Evaluating Real-Time Anomaly Detection: The Numenta Anomaly BenchmarkNumenta
Subutai Ahmad, VP Research presenting NAB and discussing the need for evaluating real-time anomaly detection algorithms. This presentation was delivered at MLConf (Machine Learning Conference) in San Francisco 2015.
Evaluating Real-Time Anomaly Detection: The Numenta Anomaly BenchmarkNumenta
Subutai Ahmad, VP Research presenting NAB and discussing the need for evaluating real-time anomaly detection algorithms. This presentation was delivered at MLConf (Machine Learning Conference) in San Francisco 2015.
Extending Flink for anomaly detection with Hierarchical Temporal Memory (HTM). Presented at Bay Area Apache Flink Meetup, in San Jose on June 27, 2016.
https://github.com/htm-community/flink-htm
Abstract:
There’s no question that we are seeing an increase in the availability of streaming, time-series data. Largely driven by the rise of the Internet of Things (IoT) and connected real-time data sources, we now have an enormous number of applications with sensors that produce important data that changes over time. This data presents a challenge and opportunity for businesses across every industry. How do they handle the onslaught of streaming data? How can they exploit it to make decisions in real-time? One way is to detect, in real time, when something unusual occurs. Early anomaly detection in streaming data has significant implications, yet can be very difficult to execute. It requires detectors to process data in real-time, not batches, and learn while simultaneously making predictions. In this talk, we’ll look at algorithms designed for such data and analyze the components that lead to optimal performance. We’ll also discuss a new benchmark with a labeled, real-world data set, designed to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. How do we score in a way that rewards algorithms that detect all anomalies as soon as possible, triggers no false alarms, works with real-world time-series data across a variety of domains, and automatically adapts to changing statistics?
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
A Practical Guide to Anomaly Detection for DevOpsBigPanda
Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
With tens of thousands of Java servers running in production in enterprise, Java has become a language of choice for building production systems. If our machines are to exhibit acceptable performance, they require regular tuning.This talk takes a detailed look at techniques for tuning a Java Server.
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Brian Brazil
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works.
If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
Vitaliy Rapp and Kalman Graffi. Continuous Gossip-based Aggregation through Dynamic Information Aging. In IEEE ICCCN ’13: Proceedings of the International Conference on Computer Communications and Networks, 2013.
Abstract—Existing solutions for gossip-based aggregation in peer-to-peer networks use epochs to calculate a global estimation from an initial static set of local values. Once the estimation converges system-wide, a new epoch is started with fresh initial values. Long epochs result in precise estimations based on old measurements and short epochs result in imprecise aggregated estimations. In contrast to this approach, we present in this paper a continuous, epoch-less approach which considers fresh local values in every round of the gossip-based aggregation. By using an approach for dynamic information aging, inaccurate values and values from left peers fade from the aggregation memory. Evaluation shows that the presented approach for continuous information aggregation in peer-to-peer systems monitors the system performance precisely, adapts to changes and is lightweight to operate.
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
In this talk, we present Apache SAMOA, an open-source platform for
mining big data streams with Apache Flink, Storm and Samza. Real time analytics is
becoming the fastest and most efficient way to obtain useful knowledge
from what is happening now, allowing organizations to react quickly
when problems appear or to detect new trends helping to improve their
performance. Apache SAMOA includes algorithms for the most common
machine learning tasks such as classification and clustering. It
provides a pluggable architecture that allows it to run on Apache
Flink, but also with other several distributed stream processing
engines such as Storm and Samza.
Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure.
In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic.
During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.
Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure.
In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic.
During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.
Numenta Brain Theory Discoveries of 2016/2017 by Jeff HawkinsNumenta
Jeff Hawkins discussed recent advances in cortical theory made by Numenta during the HTM Meetup on 11/03/2017. These discoveries are described in the recently published peer-reviewed paper, “A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.” Jeff walked through the text and figures in the paper, as well as discussed the significance of these advances and the importance they play in AI and cortical theory.
