The document discusses machine learning and edge computing in an industrial context. It describes performing machine learning at the edge, such as on devices or machines, to avoid sending private images or production data to the cloud. It outlines the benefits of edge computing, including increased security, reduced data volumes, and scalability. Various machine learning techniques are presented, including anomaly detection, time series analysis, and process analysis to optimize overall equipment effectiveness.
Cheryl Wiebe - Advanced Analytics in the Industrial WorldRehgan Avon
2018 Women in Analytics Conference
https://www.womeninanalytics.org/
Cheryl will talk about her consulting practice in Industrial Solutions, Analytic solutions for industrial IoT-enabled businesses, including connected factory, connected supply chain, smart mobility, connected assets. Her path to this practice has bounced between hands on systems development, IT strategy, business process reengineering, supply chain analytics, manufacturing quality analytics, and now Industrial IoT analytics. She spent time working in industry as a developer, as a management consultant, started and sold a company, before settling in to pursue this topic as a career analytics consultant. Cheryl will shed light on what's happening in industrial companies struggling to make the transition to digital, what that means, and what barriers they're challenged with. She'll touch on how/where artificial intelligence, deep learning, and machine learning technologies are being used most effectively in industrial companies, and what are the unique challenges they are facing. Reflecting on what's changed over the years, and her journey to witness this, Cheryl will pose what she considers important ideas to consider for women (and men) in pursuing an analytics career successfully and meaningfully.
Get an introduction to FactoryTalk® Analytics for Applications and learn how it can help you predict issues. It’s an Artificial Intelligence add-on module for ControlLogix® products that
learns and models the data in a ControlLogix® application and helps predict anomalies in the process.
Machine learning for optical networking: hype, reality and use casesADVA
Danish Rafique's ECOC workshop explored how machine learning can bring cognitive capabilities to optical networks, creating simpler operations and greater efficiency than threshold-based systems.
Cheryl Wiebe - Advanced Analytics in the Industrial WorldRehgan Avon
2018 Women in Analytics Conference
https://www.womeninanalytics.org/
Cheryl will talk about her consulting practice in Industrial Solutions, Analytic solutions for industrial IoT-enabled businesses, including connected factory, connected supply chain, smart mobility, connected assets. Her path to this practice has bounced between hands on systems development, IT strategy, business process reengineering, supply chain analytics, manufacturing quality analytics, and now Industrial IoT analytics. She spent time working in industry as a developer, as a management consultant, started and sold a company, before settling in to pursue this topic as a career analytics consultant. Cheryl will shed light on what's happening in industrial companies struggling to make the transition to digital, what that means, and what barriers they're challenged with. She'll touch on how/where artificial intelligence, deep learning, and machine learning technologies are being used most effectively in industrial companies, and what are the unique challenges they are facing. Reflecting on what's changed over the years, and her journey to witness this, Cheryl will pose what she considers important ideas to consider for women (and men) in pursuing an analytics career successfully and meaningfully.
Get an introduction to FactoryTalk® Analytics for Applications and learn how it can help you predict issues. It’s an Artificial Intelligence add-on module for ControlLogix® products that
learns and models the data in a ControlLogix® application and helps predict anomalies in the process.
Machine learning for optical networking: hype, reality and use casesADVA
Danish Rafique's ECOC workshop explored how machine learning can bring cognitive capabilities to optical networks, creating simpler operations and greater efficiency than threshold-based systems.
Heimann Sensor 32 x 32-array thermopile LWIR image sensor with silicon lens 2...system_plus
A small, easy to use, low-power, cheap non-contact temperature measurement for varying applications.
More information on that report at http://www.systemplus.fr/reverse-costing-reports/heimann-sensor-32-x-32-array-thermopile-lwir-image-sensor-with-silicon-lens/
Open Source for Industry 4.0 – Open IoT Summit NA 2018Benjamin Cabé
Industry 4.0 is set to revolutionize the manufacturing industry. The potential for more flexible manufacturing, more efficient processes and lower costs are the driving factors behind the investment in Industry 4.0 solutions. A key part of creating successful Industry 4.0 solutions will be software on the factory floor and in the cloud.
