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.
Prof Shane Greenstein of Harvard Business School talks about his new book, How the Internet Became Commercial, at the Digital Initiative's Future Assembly.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
This document discusses designing a better way for people to enjoy flowers in their homes. It emphasizes observing users, reflecting on insights, and making ideas tangible. It also mentions that the cognitive enterprise focuses on simplicity, iterative design, and agility. The document outlines a vision for front office, back office, and whole office transformation using cognitive technology to discover, decide, engage and collaborate through curiosity and innovation.
KM - Cognitive Computing overview by Ken Martin 13Apr2016HCL Technologies
This document provides an introduction to cognitive computing and how it relates to knowledge management strategies. It begins with an overview of Ken Martin's background and the agenda. It then defines key cognitive computing concepts and technologies like natural language processing, machine learning, and pattern recognition. The document contrasts traditional and cognitive systems, noting cognitive systems are interactive, self-learning, and expand conversations. It maps cognitive capabilities to the KM lifecycle, showing how capabilities like natural language processing, text mining, and social network analysis can enhance each stage.
From the conference Future Tech in Insurance at Forsikringsakademiet, nov 15 2016. Defining cognitive and how that is relevant for insurance companies.
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.
Prof Shane Greenstein of Harvard Business School talks about his new book, How the Internet Became Commercial, at the Digital Initiative's Future Assembly.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
SmartData Webinar: Cognitive Computing in the Mobile App EconomyDATAVERSITY
Mobility is transforming work and life throughout the planet. Mobile apps--built for a growing range of handhelds, wearables, Internet of Things, and other platforms--are becoming the universal access paths to commerce, content, and community in the 21st century. The app economy refers to this new world where every decision, action, exploration, and experience is continuously enriched and optimized through the cloud-served apps that accompany you everywhere. In this webinar, James Kobielus, IBM's Big Data Evangelist, will discuss the potential of cognitive computing to super-power the emerging app economy. In addition to providing an overview of IBM's Watson strategy for cognitive computing, Kobielus will go in-depth on IBM's strategic partnership with Apple to draw on the strengths of each company to transform enterprise mobility through a new class of apps that leverage IBM’s Watson-based big data analytics cloud and add value to Apple's iPhone and iPad platforms in diverse industries.
This document discusses designing a better way for people to enjoy flowers in their homes. It emphasizes observing users, reflecting on insights, and making ideas tangible. It also mentions that the cognitive enterprise focuses on simplicity, iterative design, and agility. The document outlines a vision for front office, back office, and whole office transformation using cognitive technology to discover, decide, engage and collaborate through curiosity and innovation.
KM - Cognitive Computing overview by Ken Martin 13Apr2016HCL Technologies
This document provides an introduction to cognitive computing and how it relates to knowledge management strategies. It begins with an overview of Ken Martin's background and the agenda. It then defines key cognitive computing concepts and technologies like natural language processing, machine learning, and pattern recognition. The document contrasts traditional and cognitive systems, noting cognitive systems are interactive, self-learning, and expand conversations. It maps cognitive capabilities to the KM lifecycle, showing how capabilities like natural language processing, text mining, and social network analysis can enhance each stage.
From the conference Future Tech in Insurance at Forsikringsakademiet, nov 15 2016. Defining cognitive and how that is relevant for insurance companies.
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Intelligent Maintenance: Mapping the #IIoT ProcessDan Yarmoluk
A presentation about Industrial IoT, the value chain and real-world use cases; how to create value with IoT at your organization with an emphasis on predictive maintenance (bearing fault detection).
This document discusses various emerging technologies including Internet of Things (IoT), digital transformation, big data, data analytics, machine learning, artificial intelligence, blockchain, Ripple, LiFi, and Mitz technologies. It provides overviews and examples of each technology, noting how IoT is bringing more connected devices and creating challenges around data structures, formats, and analytics. Artificial intelligence can help with IoT data preparation, discovery, visualization, prediction, and geospatial analysis. Blockchain provides benefits for tracking connected devices and enabling secure transactions without centralized control.
Cognitive computing big_data_statistical_analyticsPietro Leo
Cognitive computing, big data, and statistical analytics represent new frontiers for innovation that will transform organizations. These emerging technologies rely on analyzing vast amounts of structured and unstructured data using powerful computers and sophisticated algorithms to generate novel insights. Realizing their full potential will require integrating data and analytics from hundreds or thousands of diverse sources to reduce uncertainty and construct high-value context. This represents a strategic challenge for organizations to create an integrated view of information from all available data channels.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
This document discusses how big data can enable the travel and tourism industries. It defines big data as large datasets characterized by their volume, velocity, variety, and veracity. Big data comes from a variety of sources as people leave digital traces online and through mobile technologies. The benefits of big data for businesses include improved customer experience personalization, optimized marketing and products, predictive analytics, and risk management. The big data market is expected to double from 2014 to 2018. Future developments include improvements in data processing, centralized data repositories, and analytics solutions in the public cloud to reduce costs and security risks. Big data can deliver business insights, innovation, better customer relationships, and continuously improved experiences for the tourism industry.
The document discusses various topics related to artificial intelligence including health passports, embedded AI, responsible AI, generative AI, AI-augmented development, autonomous vehicles, blockchain, edge computing, augmented reality shopping, and the increasing use of virtual assistants. It also provides several predictions about the growth and impact of AI and related technologies by 2022 and 2030.
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
Explore our analysis of technology trends for 2019 and beyond: AI, IoT, Security, Big Data / Data Science, Mobile Apps Development, AR/VR, RPA (Robot Process Automation), Blockchain, Automotive Solutions, Business Intelligence, Cloud Computing, Service Desk, Autonomous Things, Augmented Analytics, AI-Driven Development, Digital Twins, Empowered Edge, Immersive Experience, Smart Spaces, Quantum Computing, and more.
Check our recommendations for businesses to stay current with the latest IT tendencies.
Includes a video by Gartner.
The document discusses disruptive technologies and how businesses can use them to create new markets and value networks. It provides examples of in-memory computing, crowd sourcing workforces, digital engagement, wearables, social activation, DevOps, and cloud orchestration as disruptive technologies. It also summarizes Clayton Christensen's disruptive technology theory, which separates new technologies into sustaining or disruptive categories. Disruptive technologies lack refinement initially but can eventually displace existing technologies. Examples given include personal computers displacing typewriters and changing communication, and smartphones replacing devices like cameras and GPS units.
Artificial Intelligence & Machine Learning - A CIOs PerspectiveJoAnna Cheshire
Artificial intelligence and machine learning are technologies that 43% of IT decision makers believe need investment. 61% of innovative businesses use AI to uncover new data opportunities. By 2020, insights-driven businesses will gain $1.2 trillion annually from less informed competitors. 81% of executives believe AI will significantly impact their business in the next four years.
The Institution's Innovation Council (Ministry of HRD initiative) and the Institution of Electronics and Telecommunication Engineers (IETE) invited me to grace "World Telecommunication & Information Society Day" on 18 May 2020.