The recording of the HTM Meetup is available at https://www.youtube.com/watch?v=c6U4yBfELpU&t=
Extending Flink for anomaly detection with Hierarchical Temporal Memory (HTM). Presented at Bay Area Apache Flink Meetup, in San Jose on June 27, 2016.
https://github.com/htm-community/flink-htm
Abstract:
There’s no question that we are seeing an increase in the availability of streaming, time-series data. Largely driven by the rise of the Internet of Things (IoT) and connected real-time data sources, we now have an enormous number of applications with sensors that produce important data that changes over time. This data presents a challenge and opportunity for businesses across every industry. How do they handle the onslaught of streaming data? How can they exploit it to make decisions in real-time? One way is to detect, in real time, when something unusual occurs. Early anomaly detection in streaming data has significant implications, yet can be very difficult to execute. It requires detectors to process data in real-time, not batches, and learn while simultaneously making predictions. In this talk, we’ll look at algorithms designed for such data and analyze the components that lead to optimal performance. We’ll also discuss a new benchmark with a labeled, real-world data set, designed to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. How do we score in a way that rewards algorithms that detect all anomalies as soon as possible, triggers no false alarms, works with real-world time-series data across a variety of domains, and automatically adapts to changing statistics?
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
A Practical Guide to Anomaly Detection for DevOpsBigPanda
Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
With tens of thousands of Java servers running in production in enterprise, Java has become a language of choice for building production systems. If our machines are to exhibit acceptable performance, they require regular tuning.This talk takes a detailed look at techniques for tuning a Java Server.
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Brian Brazil
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works.
If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
Building High Available and Scalable Machine Learning ApplicationsYalçın Yenigün
The slide contains some high level information about some machine learning algorithms, cross validation and feature extraction techniques. It also contains high level techniques about high available and scalable ML products.
Vitaliy Rapp and Kalman Graffi. Continuous Gossip-based Aggregation through Dynamic Information Aging. In IEEE ICCCN ’13: Proceedings of the International Conference on Computer Communications and Networks, 2013.
Abstract—Existing solutions for gossip-based aggregation in peer-to-peer networks use epochs to calculate a global estimation from an initial static set of local values. Once the estimation converges system-wide, a new epoch is started with fresh initial values. Long epochs result in precise estimations based on old measurements and short epochs result in imprecise aggregated estimations. In contrast to this approach, we present in this paper a continuous, epoch-less approach which considers fresh local values in every round of the gossip-based aggregation. By using an approach for dynamic information aging, inaccurate values and values from left peers fade from the aggregation memory. Evaluation shows that the presented approach for continuous information aggregation in peer-to-peer systems monitors the system performance precisely, adapts to changes and is lightweight to operate.
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
In this talk, we present Apache SAMOA, an open-source platform for
mining big data streams with Apache Flink, Storm and Samza. Real time analytics is
becoming the fastest and most efficient way to obtain useful knowledge
from what is happening now, allowing organizations to react quickly
when problems appear or to detect new trends helping to improve their
performance. Apache SAMOA includes algorithms for the most common
machine learning tasks such as classification and clustering. It
provides a pluggable architecture that allows it to run on Apache
Flink, but also with other several distributed stream processing
engines such as Storm and Samza.
Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure.
In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic.
During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.
Hardware fails, applications fail, our code... well, it fails too (at least mine). To prevent software failure we test. Hardware failures are inevitable, so we write code that tolerates them, then we test. From tests we gather metrics and act upon them by improving parts that perform inadequately. Measuring right things at right places in an application is as much about good engineering practices and maintaining SLAs as it is about end user experience and may differentiate successful product from a failure.
In order to act on performance metrics such as max latency and consistent response times we need to know their accurate value. The problem with such metrics is that when using popular tools we get results that are not only inaccurate but also too optimistic.
During my presentation I will simulate services that require monitoring and show how gathered metrics differ from real numbers. All this while using what currently seems to be most popular metric pipeline - Graphite together with metrics.dropwizard.io library - and get completely false results. We will learn to tune it and get much better accuracy. We will use JMeter to measure latency and observe how falsely reassuring the results are. Finally I will show how HdrHistogram helps in gathering reliable metrics. We will also run tests measuring performance of different metric classes.
