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Long term, our focus is on enabling the creation of truly intelligent machines that understand the world.
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Incorporating these ideas into artificial neural systems can enable systems that learn continuously from streaming data without any manual interventions.
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In the brain, sparse representations plus a more complex neuron model, enables us to continuously learn new patterns in an unsupervised manner. Truly intelligent machines must have the capability to learn and adapt continuously, a property that is absent in today’s deep learning systems. Sparse networks achieve accuracy competitive with the state of the art dense models, but are significantly more robust to noise. These networks have both sparse activations and sparse connections. We have developed a cortically inspired sparse algorithm that can be applied to deep learning networks trained through backpropagation. Our neuroscience research has shown that sparse representations are more robust and stable than dense representations. We are investigating ways to create highly sparse networks that learn their structure dynamically through training. As many as 30% of the connections in the neocortex turn over every few days. In the brain, cortical networks are sparsely connected and extremely dynamic. Along with our hardware partners, we are developing methods for dramatically improving the computational efficiency of sparse neural networks. These limitations have hindered research into sparse models. For machine learning, sparsity also offers the promise of significant computational benefits, but most hardware architectures are not optimized for extreme sparsity. Representations in the brain are highly sparse, resulting in an extremely efficient system. Below are some of the topics we are currently researching: Current Research Projects: Performance improvements in sparse networks By incorporating these principles, we can overcome today’s limitations and build tomorrow’s intelligent machines. These principles have the promise to solve many of the known problems today’s machine learning and AI systems face. Our neuroscience research has uncovered a number of core principles that are not reflected in today’s machine learning systems. We strive to be completely open in everything we do. In addition, we place our research commits in an open-source project. We document our research in several ways, including peer-reviewed journal papers, conference proceedings, and invited talks. Our deep neuroscience research provides the foundation for delivering transformational changes in machine learning and AI. We are one of the few teams that has developed large-scale theories of the brain that are biologically constrained, testable, and implemented in software. We have built a robust roadmap that lays out the steps to achieving truly intelligent machines based on the key principles of the Thousand Brains Theory. Numenta has developed a novel theory and broad framework for understanding what the neocortex does and how it does it, called the Thousand Brains Theory of Intelligence. Intelligent machines that learn will have an enormous beneficial impact in the coming decades, and the neocortex provides the blueprint for building them. In stark contrast, intelligent machines continuously learn patterns in their environment without supervision, enabling them to tackle problems in entirely new ways. Today’s machine learning and AI have accomplished many impressive tasks but are restricted to narrow, specific goals. The brain’s center of intelligence, the neocortex, controls a wide range of functions using a common set of principles. We believe the brain is the best example of an intelligent system. To reverse-engineer the neocortex and to enable machine intelligence technology based on cortical theory. We are a team of scientists and engineers applying neuroscience principles to machine intelligence research.