This document summarizes a presentation on innovation in entrepreneurship given by Bohitesh Misra. The presentation covers types of innovation, sources and barriers of innovation, and the importance of frugal innovation. It discusses how digital technology boosts frugal innovation through mobile phones and biometric authentication. Frugal innovation focuses on customer needs and offers more agile, customer-centric products at lower costs through simplification and minimum inputs.
Attaining IoT Value: How To Move from Connecting Things to Capturing InsightsSustainable Brands
Cisco estimates that the Internet of Everything (IoE) — the networked connection of people, process, data, and things — will generate $19 trillion in Value at Stake for the private and public sectors combined between 2013 and 2022. More than 42 percent of this value — $8 trillion — will come from one of IoE’s chief enablers, the Internet of Things (IoT). Defined by Cisco as “the intelligent connectivity of physical devices, driving massive gains in efficiency, business growth, and quality of life,” IoT often represents the quickest path to IoE value for private and public sector organizations.
This paper combines original and secondary research, as well as economic analysis, to provide a roadmap for maximizing value from IoT investments. It also explains why, in the worlds of IoT and IoE, the combination of edge computing/analytics and data center/cloud is essential to driving actionable insights that produce improved business outcomes.
This document provides an overview of blockchain and initial coin offerings (ICOs). It discusses common myths about blockchain, such as the ideas that blockchain is just for Bitcoin, proof of work is the only consensus method, and that all data needs to be stored on a blockchain. The document also covers blockchain use cases, validation of blockchains, and the benefits and challenges of ICOs as a method of fundraising. It aims to demystify blockchain and provide essential information about this emerging technology.
On December 9 & 10, Deloitte hosted over 20 business executives and thought leaders at the Internet of Things (IoT) Grand Challenge Workshop at the Tech Museum of Innovation in San Jose. The objective of the gathering was to work collectively to solve one of the more largely unexplored areas of IoT: revenue generating IoT use cases. The following report captures what was discussed during this extraordinary event where an open, collaborative dialogue focused on advancing the field of IoT.
Explore the key findings here or learn more at www2.deloitte.com/us/IoT-challenge.
The document discusses the new era of cognitive computing. It describes IBM Research's work in developing cognitive systems, including Watson 2.0 which applies complex reasoning, and Watson 3.0 which extends human cognition. It also discusses cognitive computing applications like DOME which differentiates noise from science using deep space data. Finally, it mentions projects like SyNAPSE, a neurosynaptic supercomputer, and the Human Brain Project, which aims to build a detailed brain model.
Building an AI Startup: Realities & TacticsMatt Turck
AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence.
In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics.
Topics include:
Successful data acquisition strategies
Data network effects
Competing with the giants
A pragmatic approach to building an AI team
Why social engineering is just as important to success as groundbreaking AI technology
Blocks & Bots - Digital Summit Harvard Business School 2015Mona M. Vernon
The document discusses building data science capabilities at Thomson Reuters. It introduces the Data Innovation Lab, which works on agile data science projects using lean sprints. The lab focuses on delivering insights to customers through proof-of-concepts and analytical models. It also discusses challenges in data monetization like finding reliable data sources and establishing rights to externalize data. Recent projects demonstrated include linking disparate data using PermIDs, predicting patent litigation risk through machine learning, and visualizing relationships between food insecurity and political instability.
Cognitive analytics: What's coming in 2016?IBM Analytics
Cognitive analytics is innovating and evolving rapidly. Expert predictions in this area are essential for organizations that plan to leverage cognitive analytics in their big data analytics strategies in 2016 and beyond. It is the core investment that organizations everywhere should make to stay relevant in the insight economy. IBM is the premier solution provider, with IBM Watson as its flagship cognitive analytics platform, for realizing the opportunities this innovative technology makes possible.
Learn more about IBM Analytics at http://ibm.co/advancedanalytics
Intelligent Maintenance: Mapping the #IIoT ProcessDan Yarmoluk
A presentation about Industrial IoT, the value chain and real-world use cases; how to create value with IoT at your organization with an emphasis on predictive maintenance (bearing fault detection).
This document discusses various emerging technologies including Internet of Things (IoT), digital transformation, big data, data analytics, machine learning, artificial intelligence, blockchain, Ripple, LiFi, and Mitz technologies. It provides overviews and examples of each technology, noting how IoT is bringing more connected devices and creating challenges around data structures, formats, and analytics. Artificial intelligence can help with IoT data preparation, discovery, visualization, prediction, and geospatial analysis. Blockchain provides benefits for tracking connected devices and enabling secure transactions without centralized control.
Cognitive computing big_data_statistical_analyticsPietro Leo
Cognitive computing, big data, and statistical analytics represent new frontiers for innovation that will transform organizations. These emerging technologies rely on analyzing vast amounts of structured and unstructured data using powerful computers and sophisticated algorithms to generate novel insights. Realizing their full potential will require integrating data and analytics from hundreds or thousands of diverse sources to reduce uncertainty and construct high-value context. This represents a strategic challenge for organizations to create an integrated view of information from all available data channels.
Deloitte's report and point of view on IBM's Watson. IBM Watson, AI, Cognitive Computing are rapidly evolving technologies that can support and enhance enterprise solutions. Learn about IBM Watson the Why? and the How?
This document discusses how big data can enable the travel and tourism industries. It defines big data as large datasets characterized by their volume, velocity, variety, and veracity. Big data comes from a variety of sources as people leave digital traces online and through mobile technologies. The benefits of big data for businesses include improved customer experience personalization, optimized marketing and products, predictive analytics, and risk management. The big data market is expected to double from 2014 to 2018. Future developments include improvements in data processing, centralized data repositories, and analytics solutions in the public cloud to reduce costs and security risks. Big data can deliver business insights, innovation, better customer relationships, and continuously improved experiences for the tourism industry.
The document discusses various topics related to artificial intelligence including health passports, embedded AI, responsible AI, generative AI, AI-augmented development, autonomous vehicles, blockchain, edge computing, augmented reality shopping, and the increasing use of virtual assistants. It also provides several predictions about the growth and impact of AI and related technologies by 2022 and 2030.
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
Explore our analysis of technology trends for 2019 and beyond: AI, IoT, Security, Big Data / Data Science, Mobile Apps Development, AR/VR, RPA (Robot Process Automation), Blockchain, Automotive Solutions, Business Intelligence, Cloud Computing, Service Desk, Autonomous Things, Augmented Analytics, AI-Driven Development, Digital Twins, Empowered Edge, Immersive Experience, Smart Spaces, Quantum Computing, and more.
Check our recommendations for businesses to stay current with the latest IT tendencies.
Includes a video by Gartner.
The document discusses disruptive technologies and how businesses can use them to create new markets and value networks. It provides examples of in-memory computing, crowd sourcing workforces, digital engagement, wearables, social activation, DevOps, and cloud orchestration as disruptive technologies. It also summarizes Clayton Christensen's disruptive technology theory, which separates new technologies into sustaining or disruptive categories. Disruptive technologies lack refinement initially but can eventually displace existing technologies. Examples given include personal computers displacing typewriters and changing communication, and smartphones replacing devices like cameras and GPS units.