In this talk, we will introduce how open source software has become a trusted source of technology for the enterprise IT software industry and how the Eclipse IoT open source community and other open source communities are now ready to provide production ready technology for the manufacturing industry and Industry 4.0. Open source software will provide the key building blocks that will promote the interoperability and flexibility required by Industry 4.0 solutions.
Designing data pipelines for analytics and machine learning in industrial set...DataWorks Summit
Machine learning has made it possible for technologists to do amazing things with data. Its arrival coincides with the evolution of networked manufacturing systems driven by IoT. In this presentation we’ll examine the rise of IoT and ML from a practitioners perspective to better understand how applications of AI can be built in industrial settings. We'll walk through a case study that combines multiple IoT and ML technologies to monitor and optimize an industrial heating and cooling HVAC system. Through this instructive example you'll see how the following components can be put into action:
1. A StreamSets data pipeline that sources from MQTT and persists to OpenTSDB
2. A TensorFlow model that predicts anomalies in streaming sensor data
3. A Spark application that derives new event streams for real-time alerts
4. A Grafana dashboard that displays factory sensors and alerts in an interactive view
By walking through this solution step-by-step, you'll learn how to build the fundamental capabilities needed in order to handle endless streams of IoT data and derive ML insights from that data:
1. How to transport IoT data through scalable publish/subscribe event streams
2. How to process data streams with transformations and filters
3. How to persist data streams with the timeliness required for interactive dashboards
4. How to collect labeled datasets for training machine learning models
At the end of this presentation you will have learned how a variety of tools can be used together to build ML enhanced applications and data products for instrumented manufacturing systems.
Speakers
Ian Downard, Sr. Developer Evangelist, MapR
William Ochandarena, Senior Director of Product Management, MapR
If you understand the rule engine, especially how works RETE algorithm, You may use this for Machine Learning. This slide used at Red Hat Forum Tokyo 2018 session.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
How to build containerized architectures for deep learning - Data Festival 20...Antje Barth
When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm – it’s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production! Presented at Data Festival Munich, 2019.
As the shortage of trained data scientists threatens to prevent firms from reaching their analytical potential, a new class of products and services is emerging that promises to relieve the stress on enterprise management. These new tools are making it easier for “citizen data scientists” to create and use models based on their understanding of the business logic and their data, rather than data science fundamentals.
This webinar will present an overview of the new tool landscape and highlight features, benefits, and potential pitfalls for naive adopters. Will they eliminate the need for Data Scientists? Not yet, but they may be just what your firm needs now.
Big Data LDN 2018: DATA OPERATIONS PROBLEMS CREATED BY DEEP LEARNING, AND HOW...Matt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 10:30 - 11:00
Speaker: Jim Scott
Organisation: MapR
About: There are numerous problems which have been exposed by deep learning models due to the sheer ability of the current generation of GPUs to create and run a large volume of models, and we are going to show people how to fix them. The exponential compute growth which has occurred in this area has opened the doors to creating and testing hundreds or thousands more models than the, one-by-one by hand which was performed in the past. These models use and generate data for both batch and real-time as well as training and scoring use cases. As data becomes enriched, and model parameters are explored, there is a real need for versioning everything, including the data. Many of these issues are similar to other software engineering problems, but new approaches must be taken to create solutions given the complexity of the problems. We will discuss what exactly these problems are, how they came to be and how to fix them.
Saama Presents Is your Big Data Solution Ready for StreamingSaama
Amit Gulwadi and Karim Damji presented at Panagora's IoT in Clinical Trials Summit in Boston in November 2018. Using the right analytic solution that can incorporate your unstructured IoT data provides tremendous benefits including faster time to commercialization and better business and patient outcomes.
Big Data LDN 2018: LESSONS LEARNED FROM DEPLOYING REAL-WORLD AI SYSTEMSMatt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 13:50 - 14:20
Speaker: Joshua Robinson
Organisation: Pure Storage
About: While Artificial Intelligence is fueling amazing innovation in many industries, deep learning and various related technologies remain a giant mystery for most looking to get started. At Pure Storage, we helped build and deploy some of the most advanced infrastructure for AI, including powerful AI supercomputers and systems for autonomous driving software. This session will share the top five lessons learned in making AI initiatives successful in real-world deployments.
