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H+ magazine wrote an estimate in 2009 that seems broadly comparable to other things I've seen; they think the human brain is approximately 37 petaflops. A supercomputer larger than that 37 petaflop estimate exists today. But emulation is hard. See this SO question about hardware emulation or this article on emulating the SNES, in which they require 140 ...

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The human brain contains about 100 billion neurons ($10^{11}$) and about a hundred trillion synapses ($10^{14}$). Each neuron can fire about 100 times a second. If we model the brain as a simple neural network, then it would be equivalent to a machine that requires 1016 calculations per second and 1013 bits of memory. From Wikipedia Kurzweil introduces the ...

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A strong reason why people think the mind can be implemented on a Turing Machine stems from the Computational Theory of Mind (CTOM), which is the leading theory of mind for now. There are lots of reasons for supporting the CTOM, one of which being that the language of belief/desire psychology (propositional attitudes over mental representations) seems to fit ...

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One probable hardware limiting factor is internal bandwidth. A human brain has $10^{15}$ synapses. Even if each is only exchanging a few bits of information per second, that's on the order of $10^{15}$ bytes/sec internal bandwidth. A fast GPU (like those used to train neural networks) might approach $10^{11}$ bytes/sec of internal bandwidth. You could ...

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The human brain works by having neurons constantly fire at different rates. So, if the firing rate increases, the neuron is transmitting overly exciting or calming information to further neurons connected to it. How other neurons connected to the former neuron respond on the messages sent by it, depends on the strength of the connection between the connected ...

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Consciousness is not well-understood As an AI practitioner and philosopher, I don't think that humans will be able to create a truly conscious silicon-based AGI. Humans are incapable of creating some "thing" from fiat (a decree). It's never happened in human history. The innovation cycle must begin with some "thing" (some "stuff&...

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Only a small portion of the habituation, sensitization, and classical conditioning behavior of neurons has been primitively simulated in ANN systems. Simulation of actin cytoskeletal machinery1 and other agents of neural plasticity, central to learning new domains, is in its beginnings2. As of this writing, the complexity of neuron activation dwarfs the ...

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As far as emulating an intelligent being, no. There are a few different potential architectures for possible AGI. Many of these are extremely infantile, as the bulk of AI research is in narrow AI, which focuses on creating algorithms that are highly specialized for a specific task. With that being said, here is one supervised learning approach to this ...

3

One incredibly important difference between humans and NNs is that the human brain is the result of billions of years of evolution whereas NNs were partially inspired by looking at the result and thinking "... we could do that" (utmost respect for Hubel and Wiesel). Human brains (and in fact anything biological really) have an embedded structure to them ...

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No. The reasons include but are not limited to: lack of understanding of how the brain works current ANNs are mostly good at pattern recognition and generative tasks, but lack the capacity to create abstractions on their own we can't match the size/number of artificial neurons to the number of biological neurons even with a much smaller ANN size, ...

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Not just how much, but what kind of processing power : there're specially-crafted dedicated chips, and it has a practical applications, so it's not a lab-only project

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ANNs approximate biological neuronal networks. The approximation began with extreme simplicity in the early perceptron design. Spiking networks are examples of more accurate approximations. More accurate still, are complex simulations of neuron behavior that therefore necessitate significant computing resources. If you are interested in a mathematical ...

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I'm going to go out on a limb and suggest that this is a matter of evolution, that humans are in no way exceptional in the grand scheme, and that AGI will manifest so long as technology advances, because human consciousness is simply a matter of complexity of the system. The idea comes out of emergent complexity in Conway's Game of Life. In Conway's words:...

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Regarding "mimicking human intelligence" --> No, not even close. Regarding this: More specifically, can we say the following about neural networks? Neural networks use the sigmoid function and gradient descent to fine-tune weights. No, again. ANN's aren't required to use a sigmoid activation function. There are many other options that are ...

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No, we are not even near an algorithm that can be compared to human level general intelligence. You might have heard the claim that a neural network works similar to the neurons in the brain, but that's quite a stretch. Serena Yeung talks about this in the forth lecture of the Stanford course CS231n. Jump to 1:04:30 in the video, that's around where this ...