Artificial Intelligence & Machine Learning - A CIOs PerspectiveJoAnna Cheshire
Artificial intelligence and machine learning are technologies that 43% of IT decision makers believe need investment. 61% of innovative businesses use AI to uncover new data opportunities. By 2020, insights-driven businesses will gain $1.2 trillion annually from less informed competitors. 81% of executives believe AI will significantly impact their business in the next four years.
The Institution's Innovation Council (Ministry of HRD initiative) and the Institution of Electronics and Telecommunication Engineers (IETE) invited me to grace "World Telecommunication & Information Society Day" on 18 May 2020.
This document summarizes a presentation on innovation in entrepreneurship given by Bohitesh Misra. The presentation covers types of innovation, sources and barriers of innovation, and the importance of frugal innovation. It discusses how digital technology boosts frugal innovation through mobile phones and biometric authentication. Frugal innovation focuses on customer needs and offers more agile, customer-centric products at lower costs through simplification and minimum inputs.
Attaining IoT Value: How To Move from Connecting Things to Capturing InsightsSustainable Brands
Cisco estimates that the Internet of Everything (IoE) — the networked connection of people, process, data, and things — will generate $19 trillion in Value at Stake for the private and public sectors combined between 2013 and 2022. More than 42 percent of this value — $8 trillion — will come from one of IoE’s chief enablers, the Internet of Things (IoT). Defined by Cisco as “the intelligent connectivity of physical devices, driving massive gains in efficiency, business growth, and quality of life,” IoT often represents the quickest path to IoE value for private and public sector organizations.
This paper combines original and secondary research, as well as economic analysis, to provide a roadmap for maximizing value from IoT investments. It also explains why, in the worlds of IoT and IoE, the combination of edge computing/analytics and data center/cloud is essential to driving actionable insights that produce improved business outcomes.
This document provides an overview of blockchain and initial coin offerings (ICOs). It discusses common myths about blockchain, such as the ideas that blockchain is just for Bitcoin, proof of work is the only consensus method, and that all data needs to be stored on a blockchain. The document also covers blockchain use cases, validation of blockchains, and the benefits and challenges of ICOs as a method of fundraising. It aims to demystify blockchain and provide essential information about this emerging technology.
On December 9 & 10, Deloitte hosted over 20 business executives and thought leaders at the Internet of Things (IoT) Grand Challenge Workshop at the Tech Museum of Innovation in San Jose. The objective of the gathering was to work collectively to solve one of the more largely unexplored areas of IoT: revenue generating IoT use cases. The following report captures what was discussed during this extraordinary event where an open, collaborative dialogue focused on advancing the field of IoT.
Explore the key findings here or learn more at www2.deloitte.com/us/IoT-challenge.
The document discusses the new era of cognitive computing. It describes IBM Research's work in developing cognitive systems, including Watson 2.0 which applies complex reasoning, and Watson 3.0 which extends human cognition. It also discusses cognitive computing applications like DOME which differentiates noise from science using deep space data. Finally, it mentions projects like SyNAPSE, a neurosynaptic supercomputer, and the Human Brain Project, which aims to build a detailed brain model.
Building an AI Startup: Realities & TacticsMatt Turck
AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence.
In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics.
Topics include:
Successful data acquisition strategies
Data network effects
Competing with the giants
A pragmatic approach to building an AI team
Why social engineering is just as important to success as groundbreaking AI technology
Blocks & Bots - Digital Summit Harvard Business School 2015Mona M. Vernon
The document discusses building data science capabilities at Thomson Reuters. It introduces the Data Innovation Lab, which works on agile data science projects using lean sprints. The lab focuses on delivering insights to customers through proof-of-concepts and analytical models. It also discusses challenges in data monetization like finding reliable data sources and establishing rights to externalize data. Recent projects demonstrated include linking disparate data using PermIDs, predicting patent litigation risk through machine learning, and visualizing relationships between food insecurity and political instability.
This document provides a summary of a pilgrimage route from Skånes Fagerhult to Lund Cathedral in southern Sweden. Over the course of 150 km, a small group of pilgrims is guided along rural roads and trails, stopping in various towns and churches along the way. The summary highlights the pilgrims' progress, describing some of the places they visit like Örkelljunga, Klippan, and Landskrona, and themes of faith reflected in the churches and landscapes along the route. The journey concludes with the pilgrims arriving at their final destination, Lund Cathedral.
The report summarizes a placement at the Tower Bridge Exhibition and the Monument in London. It provides details on the organizational structure and management of the attractions. It also discusses factors that influence visitor attendance, such as the number of visitors dropping off before reaching the Engine Room exhibition. Research found that visitors using a London Pass, which allows entry to multiple attractions, may spend less time at each site. Weather conditions and technical issues can also impact visitor numbers.
Trump y el efecto boomerang del comercio con MexicoLourdes Gómez
Las políticas proteccionistas del presidente electo de Estados Unidos podrían impulsar aún más la actividad industrial en México, gracias al interés de las compañías multinacionales por realizar mayores compras de insumos en México.
Ac fr ogc7-mj_8ycozkm9utakolnuvpoehmpvpq8scic8rd_r4tapovstrv4txbk5mffoolznngh...Nagendra Babu
The SAP Fiori Client is a native mobile application that provides an enhanced experience for using SAP Fiori applications on Android and iOS devices. It caches application assets to improve performance and provides consistent navigation. The application requires configuration including entering the URL for the SAP Fiori server. Users then log in to access SAP Fiori applications and can configure cache, logging, and other settings to troubleshoot issues.
Sap fiori ll11 – consultants should know about o data troubleshooting sap b...Nagendra Babu
This document provides guidance for SAP consultants on troubleshooting issues with OData services used in SAP Fiori applications. It outlines key steps consultants should take, including identifying the specific OData call, testing the call directly, checking error logs, finding the runtime data provider class, and setting breakpoints to debug the code. The goal is to help consultants understand OData technical behavior and the underlying ERP application functions so they can resolve issues that may be due to different customer backend configurations or data.
Este documento describe el método científico y su aplicación en biología. Explica que la biología es una ciencia experimental que utiliza el método científico para establecer relaciones causales entre fenómenos naturales. Luego define los pasos del método científico, incluida la observación, formulación de hipótesis, experimentación, análisis de resultados y conclusiones. Finalmente, señala que aunque el método experimental no siempre es posible en biología, la observación ordenada también puede generar resultados confiables.
El documento proporciona instrucciones sobre qué hacer en caso de una lesión dental traumática o la pérdida de un diente permanente. Recomienda consultar inmediatamente a un dentista para mejorar la posibilidad de conservar la vitalidad del diente y aplicar un tratamiento conservador con mejor pronóstico y para evitar complicaciones futuras. También indica que si un diente se cae o rompe, se debe encontrar el diente, enjuagarlo brevemente si está sucio, volver a colocarlo en su lugar o guardarlo en leche o sol
The Volta Cereal Production and Processing Group (VCPPG) is a rice value chain association which produces local rice for sale. VCPPG leaders face challenges on how to effectively manage their association. This project focused on improving group dynamics, organizational management, leadership development, financial management, governance, advocacy, and record keeping.