Softing seeberg data-driven production optimization_170712Peter Seeberg
Industrial Edge Analytics providing Real-time, On-Site Insight
Ruling data and the resulting sequence change from “algorithm -> data -> decision” towards “data -> algorithm -> decision“ represents the actual revolution taking place in front of our eyes. Industrie 4.0 may eventually result in an autonomous economy in which algorithms communicate with each other for the higher well-being of man. Before we get there algorithms can help improve Overall Equipment Effectiveness (OEE), representing machine availability and performance as well as quality of produced goods. The goal – improving OEE – is not new. New is the data-based approach by means of machine learning algorithms. Despite numerous endeavors from providers and organizations, most decision makers have a bad feeling when suggested to move their production data into the cloud. By means of an „edge“ solution close to or part of field devices and machines, data stays in the production line and is processed on the spot. Additional security precautions are unnecessary.
Processing malaria HTS results using KNIME: a tutorialGreg Landrum
Walks through a couple of KNIME Workflows for working with HTS Data.
The workflows are derived from the work described in this publication: https://f1000research.com/articles/6-1136/v2
Heimann Sensor 32 x 32-array thermopile LWIR image sensor with silicon lens 2...system_plus
A small, easy to use, low-power, cheap non-contact temperature measurement for varying applications.
More information on that report at http://www.systemplus.fr/reverse-costing-reports/heimann-sensor-32-x-32-array-thermopile-lwir-image-sensor-with-silicon-lens/
Open Source for Industry 4.0 – Open IoT Summit NA 2018Benjamin Cabé
Industry 4.0 is set to revolutionize the manufacturing industry. The potential for more flexible manufacturing, more efficient processes and lower costs are the driving factors behind the investment in Industry 4.0 solutions. A key part of creating successful Industry 4.0 solutions will be software on the factory floor and in the cloud.
In this talk, we will introduce how open source software has become a trusted source of technology for the enterprise IT software industry and how the Eclipse IoT open source community and other open source communities are now ready to provide production ready technology for the manufacturing industry and Industry 4.0. Open source software will provide the key building blocks that will promote the interoperability and flexibility required by Industry 4.0 solutions.
Designing data pipelines for analytics and machine learning in industrial set...DataWorks Summit
Machine learning has made it possible for technologists to do amazing things with data. Its arrival coincides with the evolution of networked manufacturing systems driven by IoT. In this presentation we’ll examine the rise of IoT and ML from a practitioners perspective to better understand how applications of AI can be built in industrial settings. We'll walk through a case study that combines multiple IoT and ML technologies to monitor and optimize an industrial heating and cooling HVAC system. Through this instructive example you'll see how the following components can be put into action:
1. A StreamSets data pipeline that sources from MQTT and persists to OpenTSDB
2. A TensorFlow model that predicts anomalies in streaming sensor data
3. A Spark application that derives new event streams for real-time alerts
4. A Grafana dashboard that displays factory sensors and alerts in an interactive view
By walking through this solution step-by-step, you'll learn how to build the fundamental capabilities needed in order to handle endless streams of IoT data and derive ML insights from that data:
1. How to transport IoT data through scalable publish/subscribe event streams
2. How to process data streams with transformations and filters
3. How to persist data streams with the timeliness required for interactive dashboards
4. How to collect labeled datasets for training machine learning models
At the end of this presentation you will have learned how a variety of tools can be used together to build ML enhanced applications and data products for instrumented manufacturing systems.
Speakers
Ian Downard, Sr. Developer Evangelist, MapR
William Ochandarena, Senior Director of Product Management, MapR
If you understand the rule engine, especially how works RETE algorithm, You may use this for Machine Learning. This slide used at Red Hat Forum Tokyo 2018 session.
Smart Data Webinar: Machine Learning UpdateDATAVERSITY
Machine Learning (ML) approaches and their supporting technologies can generally be classified as Supervised vs Unsupervised, and within those categories as General or Deep Learning (with Reinforcement Learning as a special case within Supervised Learning). The approaches may be based on biological models or statistical models, or hybrids. As demand for machine learning functionality in consumer and enterprise applications increases, it becomes important to have a framework for comparing ML products and services.
