning from mistakes”). In the latter we have cases where computation is defined in terms of computable functions. The conceptual mistake to avoid is to think that just because you can compute a function with a Turing machine, that therefore you are creating the same inner (bound or not) physical states along the way. And while yes, it would be

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text:
possible to approximate the field behavior we will discuss below with a Turing machine, it would be computationally inefficient (as it would need to simulate a massively parallel system) and lack the bound inner states (and their computational speedups) needed for sentience. The (conceptual engineering) move I’m suggesting we make is to first of all enrich our conception of computation. To notice that we’ve lived with an impoverished notion all along. I suggest that our conception of computation needs to be broad enough to include bound states as possible meaningful inputs, internal steps and representations, and outputs. This enriched conception of computation would be capable of making sense of computing systems that work with very unusual inputs and outputs. For instance, it has no problem thinking of a computer that takes as input chaotic superfluid helium and returns soap bubble clusters as outputs. The reason to use such exotic medium is not to add extra steps, but in fact to remove extra steps by letting physics do the hard work for you. (source) To illustrate just one example of what you can do with this enriched paradigm of computing I am trying to present to you, let’s now consider the

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text:
hidden computational power of soap films. Say that you want to connect three poles with a wire. And you want to minimize how much wire you use. One option is to use trigonometry and linear algebra, another one is to use numerical simulations. But an elegant alternative is to create a model of the poles between two parallel planes and then submerge the structure in soapy water. Letting the natural energy-minimizing property of soap bubbles find the shortest connection between three poles is an interesting way of performing a computation. It is uniquely adapted to the problem without needing tweaks or adjustments – the self-organizing principle will work the same (within reason) wherever you place the poles. You are deriving computational power from physics in a very customized way that nonetheless requires no tuning or external memory. And it’s all done simply by each point of the surface wanting to minimize its tension. Any non-minimal configuration will have potential energy, which then gets transformed into kinetic energy and makes it wobble, and as it wobbles it radiates out its excess energy until it reaches a configuration where it doesn’t wobble anymore. So you have to make the solution of your

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text:
problem precisely a non-wobbly state! In this way of thinking about computation, an intrinsic part of the question about what kind of thing a computation is will depend on what physical processes were utilized to implement it. In essence, we can (and I think should) enrich our very conception of computation to include what kind of internal bound states the system is utilizing, and the extent to which the holistic physical effects of such inner states are computationally trivial or significant. We can call this paradigm of computing “Holistic Computing”. From Soap Bubbles to ISING-Solvers Meeting Schedulers Implemented with Lasers Let’s make a huge jump from soap water-based computation. A much more general case that is nonetheless in the same family as using soap bubbles for compute, is having a way to efficiently solve the ISING problem. In particular, having an analog physics-based annealing method in this case comes with unique computational benefits: it turns out that non-linear optics can do this very efficiently. You are in a certain way using the universe’s very frustration with the problem (don’t worry I don’t think it suffers) to get it solved. Here is an amazing recent example: Ising Machines: Non-Von Neumann Computing

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text:
with Nonlinear Optics – Alireza Marandi – 6/7/2019 (presented at Caltech). The person who introduces Marandi in the video above is Kwabena Boahen, with whom I had the honor to take his course at Stanford (and play with the neurogrid!). Back in 2012 something like the neurogrid seemed like the obvious path to AGI. Today, ironically, people imagine scaling transformers is all you need. Tomorrow, we’ll recognize the importance of holistic field behavior and the boundary problem. One way to get there on the computer science front will be by first demonstrating a niche set of applications where e.g. non-linear optics ISING solvers vastly outperform GPUs for energy minimization tasks in random graphs. But as the unique computational benefits become better understood, we will sooner or later switch from thinking about how to solve our particular problem, to thinking about how we can cast our particular problem as an ISING/energy minima problem so that physics solves the problem for us. It’s like having a powerful computer but it only speaks a very specific alien language. If you can translate your problem into its own terms, it’ll solve it at lightning speed. If you can’t, it will be completely useless. Intelligence:

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text:
Collecting and Applying Self-Organizing Principles This takes us to the question of whether general intelligence is possible without switching to a Holistic Computing paradigm. Can you have generally intelligent (digital) chatbots? In some senses, yes. In perhaps the most significant sense, no. Intelligence is a contentious topic (see here David Pearce’s helpful breakdown of 6 of its facets). One particular facet of intelligence that I find enormously fascinating and largely under-explored is the ability to make sense of new modes of consciousness and then recruit them for computational and aesthetic purposes. THC and music production have a long history of synergy, for instance. A composer who successfully uses THC to generate musical ideas others find novel and meaningful is applying this sort of intelligence. THC-induced states of consciousness are largely dysfunctional for a lot of tasks. But someone who utilizes the sort of intelligence (or meta-intelligence) I’m pointing to will pay attention to the features of experience that do have some novel use and lean on those. THC might impair working memory, but it also expands and stretches musical space. Intensifies reverb, softens rough edges in heart notes, increases emotional range, and adds synesthetic brown noise (which can enhance stochastic

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text:
resonance). With wit and determination (and co-morbid THC/music addiction), musical artists exploit the oddities of THC musicality to great effect, arguably some much more successfully than others. The kind of reframe that I’d like you to consider is that we are all in fact something akin to these stoner musicians. We were born with this qualia resonator with lots of cavities, kinds of waves, levels of coupling, and so on. And it took years for us to train it to make adaptive representations of the environment. Along the way, we all (typically) develop a huge repertoire of self-organizing principles we deploy to render what we believe is happing out there in the world. The reason why an experience of “meditation on the wetness of water” can be incredibly powerful is not because you are literally tuning into the resonant frequency of the water around you and in you. No, it’s something very different. You are creating the conditions for the self-organizing principle that we already use to render our experiences with water to take over as the primary organizer of our experience. Since this self-organizing principle does not, by its nature, generate a center, full absorption into “water consciousness” also

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text:
has a no-self quality to it. Same with the other elements. Excitingly, this way of thinking also opens up our mind about how to craft meditations from first principles. Namely, by creating a periodic table of self-organizing principles and then systematically trying combinations until we identify the laws of qualia chemistry. You have to come to realize that your brain’s relationship with self-organizing principles is like that of a Pokémon trainer and his Pokémon (ideally in a situation where Pokémon play the Glass Bead Game with each other rather than try to hurt each other– more on that later). Or perhaps like that of a mathematician and clever tricks for proofs, or a musician and rhythmic patterns, and so on. Your brain is a highly tamed inner space qualia warp drive usually working at 1% or less. It has stores of finely balanced and calibrated self-organizing principles that will generate the right atmospheric change to your experience at the drop of a hat. We are usually unaware of how many moods, personalities, contexts, and feelings of the passage of time there are – your brain tries to learn them all so it has them in store for whenever needed. All

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