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Brain Works How? | On the coming revolution in machine intelligence: Sparse Distributed Representations

On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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Brain Works How? | On the coming revolution in machine intelligence: Sparse Distributed Representations | brainworkshow.sparsey.com Reviews

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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Brain Works How? | On the coming revolution in machine intelligence: Sparse Distributed Representations | brainworkshow.sparsey.com Reviews

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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Many-worlds | Brain Works How?

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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exponential speedup | Brain Works How?

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Tag Archives: exponential speedup. Sparse distributed representations compute similarity relations exponentially more efficiently than localist representations. September 10, 2015. If concepts are represented localistically, the first, the most straightforward thing to do is to place those representations in an. On the other hand, if concepts are represented using. SDCs), i.e.,. Of co-active units chosen from a much lar...

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quantum superposition | Brain Works How?

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Tag Archives: quantum superposition. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Manifests almost identically as it did in the original instance; call that version of. 8216; Thus, if S was present, and thus if. Was present, we could say that.

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low power computing | Brain Works How?

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Tag Archives: low power computing. Sparse distributed representations compute similarity relations exponentially more efficiently than localist representations. September 10, 2015. If concepts are represented localistically, the first, the most straightforward thing to do is to place those representations in an. On the other hand, if concepts are represented using. SDCs), i.e.,. Of co-active units chosen from a much lar...

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Everett | Brain Works How?

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On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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64x64 36-frame Natural snippets

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Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. The blue grids are...

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Sparsey Information Storage Capacity: Exact-match Natural-derived Sequence Recognition

http://www.sparsey.com/ONR_TR2_Result_3_exact_natural.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. Figure 6 shows the...

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32x32 8-frame natural snippets

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Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets.

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Actual vs. Restricted Input Features Experienced

http://www.sparsey.com/actual_vs_restricted_ST_inputs_armOpenClose.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. 4) In column, π.

sparsey.com

Sparsey Constant-time Best-Match Sequence Recognition

http://www.sparsey.com/ONR_TR2_Result_2_best_random.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. Given the random m...

sparsey.com

Sparsey 8-level 64x64 video Recognition Experiment

http://www.sparsey.com/DARPA_TR19_Exp_1.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. The animation show...

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8x8 4-frame close and open arm snippets

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Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets.

sparsey.com

4-lev Sparsey Memory Trace

http://www.sparsey.com/Busy_4lev_recog_trace.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets.

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Hierarchical Filtering of Space-Time Features

http://www.sparsey.com/side_by-side_hierarch_ST_features.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets.

sparsey.com

Spasrsey: multimodal cross-modal learning

http://www.sparsey.com/multimodal_concept.html

Finding the Fundamental Cortical Algorithm of Intelligence. Sparse Distributed Coding and Quantum Computing. Actual Vs Restricted Inputs Experienced. SDC vs. Localist Basis. Many-level Hexagonal Top. Models. SDC vs. Localist Hierarchical Model. Episodic Recognition of 64x64-frame Natural Snippets. Hexagonal RFs of macs from L1 to L5. Hexagonal Overlapped RFs of macs at V1 and higher. Key Invariant Recognition Issue for Deep Hierarchical Models. Episodic Memory of Weizmann HOF Snippets. This next figure s...

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Brain Works How? | On the coming revolution in machine intelligence: Sparse Distributed Representations

On the coming revolution in machine intelligence: Sparse Distributed Representations. Not Copenhagen, Not Everett, a New Interpretation of Quantum Reality. December 28, 2015. I’ve claimed for some time that quantum computing can be realized on a single processor Von Neumann machine and published a paper. Then imagine another instance of the overall graph, S’, in which 990 of those nodes are present. We could imagine that that might represent another state of reality in which. 8216; is also. Thus we can i...

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