This webinar will present an overview of the machine learning landscape, from platform providers to point solutions in each major ML category, and help participants understand their options for experimentation and deployment of ML-based applications.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
How to build containerized architectures for deep learning - Data Festival 20...Antje Barth
When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm – it’s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production! Presented at Data Festival Munich, 2019.
As the shortage of trained data scientists threatens to prevent firms from reaching their analytical potential, a new class of products and services is emerging that promises to relieve the stress on enterprise management. These new tools are making it easier for “citizen data scientists” to create and use models based on their understanding of the business logic and their data, rather than data science fundamentals.
This webinar will present an overview of the new tool landscape and highlight features, benefits, and potential pitfalls for naive adopters. Will they eliminate the need for Data Scientists? Not yet, but they may be just what your firm needs now.
Big Data LDN 2018: DATA OPERATIONS PROBLEMS CREATED BY DEEP LEARNING, AND HOW...Matt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 10:30 - 11:00
Speaker: Jim Scott
Organisation: MapR
About: There are numerous problems which have been exposed by deep learning models due to the sheer ability of the current generation of GPUs to create and run a large volume of models, and we are going to show people how to fix them. The exponential compute growth which has occurred in this area has opened the doors to creating and testing hundreds or thousands more models than the, one-by-one by hand which was performed in the past. These models use and generate data for both batch and real-time as well as training and scoring use cases. As data becomes enriched, and model parameters are explored, there is a real need for versioning everything, including the data. Many of these issues are similar to other software engineering problems, but new approaches must be taken to create solutions given the complexity of the problems. We will discuss what exactly these problems are, how they came to be and how to fix them.
Saama Presents Is your Big Data Solution Ready for StreamingSaama
Amit Gulwadi and Karim Damji presented at Panagora's IoT in Clinical Trials Summit in Boston in November 2018. Using the right analytic solution that can incorporate your unstructured IoT data provides tremendous benefits including faster time to commercialization and better business and patient outcomes.
Big Data LDN 2018: LESSONS LEARNED FROM DEPLOYING REAL-WORLD AI SYSTEMSMatt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 13:50 - 14:20
Speaker: Joshua Robinson
Organisation: Pure Storage
About: While Artificial Intelligence is fueling amazing innovation in many industries, deep learning and various related technologies remain a giant mystery for most looking to get started. At Pure Storage, we helped build and deploy some of the most advanced infrastructure for AI, including powerful AI supercomputers and systems for autonomous driving software. This session will share the top five lessons learned in making AI initiatives successful in real-world deployments.
Softing seeberg data-driven production optimization_170712Peter Seeberg
Industrial Edge Analytics providing Real-time, On-Site Insight
Ruling data and the resulting sequence change from “algorithm -> data -> decision” towards “data -> algorithm -> decision“ represents the actual revolution taking place in front of our eyes. Industrie 4.0 may eventually result in an autonomous economy in which algorithms communicate with each other for the higher well-being of man. Before we get there algorithms can help improve Overall Equipment Effectiveness (OEE), representing machine availability and performance as well as quality of produced goods. The goal – improving OEE – is not new. New is the data-based approach by means of machine learning algorithms. Despite numerous endeavors from providers and organizations, most decision makers have a bad feeling when suggested to move their production data into the cloud. By means of an „edge“ solution close to or part of field devices and machines, data stays in the production line and is processed on the spot. Additional security precautions are unnecessary.
Processing malaria HTS results using KNIME: a tutorialGreg Landrum
Walks through a couple of KNIME Workflows for working with HTS Data.
The workflows are derived from the work described in this publication: https://f1000research.com/articles/6-1136/v2
Similar to Webinar vogel it_so geht industrial edge analytics mittels machine learning_180525 (20)
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Und nebenbei auch noch über die Rolle von Daten und leistungsfähigen Prozessoren
Mitgründer des Unternehmens Netscape Communications und Entwickler von Mosaic, einer der ersten international weit verbreiteten Webbrowser.