Numenta Brain Theory Discoveries of 2016/2017 by Jeff HawkinsNumenta
Jeff Hawkins discussed recent advances in cortical theory made by Numenta during the HTM Meetup on 11/03/2017. These discoveries are described in the recently published peer-reviewed paper, “A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.” Jeff walked through the text and figures in the paper, as well as discussed the significance of these advances and the importance they play in AI and cortical theory.
The recording of the HTM Meetup is available at https://www.youtube.com/watch?v=c6U4yBfELpU&t=
The Biological Path Towards Strong AI Strange Loop 2017, St. LouisNumenta
Copy and paste this URL to your browser to watch the live presentation: https://www.youtube.com/watch?v=-h-cz7yY-G8
Abstract:
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.
Recognizing Locations on Objects by Marcus LewisNumenta
Marcus gave a talk called "Recognizing Locations on Objects" during the HTM Meetup on 11/03/2017.
The brain learns and recognizes objects with independent moving sensors. It’s not obvious how a network of neurons would do this. Numenta has suggested that the brain solves this by computing each sensor’s location relative to the object, and learning the object as a set of features-at-locations. Marcus showed how the brain might determine this “location relative to the object.” He extended the model from Numenta’s recent paper, "A Theory of How Columns in the Neocortex Enable Learning the Structure of the World," so that it computes this location. This extended model takes two inputs: each sensor’s input, and each sensor’s “location relative to the body.” The model connects the columns in such a way that a column can compute its “location relative to the object” from another column’s “location relative to object.” When a column senses a feature, it recalls a union of all locations where it has sensed this feature, then the columns work together to narrow their unions. This extended model essentially takes its sensory input and asks, “Do I know any objects that contain this spatial arrangement of features?”
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.
a preview of the platform TouchNet: A touch simulator and dataset of touchable objects to teach AIs how to interact with their environments via motor-sensory systems and touch
SmartData Webinar: Applying Neocortical Research to Streaming AnalyticsDATAVERSITY
We are witnessing an explosion of sensors and machine generated data. Every server, every building, and every device generates a continuous stream of information that is ever changing and potentially valuable. The existing big data paradigm requires storing data for batch analysis, and extensive modeling by a human expert, prior to deployment. This is incredibly inefficient and cannot scale.
In this webinar, Ahmad will describe a new paradigm for streaming data algorithms, based on recent neuroscience findings and on the computational properties of the neocortex. These systems are highly automated, adapt to changing statistics, and naturally deal with temporal data streams. Many of the core ideas have been implemented in the open source project NuPIC, and validated in commercial anomaly detection and predictive maintenance applications. Given the massive increase in the number of data sources, a general-purpose automated approach is the only scalable way to effectively analyze and act on continuously streaming information.
Using Machine Learning to Understand Kafka Runtime Behavior (Shivanath Babu, ...confluent
Apache Kafka is now nearly ubiquitous in modern data pipelines and use cases. While the Kafka development model is elegantly simple, operating Kafka clusters in production environments is a challenge. It’s hard to troubleshoot misbehaving Kafka clusters, especially when there are potentially hundreds or thousands of topics, producers and consumers and billions of messages.
The root cause of why real-time applications is lag may be due to an application problem – like poor data partitioning or load imbalance – or due to a Kafka problem – like resource exhaustion or suboptimal configuration. Therefore getting the best performance, predictability, and reliability for Kafka-based applications can be difficult. In the end, the operation of your Kafka powered analytics pipelines could themselves benefit from machine learning (ML).
Data Platform at Twitter: Enabling Real-time & Batch Analytics at ScaleSriram Krishnan
The Data Platform at Twitter supports engineers and data scientists running batch jobs on Hadoop clusters that are several 1000s of nodes, and real-time jobs on top of systems such as Storm. In this presentation, I discuss the overall Data Platform stack at Twitter. In particular, I talk about enabling real-time and batch analytics at scale with the help of Scalding, which is a Scala DSL for batch jobs using MapReduce, Summingbird, which is a framework for combined real-time and batch processing, and Tsar, which is a framework for real-time time-series aggregations.