This document provides an introduction to data science and machine learning concepts. It discusses data analytics, machine learning, artificial intelligence, and deep learning. It introduces popular tools for data analytics like Python, Jupyter Notebook, R, and SAS. It also discusses key platforms in data science like Kaggle and DataScientists.net that host data science competitions and allow users to work on real-world datasets. The document provides examples of data analytics applications in different industries like media, healthcare, finance, and manufacturing. It also discusses concepts related to big data like the four V's of big data - volume, velocity, variety and veracity.
Presentation that I delivered at "Accelerate AI, Europe 2018" in London on Sept 19, 2018. My focus is on socio-cultural perspective as well as proving information about various tools, vendors and partners available to help companies get started using AI.
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
Bio IT World 2019 - AI For Healthcare - Simon Taylor, LucidworksLucidworks
1) An AI system implemented at Johns Hopkins Hospital helped optimize hospital operations and bed assignment. It allowed beds to be assigned 30% faster.
2) This reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut wait times for ER patients to receive beds by 20%.
3) The efficiencies also allowed the hospital to accept 60% more transfer patients from other hospitals.
This document provides an overview of how artificial intelligence and deep learning are revolutionizing various industries. It discusses key concepts like artificial intelligence, machine learning, and deep learning. It then highlights several use cases across healthcare, automotive, retail, and financial services. For example, it describes how deep learning has helped reduce error rates in breast cancer diagnosis by 85% and how AI is enabling more efficient warehouse operations and personalized shopping. The document concludes by offering advice on getting started with deep learning projects.
Beyond AI The Rise of Cognitive Computing as Future of Computing ChatGPT Anal...ijtsrd
Cognitive computing, a revolutionary paradigm in computing, seeks to replicate and enhance human like intelligence by amalgamating artificial intelligence, machine learning, and natural language processing. This paper provides an overview of cognitive computing, emphasizing its core principles and applications across diverse industries. Key components, including adaptability, learning, and problem solving capabilities, distinguish cognitive computing from traditional computing models. The integration of natural language processing enables more intuitive human machine interactions, contributing to applications such as virtual assistants and personalized services. The paper explores the ethical considerations inherent in cognitive computing, highlighting the importance of transparency and responsible use. With continuous evolution and ongoing research, cognitive computing is on the verge to shape the future of computing, offering new opportunities and challenges in various domains. This abstract encapsulates the transformative nature of cognitive computing and its potential impact on the technological landscape. Manish Verma "Beyond AI: The Rise of Cognitive Computing as Future of Computing: ChatGPT Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61292.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61292/beyond-ai-the-rise-of-cognitive-computing-as-future-of-computing-chatgpt-analysis/manish-verma
Artificial Intelligence Best Practices: How AI Models Can Transform Legal and...Anna Kragie
The legal and corporate worlds are buying into the power of AI and machine learning. Now, many industries are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices. A decade ago, the legal and corporate worlds needed more convincing about the power of AI and machine learning. Now, many industries — legal included — are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices.
Borys Pratsiuk is the Head of R&D at an unnamed company. He has over 15 years of experience in engineering roles related to Android development, embedded systems, and solid state electronics. He holds a PhD in Solid State Electronics from Kiev Polytechnic Institute and has worked in both academic and industry roles in South Korea and Ukraine. The presentation discusses big data, analytics, artificial intelligence and machine learning applications across various industries. It provides examples of deep learning solutions developed for clients in areas like computer vision, natural language processing, predictive analytics and process automation. The presentation emphasizes Ciklum's full-service approach to developing and deploying deep learning solutions from data collection and modeling to deployment and ongoing support.
Fintech workshop Part I - Law Society of Hong Kong - XccelerateHenrique Centieiro
What is fintech? What are the technologies leveraging Fintech? How AI, Blockchain, Cloud and Data Analytics are changing the financial world?
Henrique works as Innovation Project Manager implementing Fintech and Blockchain Projects for the Financial Industry
Find me here: linkedin.com/in/henriquecentieiro
Applying deep learning tools to data available at the banking industry level....Data Driven Innovation
Deep learning tools can be applied to data in the banking industry to drive innovation. Some potential use cases include interventions to improve business models and customer services, as well as optimizing business processes. However, these opportunities must be balanced with risks. Artificial intelligence approaches are available to companies of all sizes. Data quality is important both as a starting point and ongoing target for AI systems. Combining multiple techniques such as computer vision and deep learning can provide benefits. Model training may eventually replace some data collection needs.
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
Once you’ve made the decision to leverage AI and/or machine learning, now you need to figure out how you will source the training data that is necessary for a fully functioning algorithm. Depending on your use case, you might need a significant amount of training data, and you’ll want to consider how that is labeled and annotated too.
View Applause's webinar with Cognilytica principal analysts Ronald Schmelzer and Kathleen Walch, alongside Kristin Simonini, Applause’s Vice President of Product, as they tackle the modern challenges that today’s companies face with sourcing training data.
Artificial Intelligence Empowering the Future of Digital Transformationijtsrd
Artificial Intelligence is not only about the machines that play an authoritative role in humans, but they both are working together. Machines provide the human with the ability of insight and perspective but the machines will not provide the decisive role of supplying judgement and creativity. There is a huge scope of artificial intelligence in this era. The combination of human creativity and technology together results in the excitement that can solve various problems and challenges related to the world. Deepak Kumar "Artificial Intelligence Empowering the Future of Digital Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30287.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30287/artificial-intelligence-empowering-the-future-of-digital-transformation/deepak-kumar
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
A large transportation company needed help optimizing their transportation model to reduce costs. Teradata developed a transportation optimization model and user interface tool that takes forecasted volumes and determines the most optimal transportation modes and routes to deliver products to customers while considering capacities, constraints, and business rules. The tool selects the lowest cost solutions for each material/customer pair and allows users to conduct "what-if" analysis of scenarios to further reduce total costs.
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
Monday was another great conference by MinneAnalytics! #MinneFRAMA was a great success with over 1,100 attendees at Science Museum of Minnesota. Alison Rempel Brown is a great host! A Teradata colleague told me that her post about my presentation "blew up" with hits and she got over 2K views, and 60+ likes. I'm proud to be a part of this great #datascience organization brining #machinelearning and #artificialintelligence #analytics to our #bigdata clients. If you want my slides, here they are.
Artificial intelligence and semantic computing can assist the financial services industry in several ways:
- Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations.
- Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights.
- Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily.
- Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
Similar to Cognitive technologies with David Schatsky at Blocks + Bots (20)
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
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https://www.wask.co/ebooks/digital-marketing-trends-in-2024
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Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
2. Agenda
What is AI?
What are cognitive technologies?
Focus on machine learning
The catalysts of progress
Types of applications
Investment and trends
Where and whether to apply these technologies
Cog tech, automation, and work
Conclusion
3. What is artificial intelligence?
DEFINING AI
“… may lack an agreed-upon definition”
− AI pioneer Nils Nilsson1
Leading AI textbook offers 8 definitions2
1 Nils Nilsson, The Quest for Artificial Intelligence
2 Stuart Russell and Peter Norvig, Artificial Intelligence
4. A useful definition of AI
DEFINING AI
The theory and
development of computer
systems able to perform
tasks that normally require
human intelligence.
