Machine Learning

The Thousand Brains Theory of Intelligence

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In our most recent peer-reviewed paper published in Frontiers in Neural Circuits, A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex, we put forward a novel theory for how the neocortex works. In this updated blog about the Thousand Brains Theory of Intelligence originally published in March 2018, Jeff Hawkins and Christy Maver describe the key insights of our theory and how it compares to the classic view of the hierarchy, as well as its implications for AI.

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The 2018 Machine Intelligence Landscape: A New Look at MI vs ML vs DL vs AI

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In 2016, Numenta co-founders Jeff Hawkins and Donna Dubinsky wrote a blog about the three major approaches to building machine intelligence: Classic AI, Simple Neural Networks, and Biological Neural Networks. This piece revisits each one and looks at the machine intelligence landscape today. Discover the state of the art, compare and contrast approaches, and understand fundamental limitations. Read why brain theory will be the future of machine intelligence.

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The Thousand Brains Model of Intelligence

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In our paper, A Theory of How Columns in the Neocortex Enable Learning the Structure of the World, we proposed that a single cortical column can learn models of complete objects through movement. Jeff Hawkins and Christy Maver explain our “Thousand Brains Model of Intelligence” and its implications for AI in this blog.

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Neuromorphic Chip Modeling Dendritic Spikes Debuts at NICE

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Neuromorphic chips, which emulate neurons in silicon, are essentially the hardware for the future of AI. The Human Brain Project’s Neuromorphic team recently unveiled a chip called BrainScaleS-2 that models neurons consistent with the model described in our 2016 paper “Why Neurons Have Thousands of Synapses.”

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The Secret to Strong AI

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AI techniques, such as deep learning and convolutional neural networks, have made stunning advancements in image recognition, self-driving cars, and other difficult tasks. Yet, leading AI researchers realize something is not right. In this piece, Jeff Hawkins writes about the “missing ingredient” for strong AI.

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Today’s Weak AI Lacks Intelligence

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This blog by Matt Taylor compares today’s weak AI to strong AI, or AGI. While Deep Learning and other ANN-based methods are useful, he believes that the path to strong AI is a biological one. At Numenta, we aim to understand how intelligence works in the cortex and create new intelligent systems using those principles.

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Brain Theory in AI – Inspiration or Direction?

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Numenta VP of Marketing Christy Maver wrote a guest post for insideBIGDATA titled, “The Importance of Brain Theory in True Machine Intelligence.” In the piece, she shares her thoughts on machine intelligence technology based on brain theory, why it’s important, how and why it works, and where it will take us.

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MLconf Interview with Austin Marshall

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MLconf Technical Chair, Nick Vasiloglou, recently interviewed Numenta engineer, Austin Marshall, about HTM and our approach in machine learning. In their conversation, Nick asks Austin about a variety of topics – from AI to reinforcement learning to how HTM differs from deep learning.

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An Insider’s Look: Interview with Yang Lan and Jeff Hawkins

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A prominent figure known as the Oprah of China, Yang Lan, interviewed our co-founder, Jeff Hawkins for an upcoming documentary series on AI. She travelled across the globe to talk to knowledgeable researchers, technologists and leaders about their findings. In this blog, you’ll get an insider’s look at their interview.

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Numenta at the Computational and Systems Neuroscience Conference (COSYNE)

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Earlier this month, I attended the annual Computational and Systems Neuroscience meeting (Cosyne) in Salt Lake City. Cosyne is a peer reviewed scientific conference that brings experimental and theoretical neuroscientists together to exchange data and ideas.

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