Slides from my talk at Philly ETE looking at the Lambda Architecture (originating at twitter) critically from the perspective of someone viewing it from the financial (faster, higher volume, spikier data) domain
Mining big data streams with APACHE SAMOA by Albert BifetJ On The Beach
In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
Real time analytics with Spark Streaming by Padma at Bangalore I & D meetup (https://www.meetup.com/Bengaluru-Insights-and-Data-Meetup/events/238459154)
Streaming data presents new challenges for statistics and machine learning on extremely large data sets. Tools such as Apache Storm, a stream processing framework, can power range of data analytics but lack advanced statistical capabilities. These slides are from the Apache.con talk, which discussed developing streaming algorithms with the flexibility of both Storm and R, a statistical programming language.
At the talk I dicsussed issues of why and how to use Storm and R to develop streaming algorithms; in particular I focused on:
• Streaming algorithms
• Online machine learning algorithms
• Use cases showing how to process hundreds of millions of events a day in (near) real time
See: https://apacheconna2015.sched.org/event/09f5a1cc372860b008bce09e15a034c4#.VUf7wxOUd5o
This is the presentation I gave at VizSec 2014 on our information-theoretic method for anomaly detection. The conference was held in Paris in November 2014.
Subutai Ahmad, VP of Research, Numenta at MLconf SF - 11/13/15MLconf
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.
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
(BDT207) Real-Time Analytics In Service Of Self-Healing EcosystemsAmazon Web Services
Netflix strives to provide an amazing experience to each member. To accomplish this, Netflix needs to maintain very high availability across our systems. However, at a certain scale, humans can no longer scale their ability to monitor the status of all systems, making it critical for Netflix to build tools and platforms that can automatically monitor their production environments and make intelligent real-time operational decisions to remedy the problems they identify. In this session, we discuss how Netflix uses data mining and machine learning techniques to automate decisions in real-time with the goal of supporting operational availability, reliability, and consistency. We review how we got to the current states, the lessons we learned, and the future of real-time analytics at Netflix. While Netflix's scale is larger than most other companies, we believe the approaches and technologies we discuss are highly relevant to other production environments, and audience members should come away with actionable ideas that are implementable in, and benefit, most other environments.
Filtering From the Firehose: Real Time Social Media StreamingCloud Elements
All Things Cloud Developer Meetup.
Filtering From the Firehose: Real Time Social Media Streaming with Jim Moffitt from Gnip. Gnip is the world's largest and most trusted provider of social data.
Learn about collecting and filtering social media data with streaming APIs. Jim will cover best practices, use case examples and live demos of filtering data from Twitter.
Real time intrusion detection in network traffic using adaptive and auto-scal...Gobinath Loganathan
Oral presentation of Real-time Intrusion Detection in Network Traffic Using Adaptive and Auto-scaling Stream Processor
at IEEE Global Communications Conference (Globecom 2018).
Abstract:
Advanced intrusion detection systems are beginning to utilize the power and flexibility offered by Complex Event Processing (CEP) engines. Adapting to new attacks and optimizing CEP rules are two challenges in this domain. Optimizing CEP rules requires a complete framework which can be ported to stream processors because a CEP rule cannot run without a stream processor. External dependencies of stream processors make CEP rule a black box which is hard to optimize. In this paper, we present a novel adaptive and functionally auto-scaling stream processor: "Wisdom" with a built-in hybrid optimizer developed using Particle Swarm Optimization, and Bisection algorithms to optimize CEP rule parameters. We show that an adaptive "Wisdom" rule tuned by the proposed optimization algorithm is able to detect selected attacks in CICIDS 2017 dataset with an average precision of 99.98% and an average recall of 93.42% while processing over 2.5 million events per second. The proposed distributed functionally auto-scaling deployment mode consumes significantly fewer system resources than the monolithic deployment of CEP rules.
Similar to Predictive Analytics with Numenta Machine Intelligence (20)
Brains@Bay Meetup: A Primer on Neuromodulatory Systems - Srikanth RamaswamyNumenta
Meetup page: https://www.meetup.com/Brains-Bay/events/284481247/
Neuromodulators are signalling chemicals in the brain, which control the emergence of adaptive learning and behaviour. Neuromodulators including dopamine, acetylcholine, serotonin and noradrenaline operate on a spectrum of spatio-temporal scales in tandem and opposition to reconfigure functions of biological neural networks and to regulate global cognition and state transition. Although neuromodulators are important in shaping cognition, their phenomenology is yet to be fully realized in deep neural networks (DNNs). In this talk, we will give an overview of the biological organizing principles of neuromodulators in adaptive cognition and highlight the competition and cooperation across neuromodulators.