5. The AI Effect
DEFINING AI
“As soon as it works, no
one calls it AI anymore.”1
“AI is whatever hasn’t been
done yet.”2
1 John McCarthy, quoted in Bostrom
2 Attributed to Larry Tesler in Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid
8. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Supervised learning
Supervised learning is like learning by example
“Learning a model from labeled training data”
Used for
• Classification - output is
one of set of discrete
values (e.g. spam, not
spam)
• Regression - output is a
number (e.g., a price) -
prediction
Source: http://faculty.chicagobooth.edu/drew.creal/teaching/basiccoursematerial/lectures/lecture9.pdf
9. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Unsupervised learning
Learning by discovering patterns – “There are two types of people ….”
Applications - customer
segmentation, product
basket discovery, topic
analysis
10. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Reinforcement learning
Learning by trial and error – how a baby learns to crawl
Applications:
- Mechanical control -
elevators, robots
- Game playing
12. Moore’s law benefited all types of computing
CATALYSTS OF PROGRESS
Current generation
of microprocessors
are 4,000,000X more
powerful than first
single-chip
microprocessor of
19711
1 Andrew Danowitz et al., “CPU DB: Recording microprocessor history,” ACMQueue, volume 10, issue 4 (April 6, 2014),
http://queue.acm.org/detail.cfm?id=2181798, accessed October 11, 2014.
13. Big data and new techniques advance work in AI
CATALYSTS OF PROGRESS
• Volume of data doubles every 2 years1
• 44 trillion gigabytes annually by 20202
• New techniques for managing and
analyzing data
• AI models improved with “training”
1, 2 IDC 2014, http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm
14. Internet and cloud support AI with access
to big data and collaborators
CATALYSTS OF PROGRESS
Access to vast data resources
Crowd-sourcing to train
machine learning models
Implicit collaboration, e.g.,
Web search, translation
15. Advances in algorithms broke
performance barriers
CATALYSTS OF PROGRESS
New algorithms
dramatically
improve
performance of
machine learning
Over 500,000
scholarly papers on
neural networks
since 20061
New distributed
computing
breakthroughs
1 Google Scholar
16. Performance is improving…continually
Facial recognition:
2014: 97% accuracy (Facebook)1;
2015: 100% accuracy (Google)2
Google speech recognition:
2013: 23% error rate
2015: 8% error rate3
IBM Watson
2400% smarter than when it won Jeopardy!4
1 Facebook, “DeepFace: Closing the gap to human-level performance in face verification,” https://www.facebook.com/publications/546316888800776/, accessed
October 3, 2014; 2 http://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf; 3 Jordan Novet, Venture Beat, “Google says its speech recognition
technology now has only an 8% word error rate,” May 28, 2015, http://venturebeat.com/2015/05/28/google-says-its-speech-recognition-technology-now-has-
only-an-8-word-error-rate/, accessed September 16, 2015; 4 IBM, “IBM Watson,” http://www-03.ibm.com/press/us/en/presskit/27297.wss, accessed October 3,
2014.
17. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Three main applications of cognitive
technologies: Product, process and insight
18. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Product: Embedding cognitive technologies in a
product or service
Embed cognitive technologies to
help increase the value of products
or services by making them more
effective, convenient, safer, faster,
distinctive, or otherwise more
valuable.
eBayNetflix
GM Domino’s
Pizza
AudiVuCOMP
19. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Process: automate tasks or processes humans used
to do
Using computer systems to do work
that people used to do. The work gets
done faster, cheaper, better, or some
combination of the three. Organization
benefits.
Automate scheduling
engineering works
Clinical trials eligibility
Process
handwritten
forms
20. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Insight: Discerning patterns, making predictions,
to improve operations or guide strategy
Generate insights that can
help reduce costs, improve
efficiency, increase
revenues, improve
effectiveness, or enhance
customer service
• Intel
• BBVA Compass
• Aetna
21. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
New applications of machine learning daily
Public sector
• Smartphone app to reduce urban congestion
• Flag parking abuses
• Detect misbehavior by prisoners
Automotive
• Detected driver absent-mindedness
Enterprise information management
• Classify and route business documents
• Setting rules for accessing and manipulating
documents
Health
• Detect signs of gambling addiction
• Predict cancer remission or drug resistance
• Drug discovery
• Predict development of psychosis
• Predict air pollution days in advance
Sales
• Predicting which deals will close
Announced in the last few
months
22. Billions in investment capital aimed mostly at
traditional business problems and industries
$281.3
$855.1
$1,037.4
$2,000.5
$2,464.0
$0 $1,000 $2,000 $3,000
Supporting Technologies
Rethinking Humans / HCI
Core Technologies
Rethinking Industries
Rethinking Enterprise
Millions
VC investment in cognitive technology companies that have raised at least $10M, (Jan. 2011 – Sep. 2015, US only)
23. Commercialization and improving performance
expand applications
Improving performance
and commercialization
fueled by surging
investment expand the
applications for
cognitive technologies
GRAPHIC: DELOITTE UNIVERSITY PRESS
24. Applications are broadening
As performance improves,
applications of speech
recognition, computer
vision, natural language
processing and
understanding are growing
conclusion
25. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Deciding where to apply cognitive technologies
in an organization
26. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Viable: Where is it possible to apply cognitive technologies
Types of tasks Examples
Perceptual tasks involving
vision, speech, reading
handwriting
Forms processing, first-tier
customer service,
warehouse operation
Analytical tasks, involving
large data sets
Document review; finding
patterns, making
predictions
Decision-making tasks
where expertise can be
expressed as rules
Planning maintenance
operations
Planning tasks in a
constrained domain
Scheduling
27. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Valuable: Where it is worth applying cognitive technologies
It may be worth using cognitive technologies
where
• Workers’ cognitive abilities or training are
underutilized
• Business process has high labor costs
• The value of improved performance is high
Opportunities Examples
Worker’s cognitive abilities
or training are under
utilized
Writing company earnings
reports; e-discovery
Business process has high
labor costs
Medical utilization
management
Expertise is scarce Medical diagnosis—
especially rare conditions
Value of improved
performance high
Decision-making in
financial services
Create new features
customers care about
Natural interfaces,
automation, “intelligence”
28. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Consumer benefits of cognitive technologies
Anupam Narula, David Schatsky, Ben Stiller, & Robert Libbey, "The thinker and the shopper: Four ways cognitive technologies can add value to
consumer products," Deloitte University Press (June 3, 2015)
29. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Vital: Where it is necessary to apply cognitive technologies
It may be necessary to use cognitive
technologies where
• Industry-standard performance requires it
(e.g. Online retail product recommendations)
• Cannot scale relying on human labor alone
(e.g. media sentiment analysis, fraud
detection)
Types of tasks Examples
Industry-standard
performance requires
cognitive tech
Online retail product
recommendations
Service cannot scale
relying on human labor
alone
Fraud detection; social
media sentiment analysis
30. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
The unintended consequences of automation
People are flawed; automated systems can have
flaws too
Humans are bad at monitoring automated
processes—paying attention to things that hardly
change
People lose skills if they don’t practice them—
the autopilot irony
Cognitive “underload” can reduce performance
Automated systems can undermine worker
motivation, cause alienation, and reduce
satisfaction, productivity, and innovation
Ill-conceived automation strategies may diminish
our sense of self-worth
31. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
What to automate, and to what extent?
