Numenta / Numenta Talks

Jeff Hawkins presented a keynote talk titled "The Thousand Brains Theory: A Roadmap to Machine Intelligence" at the Beijing Academy of Artificial Intelligence Conference on 1st June 2021. In this talk, he discussed the key components of The Thousand Brains Theory and Numenta's recent work.

This video is provided by BAAI. The BAAI Conference is committed to promoting international exchange and cooperation in academia and the AI industry. It also aims to cultivate a community and nurture technological research and breakthroughs across theory, methods, tools, and systems.

Follow-up Q&A: http://numenta.com/resources/videos/baai-conference-2021
Slides: https://www.slideshare.net/numenta/baai-conference-2021-the-thousand-brains-theory-a-roadmap-for-creating-machine-intelligence-jeff-hawkins
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Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications.

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Our VP of Research Subutai Ahmad walks us through the poster he presented at the NAISys Conference in November 2020.

Most deep learning networks today rely on dense representations. This is in stark contrast to our brains, which are extremely sparse. Why is this? Are there benefits to sparsity? In this poster, we review how sparsity is deeply ingrained in the brain. We then show how insights from the brain can be applied to practical AI systems. We show that sparse representations are generally not subject to interference and are extremely robust, as long as the underlying dimensionality is sufficiently high. A key property is that the ratio of the operable volume around a sparse vector divided by the volume of the representational space decreases exponentially with dimensionality. We then analyze computationally efficient sparse networks containing both sparse weights and sparse activations. Through simulations on popular benchmark datasets we show that sparse networks are more robust than dense networks, and more than 50 times faster than dense networks on FPGA platforms.

Link to poster: https://numenta.com/neuroscience-research/research-publications/posters/naisys-2020-sparsity-and-its-implications-for-machine-learning

For additional resources on this topic from NAISys, you can read our whitepaper here: https://numenta.com/neuroscience-research/research-publications/papers/Sparsity-Enables-50x-Performance-Acceleration-Deep-Learning-Networks

You can also watch a re-recording of Jeff’s NAISys talk here: https://www.youtube.com/watch?v=mGSG7I9VKDU
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Numenta is leading the new era of machine intelligence. Our deep experience in theoretical neuroscience research has led to tremendous discoveries on how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications. 

Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence:
https://tinyurl.com/NumentaNewsDigest

Subscribe to our Newsletter for the latest Numenta updates:
https://tinyurl.com/NumentaNewsletter

Our Social Media:
https://twitter.com/Numenta
https://www.facebook.com/OfficialNumenta
https://www.linkedin.com/company/numenta

Our Open Source Resources:
https://github.com/numenta
https://discourse.numenta.org/

Our Website:
https://numenta.com/

Presentation given by Matt Taylor of Numenta at the "Towards AGI" Meetup at UCSC Silicon Valley Extension.

Original live stream at https://www.youtube.com/watch?v=qVKVj4nx-mE

No description provided.

Jeff Hawkins, co-founder of Numenta, talks about the process of making AI more flexible and generalized.

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The Thousand Brains Theory: A Framework for Understanding the Neocortex and Building Intelligent Machines

Recent advances in reverse engineering the neocortex reveal that it is a highly-distributed sensory-motor modeling system. Each cortical column learns complete models of observed objects through movement and sensation. The columns use long-range connections to vote on what objects are currently being observed. In this talk we introduce the key elements of this theory and describe how these elements can be introduced into current machine learning techniques to improve their capabilities, robustness, and power requirements.

