This document is a keynote presentation about the growing field of data professionals given by Steven Miller at the 2nd EDISON Champions Conference in Madrid, Spain on March 15, 2017. The presentation discusses the rapid growth of data jobs and emerging roles like data scientists, data engineers, and chief data officers. It also examines trends shaping the future like artificial intelligence, the internet of things, and the need for improved data literacy and data-driven decision making across all professions.
Preparing the next generation for the cognitive era - NFAIS KeynoteSteven Miller
Keynote address at NFAIS 2016 in Philadelphia PA on February 21st 2016 focused on how the Cogntive Era is transforming our lives, creating new careers, and inspiring innovation.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
valohai에서 발표한 2021, State of MLOps 2021 survey 자료를 요약하여 정리한 것입니다. 조직내에서 MLOps 와 관련하여 역할과 팀의 규모, 집중하는 영역, 현재 툴링화 하여 사용하고 있는 영역 등에 대한 100명의 응답자 내용을 정리한 것입니다.
This presentation was delivered to students soon to complete undergraduate and masters degrees in technology and IT disciplines at Oxford Brookes University. The presentation highlights five "hot" areas of demand in the current IT jobs market, and offers resources and free or low cost certifications to allow candidates to "upskill".
Preparing the next generation for the cognitive era - NFAIS KeynoteSteven Miller
Keynote address at NFAIS 2016 in Philadelphia PA on February 21st 2016 focused on how the Cogntive Era is transforming our lives, creating new careers, and inspiring innovation.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
valohai에서 발표한 2021, State of MLOps 2021 survey 자료를 요약하여 정리한 것입니다. 조직내에서 MLOps 와 관련하여 역할과 팀의 규모, 집중하는 영역, 현재 툴링화 하여 사용하고 있는 영역 등에 대한 100명의 응답자 내용을 정리한 것입니다.
This presentation was delivered to students soon to complete undergraduate and masters degrees in technology and IT disciplines at Oxford Brookes University. The presentation highlights five "hot" areas of demand in the current IT jobs market, and offers resources and free or low cost certifications to allow candidates to "upskill".
Preparing the next generation for the cognitive eraSteven Miller
Short version of my latest presentation used during a panel session at the ASA Research Symposium at Southern Illinois University Carbondale on November 21st 2015
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...Dr. Haxel Consult
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
Dcaf transformation & kg adoption 2022 -alan morrisonAlan Morrison
A keynote presentation on knowledge graph adoption trends and how to do digital transformation differently.
Delivered at the Enterprise Data Transformation & Knowledge Graph Adoption
A Semantic Arts DCAF Event
February 28, 2022
Emerging opportunities in the age of dataEjaz Siddiqui
We live in a data-driven world. There are more than 4 billion people around the world using the internet.
This show an unprecedented spread and growth of digital devices. These digital devices (Mobiles, Computers, Watches, IoT etc) are the factories for creating data. It means we live in the Age of Data, and it’s expanding at astonishing rates. We may need to unplug and take a break from time to time, but data never sleeps.
This generation of huge data presents many new challenges as well as opportunities. There would be huge opportunity for the people who could collect, process, manage, drive insights and make useful decisions from this data. Certain fields are becoming very important and necessary to manage and process this data.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Big data course | big data training | big data classesNaviWalker
In your world of digitization, Data is an essential source. Businesses in various fields use this Data to get important ideas for their growth. Eventually, this creates a sense of urgency to start learning Big Data. By doing so, you can stay productive and solve real world problems.
Big Data helps to derive important business decisions. Furthermore, successful Big Data processing in huge industrial sectors has taught important lessons on various Big Data concepts.