High 10. The computer decides everything, acts autonomously, ignoring the human,
9. informs the human only if it, the computer, decides to
8. informs the human only if asked, or
7. executes automatically, then necessarily informs the human, and
6. allows the human a restricted time to veto before automatic execution, or
5. executes that suggestion if the human approves, or
4. suggests one alternative
3. narrows the selection down to a few, or
2. the computer offers a complete set of decision/action alternatives, or
Low 1. the computer offers no assistance: humans take all decisions and actions
Information
acquisition
Information
analysis
Decision and
action selection
Action
implementation
Adapted from: Raja Parasuraman et al., “A Model for Types and Levels of Human Interaction
with Automation,” IEEE Transactions on Systems, Man, and Cybernetics 30, no. 3 (2000): 286–297.
32. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Organizations have automation choices
Automation approach What is automated Examples
Replace Everything ATM; first-tier customer
support
Atomize/automate As much as possible Machine translation plus
human cleanup
Relieve Dull, dirty, or dangerous
jobs
Routine earnings stories at AP;
caller authentication at
Barclays
Empower What wasn’t even being
done before
IBM Watson for Oncology; oil &
gas drilling problem resolution
33. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Maximizing the value of workers and machines
A COST STRATEGY USES TECHNOLOGY TO REDUCE COSTS, ESPECIALLY BY REDUCING LABOR
A VALUE STRATEGY AIMS TO INCREASE VALUE BY COMPLEMENTING LABOR WITH TECHNOLOGY OR REASSIGNING LABOR TO HIGHER-VALUE WORK
Besides automation choices, organizations must choose between
a cost strategy and a value strategy
A cost strategy uses technology to reduce costs, especially by
reducing labor
A value strategy aims to increase value by complementing labor
with technology or reassigning labor to higher-value work
34. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Automation choices under different strategies
NEITHER THE TASK NOR THE TECHNOLOGY DICTATE THE STRATEGY TO BE FOLLOWED
Automation
Choice
Cost strategy Value strategy
Replace Eliminate worker Reassign worker; use tech to provide
superior performance
Atomize/
automate
Accelerate work, reduce staff,
possibly alienate creative
workers and artisans
Create new low-cost offers, employ
lower-skilled, less-experienced
workers
Relieve Eliminate routine tasks, increase
productivity, reduce staff
Redeploy people to higher-value
tasks; create more value for
customers
Empower Increase performance of workers Increase workers’ performance and
use to enhance their skills
35. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Some skills will become more valuable
TAKING A FRESH LOOK AT WHAT SKILLS WILL BE NEEDED
Tasks that cannot be substituted by computerization are
generally complemented by it.
Technology increases productivity, raises earnings, and
augments demand for skilled labor
The skills required for routine work to become less valuable
The skills required to perform broadly-defined jobs and
those required for successful interpersonal interactions to
become relatively more valuable:
Flexibility General problem solving
Creativity Tolerance of ambiguity
Empathy Drive
Emotional intelligence Resourcefulness
Critical thinking Openness to serendipity
36. Demystifying artificial intelligence: The real opportunities for cognitive technologies in business
Organizations must plan for cognitive technologies
THERE ARE CHOICES TO BE MADE
Cognitive technologies will change the employment landscape in the coming
years
Some jobs will disappear; others will be redesigned; new kinds of work will
arise
Workers whose skills are complemented by cognitive technologies will thrive;
those whose skills are being supplanted by smart machines may struggle
Leaders face choices about how to apply cognitive technologies:
• Will their workers be marginalized or empowered?
• Will their organizations use the technology to create value or cutting costs?
Talent strategies must start to account for impact of cognitive technologies
37. Some take aways
Understand how these technologies enable new, better ways of working.
Prepare to adopt when appropriate, or risk being sidelined.
Begin today to explore cognitive technologies
But killer robots are not around the corner
Something new and important is happening
Their impact on business is increasing
The technologies are getting better
An opportunity to differentiate
The use of cognitive technologies can confer competitive advantage
today. It will become table stakes tomorrow.
38. New online course on artificial intelligence and cognitive
technologies
Free course. Register today:
http://novoed.com/cognitive-technology
39. Now available on:
Signals for Strategists:
Sensing Emerging Trends in Business
and Technology
This book is for strategists—leaders, managers,
entrepreneurs—who are so caught up in the daily
pressures of the business that they’re missing key
signals of their future reality. Signals for Strategists
identifies the emerging trends on the horizon. The
sooner we see them, the better our response.
Includes perceptual such as recognizing speech, handwriting
Understanding language
Vision, recognizing faces
Planning, reasoning under uncertainty, learning
Moving around an unstructured environment autonomously (animals can do this too)
Not machines that think
Not computers that work the way a brain works
Definition is: what can they do
Not: how a machine gets it done
“As soon as it works, no one calls it AI anymore” – John McCarthy
Quoted in Bostrom, Loc 477
“AI is whatever hasn’t been done yet”: Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid, (Harmondsworth, Middlesex: Penguin, 1980), p. 597, accessed October 9, 2014 at http://www.physixfan.com/wp-content/files/GEBen.pdf. Hofstadter called this Tesler’s Theorm. Tesler says Hofstadter misquoted him and that what he really said was “Intelligence is whatever machines haven't done yet.”
Definition will change. Rules-based systems maybe not called AI anymore. But they used to be.
The key point: AI enables computers to do things they couldn’t do before—begin to encroach on the domain that was reserved solely for humans.
To start, it’s useful to define terms….
Artificial intelligence lacks a precise definition. Most experts in the field agree that it’s not about machines that think. It’s about what machines can do.
it’s not concerned specifically with machines as smart as people; It’s concerned with making machines that can do tasks that used to require human intelligence.
The technologies that enable machines to do these tasks I call cognitive technologies. Useful to distinguish the technologies companies are applying from the field that gave rise to them.
[next slide]
To understand the applications of AI it is useful to understand a bit about the specific cognitive technologies that have emerged from the field.
This graphic depicts many of the cognitive technologies in growing use today, which are bringing powerful new capabilities to enterprises and products. They include:
Computer vision: The ability of computers to identify objects, scenes, and activities in unconstrained (i.e., naturalistic) visual environments
Machine learning: The ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions
Natural language processing (NLP): The ability of computers to work with text the way humans do—for instance, extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct
Speech recognition: The ability to automatically and accurately transcribe human speech
Optimization: The ability to automate complex decisions and trade-offs about limited resources
Planning and scheduling: The ability to automatically devise a sequence of actions to meet goals and observe constraints
Rules-based systems: The ability to use databases of knowledge and rules to automate the process of making inferences about information
When you hear about artificial intelligence or cognitive computing, see if you can identify which specific cognitive technology is being described.
Advanced system designs that might have worked in principle were in practice off limits just a few years ago because they required computer power that was cost prohibitive or just didn’t exist
Today, the power necessary to implement these designs is readily available.
Current generation of microprocessors delivers 4 million times the performance of the first single-chip microprocessor introduced in 1971
Volume of data in the world is increasing rapidly, thanks in part to Internet, social media, mobile devices, and low-cost sensors
Development of new techniques for managing and analyzing very large data sets.