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This has been my field of research. I've seen the previous answers that suggest that we don't have sufficient computational power, but this is not entirely true. The computational estimate for the human brain ranges from 10 petaFLOPS ($1 \times 10^{16}$) to 1 exaFLOPS ($1 \times 10^{18}$). Let's use the most conservative number. The TaihuLight can do 90 ...

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Would OpenCog fit the bill? I have had tremendous amounts of trouble building up the demos, which include some non-AGI stuff, but if I’ve read the manual correctly, I think there’s something here — https://opencog.org/

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Not to my knowledge. The problem is that this is such an enormous task, it cannot really be tackled at once. So the obvious solution is to reduce the scope. In early AI people were using toy domains, whereas nowadays AI systems work more generally (but still perform better if the domain is restricted). So while (slow) progress is being made putting the ...

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The most popular theoretical framework in use currently, in the neuromorphic (brain-inspired) computing community is the Neural Engineering Framework (NEF). Neural Engineering by Chris Eliasmith and Charles Anderson explains the framework comprehensively. As a follow up to that, How to Build a Brain by Chris Eliasmith describes the more recent and more high-...

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Yes, CNNs are inspired by the human brain [1, 2, 3]. More specifically, their operations, the convolution and pooling, are inspired by the human brain. However, note that, nowadays, CNNs are mainly trained with gradient descent (GD) and back-propagation (BP), which seems not to be a biologically plausible way of learning, but, given the success of GD and BP, ...

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In the eye, the retinal ganglion cells have a receptive field that is equivalent to some types of convolution filters, most of them edge detectors. The brain is a big unknown, nobody knows how it does to organize, memorize, create concepts, learns the language, ... . Thus, it is not possible to establish a parallelism. In particular, brain has a capacity of ...

1

There is a category of neural networks that more closely attempt to mimic biological neural networks by incorporating also time (i.e. not all neurons fire at the same time). They are called spiking neural networks (SNNs) and their name comes from the fact that they use spiking neurons (i.e. neurons that fire discrete signals and affect other neurons at ...

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The Institute of Electrical and Electronic Engineers (IEEE) recently announced a new "IEEE Transaction on Artificial Intelligence". Although the topics listed do not specifically mention AGI they do not limit it. I think this will be an interesting journal to keep an eye on as there could be some interesting AGI papers. Below is from their web ...

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There's also the journal Advances in Cognitive Systems. According to their website Advances in Cognitive Systems (ISSN 2324-8416) publishes research articles, review papers, and essays on the computational study of human-level intelligence, integrated intelligent systems, cognitive architectures, and related topics. Research on cognitive systems is ...

1

Comparing Unlike Objects The comparison between a person and an artificial network cannot be made on an equal basis. The former is a composition of many things that the later is not. Unlike an artificial network sitting in computer memory on a laptop or server, a human being is an organism, from head to toe, living in the biosphere and interacting with ...

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Short answer: nobody knows. Long answer: all strong-AI works. However, to write something useful to the o.p., say that the question contains several implicit statements, analyze them could be useful to clarify the issue: a) why thing that 1 transistor has the same functionality than 1 neuron ? Some obvious differences: a transistor has 3 legs, each neuron ...

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Although it is not a rigorous proof, Marvin Minsky's book, The Society of Mind gives us a blueprint for creating a "mind" (general intelligence). In his book, he posits that by combining mindless components ("agents") together in various competing and cooperative structures, we can create actual minds. IMHO, the recent popularity of Boosting, Bagging, ...

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Rather than prove that Artificial General Intelligence is possible, I would consider an argument for why it is impossible. We start by defining what we mean by AGI. You state that the human mind can be replicated by a Turing Machine, and therefore AGI should be possible. This seems to imply that humans have `General' (capital G) intelligence. By this I mean ...

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Most artificial neurons model biological neurons but in a very simplistic way. Nowadays, the main aim is to achieve better performance at prediction tasks. However, there is a body of literature in neuroscience that looks at computational models of neurons. Neurons are complicated cells and our understanding of neurons is still not complete.

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