Die Dienste der Edge App verringern signifikant das zu übertragende Datenvolumen und damit den Datenaustausch und die Übertragungsstrecke, wodurch sich die Übertragungskosten und die Wartezeiten verringern und sich die Servicequalität insgesamt verbessert. Beim Edge Computing sind zentrale Rechenzentren seltener bzw. überhaupt nicht notwendig, wodurch ein größerer Flaschenhals für den Datentransfer und eine potentielle Fehlerquelle vermieden werden.
Die Sicherheit verbessert sich ebenfalls, da verschlüsselte Dateien näher am Netzwerkkern verarbeitet werden. Wenn die Daten das Unternehmen erreichen, können Viren, verfälschte Daten und Hackerangriffe frühzeitig abgefangen werden.
Letztendlich erweitert die Fähigkeit zur Virtualisierung die Skalierbarkeit, was bedeutet, dass sich die Anzahl der Edge-Geräte im Netzwerk problemlos steigern lässt. Beim Edge Computing werden Echtzeit-Anforderungen im Internet der Dinge besser unterstützt als dies in der Cloud der Fall ist
ML ist eine eigenständige Disziplin, die häufig
mit KI (Künstliche Intelligenz) verwechselt wird;
der Begriff KI stammt aus dem Jahre 1956 und
ist damit nur geringfügig älter. Er bezeichnet
den Versuch, eine menschenähnliche Intelligenz
nachzubilden. Das ML kann auf diesem Weg ein
erster, erfolgreicher Schritt sein, weshalb ML
gerne als Teilbereich der KI verstanden wird.
Doch nicht nur die Ziele dieser beiden
Disziplinen sind von unter-schiedlicher Größe —
es gibt einen weitaus wichtigeren Unterschied:
ML ist schon da, ist bereits unter uns; wann wir
das von der Künstlichen Intelligenz behaupten
können, steht dagegen in den Sternen.
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AI at Google: our principles
Sundar Pichai
CEO
Published Jun 7, 2018
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New York Times: Using AI to host better conversations
At its heart, AI is computer programming that learns and adapts. It can’t solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful—from email that’s spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy.
Beyond our products, we’re using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires. Farmers are using it to monitor the health of their herds. Doctors are starting to use AI to help diagnose cancer and prevent blindness. These clear benefits are why Google invests heavily in AI research and development, and makes AI technologies widely available to others via our tools and open-source code.
We recognize that such powerful technology raises equally powerful questions about its use. How AI is developed and used will have a significant impact on society for many years to come. As a leader in AI, we feel a deep responsibility to get this right. So today, we’re announcing seven principles to guide our work going forward. These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions.
We acknowledge that this area is dynamic and evolving, and we will approach our work with humility, a commitment to internal and external engagement, and a willingness to adapt our approach as we learn over time.
Objectives for AI applications
We will assess AI applications in view of the following objectives. We believe that AI should:
1. Be socially beneficial.
The expanded reach of new technologies increasingly touch society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides.
AI also enhances our ability to understand the meaning of content at scale. We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non-commercial basis.
2. Avoid creating or reinforcing unfair bias.
AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.
3. Be built and tested for safety.
We will continue to develop and apply strong safety and security practices to avoid unintended results that create risks of harm. We will design our AI systems to be appropriately cautious, and seek to develop them in accordance with best practices in AI safety research. In appropriate cases, we will test AI technologies in constrained environments and monitor their operation after deployment.
4. Be accountable to people.
We will design AI systems that provide appropriate opportunities for feedback, relevant explanations, and appeal. Our AI technologies will be subject to appropriate human direction and control.
5. Incorporate privacy design principles.
We will incorporate our privacy principles in the development and use of our AI technologies. We will give opportunity for notice and consent, encourage architectures with privacy safeguards, and provide appropriate transparency and control over the use of data.
6. Uphold high standards of scientific excellence.
Technological innovation is rooted in the scientific method and a commitment to open inquiry, intellectual rigor, integrity, and collaboration. AI tools have the potential to unlock new realms of scientific research and knowledge in critical domains like biology, chemistry, medicine, and environmental sciences. We aspire to high standards of scientific excellence as we work to progress AI development.