Brains@Bay Meetup: How to Evolve Your Own Lab Rat - Thomas MiconiNumenta
Meetup page: https://www.meetup.com/Brains-Bay/events/284481247/
A hallmark of intelligence is the ability to learn new flexible, cognitive behaviors - that is, behaviors that require discovering, storing and exploiting novel information for each new instance of the task. In meta-learning, agents are trained with external algorithms to learn one specific cognitive task. However, animals are able to pick up such cognitive tasks automatically, as a result of their evolved neural architecture and synaptic plasticity mechanisms, including neuromodulation. Here we evolve neural networks, endowed with plastic connections and reward-based neuromodulation, over a sizable set of simple meta-learning tasks based on a framework from computational neuroscience. The resulting evolved networks can automatically acquire a novel simple cognitive task, never seen during evolution, through the spontaneous operation of their evolved neural organization and plasticity system. We suggest that attending to the multiplicity of loops involved in natural learning may provide useful insight into the emergence of intelligent behavior.
Brains@Bay Meetup: The Increasing Role of Sensorimotor Experience in Artifici...Numenta
We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. The first step was to view AI as the design of agents which interact with the world and thereby have sensorimotor experience; this viewpoint became prominent in the 1980s and 1990s. The second step was to view the goal of intelligence in terms of experience, as in the reward signal of optimal control and reinforcement learning. The reward formulation of goals is now widely used but rarely loved. Many would prefer to express goals in non-experiential terms, such as reaching a destination or benefiting humanity, but settle for reward because, as an experiential signal, reward is directly available to the agent without human assistance or interpretation. This is the pattern that we see in all four steps. Initially a non-experiential approach seems more intuitive, is preferred and tried, but ultimately proves a limitation on scaling; the experiential approach is more suited to learning and scaling with computational resources. The third step in the increasing role of experience in AI concerns the agent’s representation of the world’s state. Classically, the state of the world is represented in objective terms external to the agent, such as “the grass is wet” and “the car is ten meters in front of me”, or with probability distributions over world states such as in POMDPs and other Bayesian approaches. Alternatively, the state of the world can be represented experientially in terms of summaries of past experience (e.g., the last four Atari video frames input to DQN) or predictions of future experience (e.g., successor representations). The fourth step is potentially the biggest: world knowledge. Classically, world knowledge has always been expressed in terms far from experience, and this has limited its ability to be learned and maintained. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.
Brains@Bay Meetup: Open-ended Skill Acquisition in Humans and Machines: An Ev...Numenta
In this talk, I will propose a conceptual framework sketching a path toward open-ended skill acquisition through the coupling of environmental, morphological, sensorimotor, cognitive, developmental, social, cultural and evolutionary mechanisms. I will illustrate parts of this framework through computational experiments highlighting the key role of intrinsically motivated exploration in the generation of behavioral regularity and diversity. Firstly, I will show how some forms of language can self-organize out of generic exploration mechanisms without any functional pressure to communicate. Secondly, we will see how language — once invented — can be recruited as a cognitive tool that enables compositional imagination and bootstraps open-ended cultural innovation.
For more:
Brains@Bay Meetup: The Effect of Sensorimotor Learning on the Learned Represe...Numenta
Most current deep neural networks learn from a static data set without active interaction with the world. We take a look at how learning through a closed loop between action and perception affects the representations learned in a DNN. We demonstrate how these representations are significantly different from DNNs that learn supervised or unsupervised from a static dataset without interaction. These representations are much sparser and encode meaningful content in an efficient way. Even an agent who learned without any external supervision, purely through curious interaction with the world, acquires encodings of the high dimensional visual input that enable the agent to recognize objects using only a handful of labeled examples. Our results highlight the capabilities that emerge from letting DNNs learn more similar to biological brains, though sensorimotor interaction with the world.