Big data has been a boon to the development of AI: some AI techniques use statistical models for reasoning probabilistically about data such as images, text, or speech.
These models can be improved, or “trained,” by exposing them to large sets of data, which are now more readily available than ever.
Volume of data doubles every two years. By 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.
http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm (IDC 2014)
Supported progress in AI for two reasons: access to data and fostering collaboration
make available vast amounts of data and information to any Internet-connected computing device, propelling work on AI approaches that require large data sets.
provided a way for humans to collaborate—sometimes explicitly and at other times implicitly—in helping to train AI systems.
E.g. some researchers have used cloud-based crowdsourcing services like Mechanical Turk to enlist thousands of humans to describe digital images, enabling image classification algorithms to learn from these descriptions.
Google’s language translation project analyzes feedback and freely offered contributions from its users to improve the quality of automated translation.
For discussion of “cognitive analytics,” including the role of cloud computing, see Rajeev Ronanki and David Steier, “Cognitive analytics,” Deloitte University Press, February 21, 2014, http://dupress.com/articles/2014-tech-trends-cognitive-analytics/, accessed October 9, 2014.
Catherine Wah, “Crowdsourcing and its applications in computer vision,” U.C. San Diego, May 26, 2011, http://vision.ucsd.edu/~cwah/files/re_cwah.pdf, accessed October 8, 2014.
Google Inc., “Google Translate Community FAQ,” https://docs.google.com/document/d/1dwS4CZzgZwmvoB9pAx4A6Yytmv7itk_XE968RMiqpMY/pub, accessed October 8, 2014.
An algorithm is a routine process for solving a program or performing a task
In recent years, new algorithms have been developed that dramatically improve the performance of machine learning, an important technology in its own right and an enabler of other technologies such as computer vision. (These technologies are described below.)
The fact that machine learning algorithms are now available on an open-source basis is likely to foster further improvements as developers contribute enhancements to each other’s work.
Multiple researchers have devised algorithms that have improved the performance of machine learning. Google Scholar finds some 500,000 scholarly papers on the topic of neural networks, for example, published since 2006. Geoffrey Hinton is a widely published and cited researcher in this area credited with several important innovations. See: Geoffrey Hinton, “Home Page of Geoffrey Hinton,” http://www.cs.toronto.edu/~hinton/, accessed October 6, 2014. Other researchers who are widely recognized for contributions in this area include Yann LeCun (See Yann LeCunn, “Yann LeCun’s Home Page,” http://yann.lecun.com/, accessed October 9, 2014), and Yoshua Bengio (See Joshua Bengio, “Yoshua Bengio’s Research,” http://www.iro.umontreal.ca/~bengioy/yoshua_en/research.html, accessed October 9, 2014. Recently, Microsoft demonstrated a new machine learning architecture that dramatically accelerates the machine learning process, improving precision and accuracy. See, Microsoft Research, “On Welsh Corgis, computer vision, and the power of deep learning,” http://research.microsoft.com/en-us/news/features/dnnvision-071414.aspx?0hp=002c, accessed October 6, 2014.
The Apache Software Foundation sponsors Apache Mahout, an open source machine learning library. Startup PredictionIO is offering an open-source machine learning server and recently received $2.5 million in venture funding. See Steve O’Hear, “PredictionIO raises $2.5M for its open source machine learning server,” TechCrunch, July 17, 2014, accessed October 6, 2014.
Google’s Facenet results – trained on massive 260-million-image dataset
Driven by clever engineering, access to data sets for training, algorithm improvements (to a lesser degree)
Voice recognition: Accuracy of Google’s voice recognition technology improved from 84 percent in 2012 to 98 percent less than two years later, according to an assessment by investment bank Piper Jaffray
Computer vision: Facebook reported in a peer-reviewed paper that its DeepFace technology can now recognize faces with 97 percent accuracy – about as well as people can
msft dog breed;
A standard benchmark used by computer vision researchers has shown a four-fold improvement in image classification accuracy from 2010 to 2014
IBM Watson precision: IBM doubled the precision of Watson’s answers in the few years leading up to its famous Jeopardy! victory in 2011.
The company now reports its technology is 2,400 percent “smarter” today than on the day of that triumph.
Not everything is improving so fast. One benchmark found a 13 percent improvement in the accuracy of Arabic to English translations between 2009 and 2012, for instance.
NIST Information Technology Laboratory, “OpenMT12 Evaluation Results,” August 28, 2012, http://www.nist.gov/itl/iad/mig/openmt12results.cfm, accessed October 8, 2014. BBN’s system, BBN_ara2eng_primary_cn, performed better than all competitors in both years but improved just 13 percent.
Apple Insider, “Tests find Apple's Siri improving, but Google Now voice search slightly better,” http://appleinsider.com/articles/14/07/22/tests-find-apples-siri-improving-but-google-now-voice-search-slightly-better, accessed October 3, 2014.
Facebook, “DeepFace: Closing the gap to human-level performance in face verification,” https://www.facebook.com/publications/546316888800776/, accessed October 3, 2014.
Olga Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575v1 [cs.CV] (September 1, 2014), http://arxiv.org/pdf/1409.0575v1.pdf, accessed October 3, 2014.
We looked at over 100 examples of companies that had deployed applications of cognitive technologies and found that most applications fall into one of three main categories, which I call product, process, and insight:
Product:
Embedding cognitive technologies in a product or service in a way that touches and delivers a benefit to the end customer
Process:
Automate or enhance tasks or business processes internal to an organization
Insight
Analyzing data, often large amounts of data, unstructured data such as text, images, etc. to discern patterns or make predictions
Let’s look at some examples of each type of application. [next slide]
We reviewed over 100 examples of organizations that implemented or piloted cognitive technologies
examples spanned 17 industry sectors, including aerospace and defense, agriculture, automotive, banking, consumer products, health care, life sciences, media and entertainment , oil and gas, power and utilities, the public sector, real estate, retail, technology, and travel, hospitality and leisure.
Application areas were broad and included research and development, manufacturing, logistics, sales, marketing, and customer service.
Netflix
uses machine learning to predict which movies a customer will like
Now accounts for as much as 75 percent of Netflix usage
eBay
uses machine translation to enable users who search in Russian to discover English-language listings that match
GM
Will make some of its vehicles safer by equipping them with computer vision to determine whether the driver is distracted or not spending enough time looking in certain areas such as the road ahead or the rear-view mirror [possible image from seeingmachines.com here: http://www.cbsnews.com/news/gm-takes-aim-at-distracted-driving-with-head-eye-trackers/]
Audi
integrating speech recognition technology into some cars to enable drivers to engage in a more convenient, natural communication with infotainment and navigation systems
VuCOMP
A maker of medical imaging technology
make radiologists more effective by using computer vision algorithms to identify and outline areas of mammograms consistent with breast cancer
Clinical study: radiologists were significantly more effective in finding cancer and in differentiating cancer from non-cancer when using the system
[** Image: VuCOMP?]
Dominos
function on its mobile app that lets customers place orders by speaking with a computer-generated voice named "Dom."