See more at https://www.microsoft.com/en-us/research/video/the-thousand-brains-theory-a-framework-for-understanding-the-neocortex-and-building-intelligent-machines/

On October 15, 2018, Numenta Co-founder Jeff Hawkins gave a keynote presentation at the Human Brain Project Summit Open Day in Maastricht, the Netherlands. Because we were not able to get a recording of that talk, we created a screencast of Jeff presenting the material in our office. The material covers our research paper that was released two days prior to the Human Brain Project Summit, "A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex."

https://numenta.com/neuroscience-research/research-publications/papers/a-framework-for-intelligence-and-cortical-function-based-on-grid-cells-in-the-neocortex/

In this talk, our Open Source Community Manager Matt Taylor discusses why today’s weak AI will not produce truly intelligent machines. First, he describes the anatomy of the neocortex while explaining the layers and columns within it and highlighting the power of pyramidal neurons. He then talks about details of our Hierarchical Temporal Memory (HTM) technology, such as Sparse Distributed Representations (SDRs), Spatial Pooling, Temporal Memory, and Sensorimotor Inference Theory.

This video was posted with permission from AI Singapore (https://www.aisingapore.org/).
Slides can be accessed at: https://numenta.com/resources/papers-videos-and-more/ai-singapore-meetup/

Jeff Hawkins, Numenta
https://simons.berkeley.edu/talks/jeff-hawkins-4-17-18
Computational Theories of the Brain

Jeffrey Hawkins is the American founder of Palm Computing and Handspring. He has since turned to work on neuroscience full-time, founded the Redwood Center for Theoretical Neuroscience in 2002, founded Numenta in 2005 and published On Intelligence describing his memory-prediction framework theory of the brain.
Recorded At MIT, Dec 15th, 2017

No description provided.

Jeff Hawkins, Co-Founder, Numenta

Abstract: In this talk I will describe a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. We propose the second input is a representation of allocentric location. The allocentric location represents where the sensed feature is relative to the object being sensed, in an object-centric reference frame. As the sensors move, cortical columns learn complete models of objects by integrating sensory features and location representations over time. Lateral projections allow columns to rapidly reach a consensus of what object is being sensed. We propose that the representation of allocentric location is derived locally, in layer 6 of each column, using the same tiling principles as grid cells in the entorhinal cortex. Because individual cortical columns are able to model complete complex objects, cortical regions are far more powerful than currently believed. The inclusion of allocentric location offers the possibility of rapid progress in understanding the function of numerous aspects of cortical anatomy.

I will be discussing material from these two papers. Others can be found at www.Numenta.com/papers

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World
URL: https://doi.org/10.3389/fncir.2017.00081

Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in the Neocortex
URL: https://doi.org/10.3389/fncir.2016.00023



Speaker Biography: Jeff Hawkins is a scientist and co-founder at Numenta, an independent research company focused on neocortical theory. His research focuses on how the cortex learns predictive models of the world through sensation and movement. In 2002, he founded the Redwood Neuroscience Institute, where he served as Director for three years. The institute is currently located at U.C. Berkeley. Previously, he co-founded two companies, Palm and Handspring, where he designed products such as the PalmPilot and Treo smartphone. In 2004 he wrote “On Intelligence”, a book about cortical theory.

Hawkins earned his B.S. in electrical engineering from Cornell University in 1979. He was elected to the National Academy of Engineering in 2003.

Today's wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical Temporal Memory (HTM) is a realistic biologically-constrained model of the pyramidal neuron reflecting today's most recent neocortical research. This talk will describe and visualize core HTM concepts like sparse distributed representations, spatial pooling and temporal memory. Strong AI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven't produced something we could all call "intelligent". Given recent advances in neuroscience research, we know a lot more about how neurons work together now than we did when ANNs were created. We believe systems with a more realistic neuronal model will be more likely to produce Strong AI. Hierarchical Temporal Memory is a theory of intelligence based upon neuroscience research. The neocortex is the seat of intelligence in the brain, and it is structurally homogeneous throughout. This means a common algorithm is processing all your sensory input, no matter which sense. We believe we have discovered some of the foundational algorithms of the neocortex, and we've implemented them in software. I'll show you how they work with detailed dynamic visualizations of Sparse Distributed Representations, Spatial Pooling, and Temporal Memory.

Matt Taylor
NUMENTA, INC.