For more:
SBMT 2021: Can Neuroscience Insights Transform AI? - Lawrence SpracklenNumenta
Numenta's Director of ML Architecture Lawrence Spracklen presented a talk at the SBMT Annual Congress on July 10th, 2021. He talked about how neuroscience principles can inspire better machine learning algorithms.
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks -...Numenta
Nick Ni (Xilinx) and Lawrence Spracklen (Numenta) presented a talk at the FGPA Conference Europe on July 8th, 2021. In this talk, they presented a neuroscience approach to optimize state-of-the-art deep learning networks into sparse topology and how it can unlock significant performance gains on FPGAs without major loss of accuracy. They then walked through the FPGA implementation where they exploited the advantage of sparse networks with a unique Domain Specific Architecture (DSA).
BAAI Conference 2021: The Thousand Brains Theory - A Roadmap for Creating Mac...Numenta
Jeff Hawkins presented a talk on "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.
Jeff Hawkins NAISys 2020: How the Brain Uses Reference Frames, Why AI Needs t...Numenta
Jeff Hawkins presents a talk on "How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same." In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
This talk was first presented at the NAISys conference on November 10, 2020. You can find a re-recording of the talk here: https://youtu.be/mGSG7I9VKDU
OpenAI’s GPT 3 Language Model - guest Steve OmohundroNumenta
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to YouTube recording of Steve's talk: https://youtu.be/0ZVOmBp29E0
CVPR 2020 Workshop: Sparsity in the neocortex, and its implications for conti...Numenta
Numenta VP Research Subutai Ahmad presents a talk on "Sparsity in the Neocortex and its Implications for Continual Learning" at the virtual CVPR 2020 workshop. In this talk, he discusses how continuous learning systems can benefit from sparsity, active dendrites and other neocortical mechanisms.
The Thousand Brains Theory: A Framework for Understanding the Neocortex and B...Numenta
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.
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...Numenta
Jeff Hawkins delivered this keynote presentation at the 2018 Human Brain Project Summit Open Day in Maastricht, the Netherlands on October 15, 2018. A screencast recording of the slides is also available at: https://numenta.com/resources/videos/jeff-hawkins-human-brain-project-screencast/
Location, Location, Location - A Framework for Intelligence and Cortical Comp...Numenta
Jeff Hawkins gave this presentation as part of the Johns Hopkins APL Colloquium Series on Septemer 21, 2018.
View the video of the talk here: https://numenta.com/resources/videos/jeff-hawkins-johns-hopkins-apl-talk/
Have We Missed Half of What the Neocortex Does? A New Predictive Framework ...Numenta
Numenta VP of Research Subutai Ahmad delivered this presentation at the Centre for Theoretical Neuroscience, University of Waterloo on October 2, 2018.
The Biological Path Toward Strong AI by Matt Taylor (05/17/18)Numenta
These are Matt Taylor's slides from the AI Singapore Meetup on May 17, 2018.
Abstract:
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.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
4. THE STREAMING ANALYTICS PROBLEM
Given all past input and current
input, compute the state of the
system right now.
Must report decision, perform
any retraining, bookkeeping,
etc. before next input arrives.
• No look-ahead – online, not batch
• No training/test set split
• System must be automated, and customized to each stream
• Unsupervised, continuous learning
5. REAL-TIME ANALYTICS
• Enormous increase in the availability of streaming, time-series data
• Prediction is fundamental to real-time analytics, and valuable in all
domains!
Monitoring
IT infrastructure
Financials data Tracking vehicles
Real-time
health
monitoring
Energy
consumption
8. HIERARCHICAL TEMPORAL MEMORY (HTM)
HTM is a powerful sequence memory derived from recent
findings in experimental neuroscience.
• High capacity memory-based system
• Models complex, high-order temporal sequences
• Inherently streaming
• Continuously learning and predicting
• No need to tune hyper-parameters
• Robust and fault-tolerant
• Runs in real time on a laptop
• Open source: github.com/numenta
9. HIERARCHICAL TEMPORAL MEMORY (HTM)
Want to dive in to HTM?