We will work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will responsibly share AI knowledge by publishing educational materials, best practices, and research that enable more people to develop useful AI applications.
7. Be made available for uses that accord with these principles.
Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors:
Primary purpose and use: the primary purpose and likely use of a technology and application, including how closely the solution is related to or adaptable to a harmful use
Nature and uniqueness: whether we are making available technology that is unique or more generally available
Scale: whether the use of this technology will have significant impact
Nature of Google’s involvement: whether we are providing general-purpose tools, integrating tools for customers, or developing custom solutions
AI applications we will not pursue
In addition to the above objectives, we will not design or deploy AI in the following application areas:
Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints.
Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people.
Technologies that gather or use information for surveillance violating internationally accepted norms.
Technologies whose purpose contravenes widely accepted principles of international law and human rights.
We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. These include cybersecurity, training, military recruitment, veterans’ healthcare, and search and rescue. These collaborations are important and we’ll actively look for more ways to augment the critical work of these organizations and keep service members and civilians safe.
AI for the long term
While this is how we’re choosing to approach AI, we understand there is room for many voices in this conversation. As AI technologies progress, we’ll work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will continue to share what we’ve learned to improve AI technologies and practices.
We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. There we made clear our intention to take a long-term perspective, even if it means making short-term tradeoffs. We said it then, and we believe it now.
ML ist eine eigenständige Disziplin, die häufig
mit KI (Künstliche Intelligenz) verwechselt wird;
der Begriff KI stammt aus dem Jahre 1956 und
ist damit nur geringfügig älter. Er bezeichnet
den Versuch, eine menschenähnliche Intelligenz
nachzubilden. Das ML kann auf diesem Weg ein
erster, erfolgreicher Schritt sein, weshalb ML
gerne als Teilbereich der KI verstanden wird.
Doch nicht nur die Ziele dieser beiden
Disziplinen sind von unter-schiedlicher Größe —
es gibt einen weitaus wichtigeren Unterschied:
ML ist schon da, ist bereits unter uns; wann wir
das von der Künstlichen Intelligenz behaupten
können, steht dagegen in den Sternen.
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AI at Google: our principles
Sundar Pichai
CEO
Published Jun 7, 2018
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At its heart, AI is computer programming that learns and adapts. It can’t solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful—from email that’s spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy.
Beyond our products, we’re using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires. Farmers are using it to monitor the health of their herds. Doctors are starting to use AI to help diagnose cancer and prevent blindness. These clear benefits are why Google invests heavily in AI research and development, and makes AI technologies widely available to others via our tools and open-source code.
We recognize that such powerful technology raises equally powerful questions about its use. How AI is developed and used will have a significant impact on society for many years to come. As a leader in AI, we feel a deep responsibility to get this right. So today, we’re announcing seven principles to guide our work going forward. These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions.
We acknowledge that this area is dynamic and evolving, and we will approach our work with humility, a commitment to internal and external engagement, and a willingness to adapt our approach as we learn over time.
Objectives for AI applications
We will assess AI applications in view of the following objectives. We believe that AI should:
1. Be socially beneficial.
The expanded reach of new technologies increasingly touch society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides.
AI also enhances our ability to understand the meaning of content at scale. We will strive to make high-quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non-commercial basis.
2. Avoid creating or reinforcing unfair bias.
AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.
3. Be built and tested for safety.
We will continue to develop and apply strong safety and security practices to avoid unintended results that create risks of harm. We will design our AI systems to be appropriately cautious, and seek to develop them in accordance with best practices in AI safety research. In appropriate cases, we will test AI technologies in constrained environments and monitor their operation after deployment.
4. Be accountable to people.
We will design AI systems that provide appropriate opportunities for feedback, relevant explanations, and appeal. Our AI technologies will be subject to appropriate human direction and control.
5. Incorporate privacy design principles.
We will incorporate our privacy principles in the development and use of our AI technologies. We will give opportunity for notice and consent, encourage architectures with privacy safeguards, and provide appropriate transparency and control over the use of data.