Matt manages Numenta's open source projects, helps the HTM Community, and produces educational videos about HTM.

No description provided.

Matt Taylor's April 2017 talk at the AI With The Best conference.

Jeff Hawkins gives a keynote talk at Cornell Silicon Valley’s premiere event, March 2017. This is a trimmed video of the talk with permission from Cornell University.

Numenta engineer Yuwei Cui walks through how the HTM Spatial Pooler works, explaining why desired properties exist and how they work. Includes lots of graphs of SP online learning performance, discussion of topology and boosting.

See corresponding paper at https://discourse.numenta.org/t/the-htm-spatial-pooler-a-neocortical-algorithm-for-online-sparse-distributed-coding/1548

This was recorded in the Numenta office during an engineering lunch meeting on Feb 8, 2017.

This is the presentation Matt gave at the ai.withthebest.com conference in September 2016.

Jeff Hawkins

Presented by Jeff Hawkins

Subutai Ahmad talks about the history of HTM algorithms at Numenta. This video was recorded at the 2015 HTM Challenge.

Did you realize that the development of HTM algorithms at Numenta has been going on for over 10 years? Subutai (who has seen it all) will step you through the sequence of HTM developments, from our very first demos, algorithms and products, to our current research on cortical algorithms.

Real-time Anomaly Detection for Real-time Data Needs: Much of the world’s data is becoming streaming, time-series data, where anomalies give significant information in often-critical situations. Examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. Are there algorithms up for the challenge? Which are the most capable? The Numenta Anomaly Detection Benchmark (NAB) attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. These characteristics are formalized in NAB, using a custom scoring algorithm to evaluate the detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and describe the end-to-end scoring process. We give results and analyses for several algorithms to illustrate NAB in action. The goal for NAB is to provide a standard, open-source framework for which we can compare and evaluate different algorithms for detecting anomalies in streaming data.

Second Annual IBM Research Cognitive Computing Colloquium keynote by Jeff Hawking, co-founder of Numenta.

"What the Brain says about Machine Intelligence"

Jeff Hawkins
Co-founder, Numenta

21 Nov 2014

"Principles of Hierarchical Temporal Memory (HTM): Foundations of Machine Intelligence"

The Q & A Session that followed this presentation can be found here: https://youtu.be/EU2Vm-VlfEk

Jeff Hawkins, Co-Founder, Numenta

Numenta Workshop Oct 2014 Redwood City CA

This is a live-stream of an upcoming Meetup for the Deep Learning London Meetup group. Jeff will be presenting from the Numenta offices in Redwood City to an audience in London. See the details here: http://www.meetup.com/Deep-Learning-London/events/171081112/

This presentation was recorded at GOTO Aarhus 2013
http://gotocon.com

Jeff Hawkins - Brain Inspired Computing

ABSTRACT
Understanding how the brain works and building machines that work on the same principles is one of the greatest quests of our time. In this talk I will describe recent advances in neocortical theory, including why the brain uses sparse distributed representations and how the brain makes predictions from high velocity sensory data streams.

I will demonstrate a product called Grok, that uses a detailed model of neocortical memory to act on machine generated data and how developers can contribute to the development of intelligent machines via the NuPIC open source project (www.numenta.org).

https://twitter.com/gotocon
https://www.facebook.com/GOTOConference
http://gotocon.com

The neocortex generates most of our high level behavior and every region in the neocortex has some form of motor output. In this talk I will describe what we know about how the cortex generates behavior and how behavior fits within the framework of Hierarchical Temporal Memory theory. Although we don't yet have a comprehensive theory of how the neocortex generates behavior we do understand several of the major components giving us hope that a comprehensive theory may be reachable in the near future.