• http://numenta.com/learn
• BaMI
• Research papers
• HTM School
• http://numenta.org for NuPIC
• https://discourse.numenta.org
• Social media:
11. HTM PREDICTS FUTURE INPUT
Active Inactive Depolarized
(predicted)
HTM 𝑎(𝑥$)
𝜋(𝑥$)
𝑥$
• Input to the system is a stream of data:
• Encoded into a sparse, high dimensional vector
• Learns temporal sequences in inputstream:
• Makes a prediction in the form of a sparse vector:
• 𝜋(𝑥$) represents a predictionfor upcoming input:
𝑥$
𝑎(𝑥$)
𝜋(𝑥$)
𝑎(𝑥$'()
12. HTM
Raw anomaly
score
Anomaly
likelihood
• 𝑠$ is an instantaneous measure of
prediction error
• 0 if input was perfectly prediction
• 1 if it was completely unpredicted
• Could threshold it directly to report
anomalies, but in very noisy
environments we can do better
𝑥$
𝑎(𝑥$)
𝜋(𝑥$)
𝐿$
𝑠$
ANOMALY DETECTION WITH HTM
13. ANOMALY LIKELIHOOD
Second order measure: did the predictability of the metric change?
1. Estimate historical distribution of raw anomaly scores
2. Check if recent scores are very different
14. ANOMALY LIKELIHOOD
Second order measure: did the predictability of the metric change?
1. Estimate historical distribution of raw anomaly scores
2. Check if recent scores are very different
16. MULTIPLE STREAMS
Ahmad & Purdy, "Real-Time Anomaly Detection for StreamingAnalytics": https://arxiv.org/abs/1607.02480
17. PREDICTION USES SOFTMAX CLASSIFIER
HTM
SDR
Classifier
• Classifier maps 𝑎(𝑥$) to a probability distribution over inputs using a linear classifier
plus softmax
• Classifier trained to optimize negative log likelihood
• System can predict multiple time steps into the future
• Weights are updated continuously
• Can predict categories and scalar values
𝑎(𝑥$)𝑥$
𝑃(𝑥$',|𝑥$)
19. Cui et al, "Continuous online sequence learning with an unsupervised neural network model":
https://arxiv.org/abs/1512.05463
PERFORMANCE ON REAL-WORLD
STREAMING DATA SOURCES
20. New pattern introduced:
20% increase of night taxi demand
20% decrease of morning taxi demand
Cui et al, "Continuous online sequence learning with an unsupervised neural network model":
https://arxiv.org/abs/1512.05463
FAST ADAPTATION TO CHANGES IN THE DATA
STREAMS
22. HTM ENGINE FOR STREAMING ANALYTICS
Datacenter
server
anomalies
Rogue human
behavior
Geospatial
tracking
Stock
anomalies
Social media
streams
(Twitter)
HTM High Order
Sequence Memory
Encoder
SDRData
Prediction
Anomaly detection
Classification
23. HTM ENGINE + RIVER VIEW
HTM Engine code: https://github.com/numenta/numenta-apps
River View service: http://data.numenta.org/
29. § HTM Studio
§ Easy to use desktop application
§ No data upload required, no coding required
§ Download application at http://numenta.com/htm-studio
TRY HTM ANOMALY DETECTION WITH HTM
STUDIO!
30. ANOMALIES IN IT INFRASTRUCTURE
Grok
• Commercial server based product detects anomalies in IT infrastructure
• Runs thousands of HTM anomaly detectors in real time
• 10 milliseconds per input per metric, including continuous learning
• No parameter tuning required
• grokstream.com
31. ANOMALIES IN FINANCIALAND SOCIAL
MEDIA DATA
HTM for Stocks
• Real-time free demo application
• Continuously monitors top 200 stocks
• Available on iOS App Store or Google Play Store
• Open source application: github.com/numenta/numenta-apps
32. NUMENTAANOMALY BENCHMARK (NAB)
• NAB: a rigorous benchmark for anomaly
detection in streaming applications
• Real-world benchmark data set
• 58 labeled data streams
(47 real-world, 11 artificial streams)
• Total of 365,551 data points
• Scoring mechanism
• Rewards early detection
• Different “application profiles”
• Open resource
• AGPL repository contains data, source code, and
documentation
• github.com/numenta/NAB
• Ongoing competition to expand NAB