Big Data training with various Big Data Analytics courses will help you master Data Analysis. In the present world, you have ample scope of becoming a Big Data Scientist. And also getting other Big Data job roles.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleVasu S
Real-world data science practitioners offer perspectives and advice on six common Machine Learning problems
https://www.qubole.com/resources/ebooks/oreilly-ebook-machine-learning-at-enterprise-scale
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
The current challenges and opportunities of big data and analytics in emergen...IBM Analytics
Big data and analytics present many possibilities for emergency management specialists and first responders. Some of these benefits include pinpointing vulnerabilities, bringing in the right resources and maximizing existing resources to pave the way to adoption. However, these opportunities are not without challenges. Emergency management experts Adam Crowe, Director, Emergency Preparedness at Virginia Commonwealth University; William Moorhead, President of All Clear Emergency Management Group; and Gary Nestler, Associate Partner and Global Leader, Emergency Management solutions at IBM discuss these challenges and opportunities in this slideshare—which is intended to help disaster management stakeholders achieve the most accurate situational awareness using analytics.
Discover analytics solutions for emergency management http://ibm.co/emergencymgmt
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...Cristene Gonzalez-Wertz
Summary of speech provided at Taipei Computer Association Event June 27, 2017 - Artificial Intelligence Use Cases especially for Industrial Markets and Electronics
Beyond hype why artificial intelligence is the real deal - evanta nycCristene Gonzalez-Wertz
15 real examples of how artificial intelligence is changing the game. From easy to understand definitions you'll get a sense of the wide range of AI use cases as well as a template you can use. Youtube accompanying playlist: https://www.youtube.com/playlist?list=PLxRr8_nhJ6RmUrFudVS6kaehIrmJ9Cu_p
Preparing the next generation for the cognitive eraSteven Miller
Short version of my latest presentation used during a panel session at the ASA Research Symposium at Southern Illinois University Carbondale on November 21st 2015
AI-SDV 2020: AI, IoT, Blockchain & Co: How to keep track and take advantage o...Dr. Haxel Consult
The presentation first addresses 6 key questions: Where do we stand with the diversification of AI? Machines are getting better at understanding - where are we with writing? Has AI Research hit a wall in 2020? What's the progress made in AI ethics? Could synthetically created data make AI cheaper? And: Are deep fake deployments of AI becoming more insidious? The presentation then moves on to discuss the 4th generation of AI: Artificial Intuition. It discusses the lines of effort in AI of the US Government and concludes with three actualities on AI-based decision making, COVID-loneliness detection and extrapolation of AI into the long run.
Thank you for your interest in the recent NY Outthink breakfast on July 19th at the Rainbow Room. Presentations shared highlighted how cognitive computing is being applied today in a variety of business situations, in many industries, and across multiple business functions. Presentation by Jason Kelley
Dcaf transformation & kg adoption 2022 -alan morrisonAlan Morrison
A keynote presentation on knowledge graph adoption trends and how to do digital transformation differently.
Delivered at the Enterprise Data Transformation & Knowledge Graph Adoption
A Semantic Arts DCAF Event
February 28, 2022
Emerging opportunities in the age of dataEjaz Siddiqui
We live in a data-driven world. There are more than 4 billion people around the world using the internet.
This show an unprecedented spread and growth of digital devices. These digital devices (Mobiles, Computers, Watches, IoT etc) are the factories for creating data. It means we live in the Age of Data, and it’s expanding at astonishing rates. We may need to unplug and take a break from time to time, but data never sleeps.
This generation of huge data presents many new challenges as well as opportunities. There would be huge opportunity for the people who could collect, process, manage, drive insights and make useful decisions from this data. Certain fields are becoming very important and necessary to manage and process this data.
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Gene Villeneuve - Moving from descriptive to cognitive analyticsIBM Sverige
As the scope of big data rapidly expands, so does the scope of the analytics that are necessary to extract insight from that data. It is simply impossible for humans or indeed rules-based engines to take that information to action. More and more, clients need analytics to make the best decisions possible; or better yet, embed those analytics into processes to automate the decision-making process, which they simply the answers based on the questions being asked at the point of impact. In order to address these rapidly evolving needs, we need to ensure the right analytics capability are deployed to suit each situation, each point of interaction and each decision point within a process. Join this session, and learn how IBM can provide a solution for the varying types of analytics: from descriptive to predictive to prescriptive to cognitive.