From OSCON: http://www.oscon.com/oscon2013/public/schedule/detail/30342

This new open source library is based concepts first described in Jeff Hawkins' book On Intelligence and subsequently developed by Numenta Inc. NuPIC consists of a set of machine learning algorithms that accurately model layers of neurons in the neocortex. NuPIC's algorithms continuously learn temporal patterns, make predictions, and detect anomalous behavior within streaming data. These are the same algorithms and code used in Numenta's commercial product, Grok.

NuPIC represents a new approach to machine learning and machine intelligence. Given the large interest we have had from people wanting to study these algorithms and apply them in novel ways we created the NuPIC open source library and the accompanying Numenta.org website.

In this hands-on session, we'll introduce NuPIC's Online Prediction Framework (OPF) and demonstrate how one creates models using an OPF client. We'll set up some live streaming data to pass into the client and watch as NuPIC makes online inferences, learning the changing patterns in the streaming data set. NuPIC and the OPF have been applied to many scenarios and form the foundation for Numenta's commercial product, Grok. To master NuPIC you will have to become comfortable with concepts such as sparse distributed representations and on-line learning. You can read about these concepts and the algorithms in this white paper: https://www.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf.

Google Tech Talk
February 12, 2013
(more info below)
Presented by Jeff Hawkins.

ABSTRACT

The neocortex works on principles that are fundamentally different than traditional computers. In this talk I will describe recent advances in understanding the neocortex and how we are applying them to model millions of high velocity data streams.
The talk will start with a description of sparse distributed representations, which are the fundamental units of information in brains. I will then discuss how these representations are learned and how the brain processes them to build predictive models from sensory data. Numenta has built a product called Grok that emulates these capabilities of the neocortex. Grok is being used to understand high velocity machine generated data in many different domains. I will give a brief introduction to Grok and speculate on the future of machine intelligence.

New Frontiers in Cognitive, Evolutionary, and Computational Models of the Mind: Part 2.

(Visit: http://www.uctv.tv/) Are intelligent machines possible? If they are, what will they be like? Jeff Hawkins, an inventor, engineer, neuroscientist, author and entrepreneur, frames these questions by reviewing some of the efforts to build intelligent machines. He posits that machine intelligence is only possible by first understanding how the brain works and then building systems that work on the same principles. He describes Numenta's work using neocortical models to understand the torrent of machine-generated data being created today. He will conclude with predictions on how machine intelligence will unfold in the near and long term future and why creating intelligent machines is important for humanity. Series: "UC Berkeley Graduate Council Lectures" [12/2012] [Science] [Show ID: 24412]

In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley.

Slides: http://www.numenta.com/htm-overview/05-08-2012-Berkeley.pdf

Abstract:
Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams.

Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3UbYhjM

General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_paper.

How a Theory of the Neocortex May Lead to Truly Intelligent Machines

Jeff Hawkins (Numenta founder) presents as part of the UBC Department of Computer Science's Distinguished Lecture Series, March 18, 2010.

Coaxing computers to perform basic acts of perception and robotics, let alone high-level thought, has been difficult. No existing computer can recognize pictures, understand language, or navigate through a cluttered room with anywhere near the facility of a child. Hawkins and his colleagues have developed a model of how the neocortex performs these and other tasks. The theory, called Hierarchical Temporal Memory, explains how the hierarchical structure of the neocortex builds a model of its world and uses this model for inference and prediction. To turn this theory into a useful technology, Hawkins has created a company called Numenta. In this talk Hawkins will describe the theory, its biological basis, and progress in applying Hierarchical Temporal Memory to machine learning problems.

Part of this theory was described in Hawkins' 2004 book, On Intelligence. Further information can be found at www.Numenta.com

http://www.ted.com Treo creator Jeff Hawkins urges us to take a new look at the brain -- to see it not as a fast processor, but as a memory system that stores and plays back experiences to help us predict, intelligently, what will happen next.

TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers are invited to give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, and Design, and TEDTalks cover these topics as well as science, business, politics and the arts. Watch the Top 10 TEDTalks on TED.com, at
http://www.ted.com/index.php/talks/top10