6. Uphold high standards of scientific excellence.
Technological innovation is rooted in the scientific method and a commitment to open inquiry, intellectual rigor, integrity, and collaboration. AI tools have the potential to unlock new realms of scientific research and knowledge in critical domains like biology, chemistry, medicine, and environmental sciences. We aspire to high standards of scientific excellence as we work to progress AI development.
We will work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will responsibly share AI knowledge by publishing educational materials, best practices, and research that enable more people to develop useful AI applications.
7. Be made available for uses that accord with these principles.
Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors:
Primary purpose and use: the primary purpose and likely use of a technology and application, including how closely the solution is related to or adaptable to a harmful use
Nature and uniqueness: whether we are making available technology that is unique or more generally available
Scale: whether the use of this technology will have significant impact
Nature of Google’s involvement: whether we are providing general-purpose tools, integrating tools for customers, or developing custom solutions
AI applications we will not pursue
In addition to the above objectives, we will not design or deploy AI in the following application areas:
Technologies that cause or are likely to cause overall harm. Where there is a material risk of harm, we will proceed only where we believe that the benefits substantially outweigh the risks, and will incorporate appropriate safety constraints.
Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people.
Technologies that gather or use information for surveillance violating internationally accepted norms.
Technologies whose purpose contravenes widely accepted principles of international law and human rights.
We want to be clear that while we are not developing AI for use in weapons, we will continue our work with governments and the military in many other areas. These include cybersecurity, training, military recruitment, veterans’ healthcare, and search and rescue. These collaborations are important and we’ll actively look for more ways to augment the critical work of these organizations and keep service members and civilians safe.
AI for the long term
While this is how we’re choosing to approach AI, we understand there is room for many voices in this conversation. As AI technologies progress, we’ll work with a range of stakeholders to promote thoughtful leadership in this area, drawing on scientifically rigorous and multidisciplinary approaches. And we will continue to share what we’ve learned to improve AI technologies and practices.
We believe these principles are the right foundation for our company and the future development of AI. This approach is consistent with the values laid out in our original Founders’ Letter back in 2004. There we made clear our intention to take a long-term perspective, even if it means making short-term tradeoffs. We said it then, and we believe it now.
3.4 Milliarden Transistoren
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Wir arbeiten uns fast immer mit Zeitreihen;
also mit Daten die nicht kontinuierlich, sondern diskret in zeitlichen Abständen anfallen (kontinuierliche Daten können gesampled / abgetastet werden, also zu festen Zeiten ausgelesen werden.);
und zwar Daten von Steuerungen, Aktoren, Sensoren aus der Fabrik, sowie eventuell aus der direkten Umgebung anfallende Daten wie Temperatur und Luftfeuchtigkeit;
aus dem Grund nicht univariate, also einzelne sondern multivariate, also aus einer Mehrzahl von Zahlenwerten;
meistens äquidistant, also in konstanten Abständen;
Und zwar suchen wir meistens in der Zeitreihe auftretende Anomalien;
Also mit Daten die sich anders verhalten als erwartet;
(wie vorher erwähnt, geht es beim maschinellen Lernen um Vorhersage)
Know-how in Soft-
and Hardware
next thing I look at now is: can we actually use a distance-measure from the clustering to find anomalies? Here is the intuition1) take a time area of "good" operation2) make clusters3) score on another area4) rate a point as outlier if is is "far away" from a known cluster4.1. far away: I took the zScore based on the distance distribution of points assigned to a cluster as reference
2:20
here's a result:
on the right side we see the scoring based on only the clusters of good operation. the background colors are: red: zScore is >4.75 (~1 out of a million), and blue, yellow are respectively 1 out of 100.000, 10.000
2:23
we can clearly see that the paper rip is in the "1 out of a million" zScore-area. So yes, for this example, we can learn a "good operation" and a zScore on the cluster distance density distribution gives us a measure of anomaly :slightly_smiling_face:
2:23
... I again used a PCA for 3 dimensions and k-means with k=5.