Big data course | big data training | big data classesNaviWalker
In your world of digitization, Data is an essential source. Businesses in various fields use this Data to get important ideas for their growth. Eventually, this creates a sense of urgency to start learning Big Data. By doing so, you can stay productive and solve real world problems.
Big Data helps to derive important business decisions. Furthermore, successful Big Data processing in huge industrial sectors has taught important lessons on various Big Data concepts.
Big Data training with various Big Data Analytics courses will help you master Data Analysis. In the present world, you have ample scope of becoming a Big Data Scientist. And also getting other Big Data job roles.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleVasu S
Real-world data science practitioners offer perspectives and advice on six common Machine Learning problems
https://www.qubole.com/resources/ebooks/oreilly-ebook-machine-learning-at-enterprise-scale
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
The current challenges and opportunities of big data and analytics in emergen...IBM Analytics
Big data and analytics present many possibilities for emergency management specialists and first responders. Some of these benefits include pinpointing vulnerabilities, bringing in the right resources and maximizing existing resources to pave the way to adoption. However, these opportunities are not without challenges. Emergency management experts Adam Crowe, Director, Emergency Preparedness at Virginia Commonwealth University; William Moorhead, President of All Clear Emergency Management Group; and Gary Nestler, Associate Partner and Global Leader, Emergency Management solutions at IBM discuss these challenges and opportunities in this slideshare—which is intended to help disaster management stakeholders achieve the most accurate situational awareness using analytics.
Discover analytics solutions for emergency management http://ibm.co/emergencymgmt
Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan compu...Cristene Gonzalez-Wertz
Summary of speech provided at Taipei Computer Association Event June 27, 2017 - Artificial Intelligence Use Cases especially for Industrial Markets and Electronics
Beyond hype why artificial intelligence is the real deal - evanta nycCristene Gonzalez-Wertz
15 real examples of how artificial intelligence is changing the game. From easy to understand definitions you'll get a sense of the wide range of AI use cases as well as a template you can use. Youtube accompanying playlist: https://www.youtube.com/playlist?list=PLxRr8_nhJ6RmUrFudVS6kaehIrmJ9Cu_p
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
The 5 Biggest Enterprise Architecture challenges solved with real-time metric...LeanIX GmbH
Find out how you can rethink Enterprise Architecture by combining your existing Enterprise Architecture (EA) repository with real-time data. This presentation demonstrates five use cases where real-time metrics can help to solve EA problems.
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LeanIX offers an innovative software-as-a-service solution for Enterprise Architecture Management (EAM), based either in a public cloud or the client’s data center.
Companies like Adidas, Axel Springer, Helvetia, RWE, Trusted Shops and Zalando use LeanIX Enterprise Architecture Management tool.
Free Trial: http://bit.ly/LeanIXFreeTrial
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
Achieving digital business requires not just traditional workload automation, but automation that spans operations, development, and business functions. Digital business success requires Digital Business Automation.
Check out these slides from the webinar featuring Dan Twing, president and COO of leading IT analyst firm Enterprise Management Associates (EMA), and Tim Eusterman, senior director solutions marketing at BMC, to discover why automation is at the core of digital business success.
Fighting Financial Crime with Artificial IntelligenceDataWorks Summit
How can we take the state of the art in deep learning and AI research, and transplant it into a large bank to deliver useful results which impact the general public? To answer this broad-reaching question, we take the viewer through a solution Think Big Analytics recently deployed at a major European bank for fraud detection, using state of the art AI techniques and a near-real time open-source architecture. We show how financial transactions can be transposed into a form where the latest AI techniques in image recognition can be leveraged, in surprisingly novel ways. We have been able to more accurately detect fraud and reduce financial crime, cutting losses and improving customer experience. We describe some architectures which can be used to do this in production, at scale, in global financial institutions.
Speaker:
Tim Seears, Director of Data Science, Think Big Analytics, a Teradata Company
Introduction to Predictive Analytics with IBM SPSS. Predictive analytics helps organizations use their data to make better decisions by allowing them to draw reliable, data-driven conclusions about current conditions and future events.
Predictive analytics encompasses a variety of techniques such as Statistics, Game theory and Data mining to do this analysis,
and make these predictions.
So by deploying predictive analytics, organizations are addressing their business issues proactively to get the best outcomes.
Watson and Cognitive Meetup April 2017Rick Osowski
(Presented by Randy Vogel)
Have you heard about cognitive and wondered what's the buzz? Have you heard of Watson and wondered what it is and how it can help you? Are you wondering if that's the same thing that won Jeopardy? Are you curious how you can do more with your data than just react and report?
If these topics are on your mind, join us for an evening featuring Watson and Cognitive Learning, powered by the IBM Cloud. We'll provide an introduction into the opportunity around data and analytics, layer on the advanced abilities of cognitive learning, and show some of Watson's services live.
Whether you're a seasoned data scientist or just beginning to explore the opportunities with data and cognition, you'll find this Meetup perfect to start expanding your possibilities.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
The Data & Analytics Journey – Why it’s more attainable for your company than...John Head
Presented at CampIT Conference on April 13th, 2017 ( http://campconferences.com/events/2017/intelligence.htm )
The typical perception of Big Data, Analytics, and Predicative/AI is that only the big companies can reap the benefits. Many believe they need a data warehouse, expensive reporting software, and an army of data scientists to get any value out of effort and cost. This session will explore and debunk that myth and showcase how companies of any size can participate in the journey. While there are many maturity models and journey maps available, most are not designed to be practical guides to solving common business problems. Because of the explosion in availability in cloud services, the barrier to entry has eroded significantly. During this session, we will look at some practical steps to access these capabilities and provide examples to where market-leading and growth companies have seen large benefits. Attendees will walk away with broader understanding of what’s possible to move their company through the journey in 2017.
“IT Technology Trends in 2017… and Beyond”diannepatricia
William Chamberlin, IBM Distinguished Market Intelligence Professional, presented “IT Technology Trends in 2017… and Beyond” as part of the Cognitive Systems Institute Speaker Series on January 26, 2017.
The data economy is driving an incredible rate of innovation. New job roles are emerging, existing job roles are evolving. While much of the hype has focused on the data scientist role it's just one of many.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Data is the new oil. Our ability to make use of ALL data anytime anywhere is transforming our world.
Data plays a key role in every single one of these trends.
AI and Advanced Machine LearningArtificial intelligence (AI) and advanced machine learning (ML) are composed of many technologies and techniques (e.g., deep learning, neural networks, natural-language processing [NLP]). The more advanced techniques move beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously. This is what makes smart machines appear "intelligent."
"Applied AI and advanced machine learning give rise to a spectrum of intelligent implementations, including physical devices (robots, autonomous vehicles, consumer electronics) as well as apps and services (virtual personal assistants [VPAs], smart advisors), said Mr. Cearley. "These implementations will be delivered as a new class of obviously intelligent apps and things as well as provide embedded intelligence for a wide range of mesh devices and existing software and service solutions."
Intelligent AppsIntelligent apps such as VPAs perform some of the functions of a human assistant making everyday tasks easier (by prioritizing emails, for example), and its users more effective (by highlighting the most important content and interactions). Other intelligent apps such as virtual customer assistants (VCAs) are more specialized for tasks in areas such as sales and customer service. As such, these intelligent apps have the potential to transform the nature of work and structure of the workplace.
"Over the next 10 years, virtually every app, application and service will incorporate some level of AI," said Mr Cearley. "This will form a long-term trend that will continually evolve and expand the application of AI and machine learning for apps and services."
Intelligent Things
Intelligent things refer to physical things that go beyond the execution of rigid programing models to exploit applied AI and machine learning to deliver advanced behaviors and interact more naturally with their surroundings and with people. As intelligent things, such as drones, autonomous vehicles and smart appliances, permeate the environment, Gartner anticipates a shift from stand-alone intelligent things to a collaborative intelligent things model.