2:25
One lucky coincidence I had was that the clusters are obviously "good-natured": k-Means can only handle convex / ellipsoid contours, and for the data (at least after PCA), this was the case
2:27
We also see on the right side, that there are "anomalies" even in the good operation area: These are the transitions between clusters, if we move from one cluster to another over time, we will pass the tail of probability distributions of the clusters until we enter the new state, so, yes: if the clusters represent a stable system status, then the transition is also a rare event, and from this understanding an "anomaly", but it's only a short time (the transition time)
next thing I look at now is: can we actually use a distance-measure from the clustering to find anomalies? Here is the intuition1) take a time area of "good" operation2) make clusters3) score on another area4) rate a point as outlier if is is "far away" from a known cluster4.1. far away: I took the zScore based on the distance distribution of points assigned to a cluster as reference
2:20
here's a result:
on the right side we see the scoring based on only the clusters of good operation. the background colors are: red: zScore is >4.75 (~1 out of a million), and blue, yellow are respectively 1 out of 100.000, 10.000
2:23
we can clearly see that the paper rip is in the "1 out of a million" zScore-area. So yes, for this example, we can learn a "good operation" and a zScore on the cluster distance density distribution gives us a measure of anomaly :slightly_smiling_face:
2:23
... I again used a PCA for 3 dimensions and k-means with k=5.
2:25
One lucky coincidence I had was that the clusters are obviously "good-natured": k-Means can only handle convex / ellipsoid contours, and for the data (at least after PCA), this was the case
2:27
We also see on the right side, that there are "anomalies" even in the good operation area: These are the transitions between clusters, if we move from one cluster to another over time, we will pass the tail of probability distributions of the clusters until we enter the new state, so, yes: if the clusters represent a stable system status, then the transition is also a rare event, and from this understanding an "anomaly", but it's only a short time (the transition time)
Well osoa was initially meant to be an onsite offline system, with easy to use autonomous analytics. We are still working on getting the autonomous aspect better, but thats the ideaAnd yes, we use osoa as an offsite solution today, but I hope we can make a self service onsite out of itI'm totally convinced that onsite is a must if we want to position seriously as edge technology provider. Because offsite will have to compare to cloud/private cloud always. But osoa is a python in-memory system, which is made for edge
What you see here is my first trial on density-based clustering
(this time the DBSCAN), it builds cluster unsupervised based on local density
it's Prinovis data (100 sensors, features extracted with sliding window, then PCA to reduce to 5 dimensions):
We see 3 sensors and the two clusters (violet: gurt, green: pendelwalze, red: Leitgeschwindigkeit,
then blue: clusternumer with k-means,
orange: clusternumber with DBSCAN.
As a comparison, the DBSCAN seams to be more precise in distiguishing clusters, it actually has build around 10 cluster, where the 5 means was told to do only 5.
For the DBSCAN, there is no such setting, it finds the clusters by itself
Now what am I after with this? The DBSCAN look similar to k-means results, right?
Well, the densitiy-based clustering can do anomaly detection in the scoring phase. The "density of values" will not be very high for anomaly value.
k-Means instead can't do anomaly in scoring, unless you abuse k-Means by building in e.g. a limit based on previous data
Another important thing to mention: the distance metric for k-Means is typically euclidian, that means we cannot support concave structures. Density-based approaches can support any shape of data clusters.
here is another nice visualization of the k-Means capabilities, I used raw data with PCA to 3 dimensions (for a nice 3d-plot) and then a k-Means(k=5) for clustering:
on the right side, we see the time series with the background colors of the clusters, on the left we see the pca-vectors in a 3d
11:14
What is interesting is to see that the paperrips (dark blue, green clusters) are clearly separated from the "normal operation" like the light blue and pink
The ROC curve shows classification performance as a trade off between selectivity and sensitivity at different detection threshold levels.
It is useful in the selection of the optimal threshold as well as for choosing between competing models.
Its usefulness is limited in the case of imbalanced classes, because it only focuses on the positive instances. In such scenarios, other measures are preferred (such as precision-recall AUC).
*The model evaluation metrics depend on the type of model used as well as the characteristics of the dataset used (balanced vs imbalanced, binary vs multi-class classification, costs assigned to misclassifications of certain classes etc.)