Virtual and Augmented RealityImmersive technologies, such as virtual reality (VR) and augmented reality (AR), transform the way individuals interact with one another and with software systems. "The landscape of immersive consumer and business content and applications will evolve dramatically through 2021," said Mr. Cearley. "VR and AR capabilities will merge with the digital mesh to form a more seamless system of devices capable of orchestrating a flow of information that comes to the user as hyperpersonalized and relevant apps and services. Integration across multiple mobile, wearable, Internet of Things (IoT) and sensor-rich environments will extend immersive applications beyond isolated and single-person experiences. Rooms and spaces will become active with things, and their connection through the mesh will appear and work in conjunction with immersive virtual worlds."
Digital Twin A digital twin is a dynamic software model of a physical thing or system that relies on sensor data to understand its state, respond to changes, improve operations and add value. Digital twins include a combination of metadata (for example, classification, composition and structure), condition or state (for example, location and temperature), event data (for example, time series), and analytics (for example, algorithms and rules).
Within three to five years, hundreds of millions of things will be represented by digital twins. Organizations will use digital twins to proactively repair and plan for equipment service, to plan manufacturing processes, to operate factories, to predict equipment failure or increase operational efficiency, and to perform enhanced product development. As such, digital twins will eventually become proxies for the combination of skilled individuals and traditional monitoring devices and controls (for example, pressure gauges, pressure valves).
Blockchain and Distributed LedgersBlockchain is a type of distributed ledger in which value exchange transactions (in bitcoin or other tokens) are sequentially grouped into blocks. Each block is chained to the previous block and recorded across a peer-to-peer network, using cryptographic trust and assurance mechanisms. Blockchain and distributed-ledger concepts are gaining traction because they hold the promise to transform industry operating models. While the current hype is around the financial services industry, there are many possible applications including music distribution, identity verification, title registry and supply chain.
"Distributed ledgers are potentially transformative but most initiatives are still in the early alpha or beta testing stage," said Mr. Cearley.
Conversational SystemThe current focus for conversational interfaces is focused on chatbots and microphone-enabled devices (e.g., speakers smartphones, tablets, PCs, automobiles). However, the digital mesh encompasses an expanding set of endpoints people use to access applicatons and information, or interact with people, social communities, governments, and businesses. The device mesh moves beyond the traditional desktop computer and multiple devices to encompass the full range of endpoints with which humans might interact. As the device mesh evolves, connection models will expand and greater cooperative interaction between devices will emerge, creating the foundation for a new continuous and ambient digital experience.
Mesh App and Service ArchitectureIn the mesh app and service architecture (MASA), mobile apps, web apps, desktop apps and IoT apps link to a broad mesh of back-end services to create what users view as an "application." The architecture encapsulates services and exposes APIs at multiple levels and across organizational boundaries balancing the demand for agility and scalability of services with composition and reuse of services. The MASA enables users to have an optimized solution for targeted endpoints in the digital mesh (e.g., desktop, smartphone, automobile) as well as a continuous experience as they shift across these different channels.
Digital Technology PlatformsDigital technology platforms provide the basic building blocks for a digital business and are a critical enabler to become a digital business. Gartner has identified the five major focal points to enable the new capabilities and business models of digital business — information systems, customer experience, analytics and intelligence, the IoT, and business ecosystems. Every organization will have some mix of these five digital technology platforms. The platforms provide the basic building blocks for a digital business and are a critical enabler to become a digital business.
Adaptive Security Architecture
The intelligent digital mesh and related digital technology platforms and application architectures create an ever-more-complex world for security. "Established security technologies should be used as a baseline to secure Internet of Things platforms," said Mr. Cearley. "Monitoring user and entity behavior is a critical addition that is particularly needed in IoT scenarios. However, the IoT edge is a new frontier for many IT security professionals creating new vulnerability areas and often requiring new remediation tools and processes that must be factored into IoT platform efforts."
This next section provides clear examples of innovating with data.
https://en.wikipedia.org/wiki/IPhone_6
The Apple iWatch has a heart rate sensor
Everything can be measured. Cities are actively measuring air quality, water quality, traffic, transit, noise, weather, tides, people, anything that be sensed & measured.
Data is the new oil. Our ability to make use of ALL data anytime anywhere is transforming our world.
Every professional needs to become a citizen analyst in the data driven economy
Everyone needs data literacy. EVERYONE!!!
Free stock photo from: https://unsplash.com/collections/164957/crowd?photo=TZCppMjaOHU
Human data scientists support the business or lines of business helping them understand and make better decisions.
Machine data scientists drive the machines that run much of the cognitive world.
Michael Li’s article
http://data-informed.com/two-types-of-data-scientists-which-is-right-for-your-needs/
With the data engineer data scientists will accomplish little.
A business leader needs a comprehensive view of data, analytics, and putting it to work
http://bit.ly/ibmcdostudy
The chief data officer is growing fast… not sure what drove the plunge in Indeed’s data but instead focus on the trend line.
Graphic published with permission of ThotWave. https://www.thotwave.com/
Other possible adjectives: Controlled? Pedigreed? Grey Data? Black Data? Version control?
The Data Self by Rob Horning http://thenewinquiry.com/blogs/marginal-utility/dumb-bullshit/
http://www.gartner.com/newsroom/id/2506315
“The underlying message of all these examples is that information is an asset in its own right. It has value. Gartner calls this emerging discipline of valuating information "Infonomics.It is not something of the far future, in fact, this is happening today in various industries, in commerce and public sector, in large and small enterprises.”
However, Mr. Buytendijk underlined the fact that as exciting as all new business opportunities are, there are also reasons for concern. Concerning the ethics of big data, a recent Gartner Circle study showed that "governance and privacy" was the most important concern around big data – clearly there is a fine line between superior customer insight and being "creepy."
In partnership with Oceans of Data we brought a group of experts together from industry & academia to define data & analytics literacy. These are the top level recommendations.
Michael Bowen
Associate Professor, Science Education, Mount Saint Vincent University, Halifax, Nova Scotia
Ben Davison
Quantitative User Experience Researcher, Google
Rob Gould
Faculty, UCLA Department of Statistics
Ryan Kapaun
Crime Analyst, Eden Prairie Police Department
Cliff Konold
Director, Scientific Reasoning Research Institute, University of Massachusetts, Amherst
Juan Miguel Lavista Ferres
Principal Data Scientist at Bing/Microsoft
Odette Merchant
Project Manager, Nova Scotia Community College (NSCC), Halifax, Nova Scotia, Canada
Andrew Schaffner
Professor of Statistics, California Polytechnic State University, San Luis Obispo
Hunter Whitney
Consultant, Author, and Instructor; UX and Data Visualization
Sponsored by Steven Miller
IBM
Moderated by the Oceans of Data Team
Making better decisions requires confidence. Let’s talk about how Watson uses Cognitive to do just that.
In 2011 Watson competed against Ken Jennings & Brad Rutter. As we have all heard, Watson beat the best players in the world. Watson knew more, could answer faster, and pressed the button only when Watson determined its answer a high degree of confidence, otherwise it didn’t push the button at all. IBM donated the $1M prize to charity.
When doctors face challenges they face every day.. Their knowledge and experience is easily up to the task. Many problems they see, however are rare, or at least rare for them. What would happen if we applied Watson to one of the greater challenges of our time? Cancer
NCCN = National Comprehensive Cancer Network
This slide is self explanatory. Yes, Watson has proven to be quite adept at helping doctors diagnose and recommend treatment for cancers. Poor diagnoses are a huge burden on the cost of health care and an even bigger burden on the patient. If the correct treatmet is the 3rd or 4th one tried it’s often too late.