Not for cost cutting. Instead, to increase convenience and sales
Customers say they prefer to order online or mobile, and spend more when they do
Associated Press
To scale and improve the quality of its business news coverage
implemented natural language generation software that automatically writes corporate earnings stories
Rather than reduce staffing levels, AP using the technology to increase by 10X the number of such stories it publishes
AP to cover companies of local or regional importance it did not have the resources to cover before
Freeing journalists from writing formulaic earnings stories so they can focus on more analytical and exclusive stories
New categories like Roomba, Google Now
Sallie Davies, “GM to launch cars that can pick up on distracted driving ,” Financial Times, September 1, 2014, http://www.ft.com/intl/cms/s/0/e5787fea-30e9-11e4-8313-00144feabdc0.html#axzz3CMkdphUC, accessed October 14, 2014.
Nabanita Singha Roy, “Audi TT’s new voice and natural language understanding (NLU) technology,” Rushlane, October 2, 2014, http://www.rushlane.com/audi-tt-nlu-tech-paris-2014-12132560.html, accessed October 14, 2014.
VuCOMP, “The M-Vu System,” http://www.vucomp.com/products/m-vu-system, accessed October 14, 2014.
Candice Choi, “Domino's introduces a 'Siri' to take mobile orders,” Associated Press, June 16, 2014, http://bigstory.ap.org/article/dominos-introduces-siri-take-mobile-orders, accessed October 14, 2014.
Hong Kong subway - It carries over 5 million passengers daily and boasts a 99.9 percent on-time record
To improve quality and efficiency
Automate and optimize the planning of 2600 weekly these engineering works performed by 10,000 people
a “genetic algorithm” that pits many solutions to the same problem against each other to find the best one, producing an optimal engineering schedule automatically and saving two days of planning work per week
State of Georgia Government Transparency and Campaign Finance Commission
Has to process 40,000 pages of campaign finance disclosures per month, many of which are handwritten
Solution uses automated handwriting recognition to keep up with the workload coupled with crowdsourced human review to ensure quality
Cincinnati children’s hospital
automatically identifying patients eligible for clinical trials
using natural language processing to read free-form clinical notes, and machine learning to refine the list of terms extracted from them
reduced the workload by 92 and increased efficiency by 450 percent
Hal Hodson, “The AI boss that deploys Hong Kong's subway engineers,” NewScientist, July 4, 2014, http://www.newscientist.com/article/mg22329764.000-the-ai-boss-that-deploys-hong-kongs-subway-engineers.html#.U9efI_ldWSo, accessed October 14, 2014.
Richard W. Walker, “Georgia Solves Campaign Finance Data Challenge Via OCR,” InformationWeek, April 15, 2014, http://www.informationweek.com/government/cloud-computing/georgia-solves-campaign-finance-data-challenge-via-ocr/d/d-id/1204471, accessed October 14, 2014.
Yizhao Ni et al., “Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department,” JAMIA, July 16, 2014, doi:10.1136/amiajnl-2014-002887, http://jamia.bmj.com/content/early/2014/07/16/amiajnl-2014-002887.full, accessed October 14, 2014.
Insight applications represent a great opportunity for a many companies.
On example is Intel, which is using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. This enables them to automatically prioritize sales efforts and tailor promotions. The company expects this strategy to result in tens of millions of dollars of additional revenue when rolled out globally.
Another example is Aetna, which has used machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. They did an analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes
Using claims and biometric data for a population of 37,000 Aetna members, they developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with it. They were also able to determine which medical interventions are most likely to improve an individual’s health outlook.
Wherever there is large amounts of data, it may be possible to apply machine learning to discover useful patterns and make valuable predictions.
[next slide]
Stevia First
Optimizing its industrial processes. Rather than explore various production approaches by trial and error, the company uses what it calls “smart search” cognitive algorithms to determine the optimal parameters for the volume of raw materials and process time, for instance, to boost the cost efficiency of their production process.
Company is evaluating other applications from using NLP to automatically read and summarize findings from thousands of academic papers, to using machine learning to reanalyze data sets from old biotechnology research in search of a new gene or a new drug.
[**Image See http://www.steviafirst.com/ for image ideas]
Intel
Using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns.
Prioritize sales efforts and tailor promotions
company expects this strategy to result in $20 million in additional revenue when rolled out globally
BBVA Compass
To improve marketing and customer service
uses a social media sentiment monitoring tool with NLP to track and understand what consumers are saying about itself and its competitors.
automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics.
Insights influence the bank’s decisions on setting fees and offering consumer perks, and how customer service representatives should response to certain customer inquiries about services and fees.
Aetna
With GNS Health, use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them
analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes
Using claims and biometric data for a population of 37,000 Aetna members, the companies developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with the disorder. It also is able to determine which medical interventions are most likely to improve an individual’s health outlook Robert Brooke, “Stevia First – A New Era is Beginning,” http://www.thechairmansblog.com/stevia-first/robert-brooke/stevia-first-new-era-beginning/#, accessed October 14, 2014.
Derrick Harris, GigaOm, November 18, 2013, https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom-line/, accessed October 14, 2014.
Insight applications represent a great opportunity for a many companies.
On example is Intel, which is using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. This enables them to automatically prioritize sales efforts and tailor promotions. The company expects this strategy to result in tens of millions of dollars of additional revenue when rolled out globally.
Another example is Aetna, which has used machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. They did an analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes
Using claims and biometric data for a population of 37,000 Aetna members, they developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with it. They were also able to determine which medical interventions are most likely to improve an individual’s health outlook.
Wherever there is large amounts of data, it may be possible to apply machine learning to discover useful patterns and make valuable predictions.
[next slide]
Stevia First
Optimizing its industrial processes. Rather than explore various production approaches by trial and error, the company uses what it calls “smart search” cognitive algorithms to determine the optimal parameters for the volume of raw materials and process time, for instance, to boost the cost efficiency of their production process.
Company is evaluating other applications from using NLP to automatically read and summarize findings from thousands of academic papers, to using machine learning to reanalyze data sets from old biotechnology research in search of a new gene or a new drug.
[**Image See http://www.steviafirst.com/ for image ideas]
Intel
Using machine learning to improve sales effectiveness and boost revenue. One approach: automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns.
Prioritize sales efforts and tailor promotions
company expects this strategy to result in $20 million in additional revenue when rolled out globally
BBVA Compass
To improve marketing and customer service
uses a social media sentiment monitoring tool with NLP to track and understand what consumers are saying about itself and its competitors.
automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics.
Insights influence the bank’s decisions on setting fees and offering consumer perks, and how customer service representatives should response to certain customer inquiries about services and fees.
Aetna
With GNS Health, use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them
analysis focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke and diabetes
Using claims and biometric data for a population of 37,000 Aetna members, the companies developed models that predicted the risk of developing metabolic syndrome and the probability of developing any of the five conditions associated with the disorder. It also is able to determine which medical interventions are most likely to improve an individual’s health outlook Robert Brooke, “Stevia First – A New Era is Beginning,” http://www.thechairmansblog.com/stevia-first/robert-brooke/stevia-first-new-era-beginning/#, accessed October 14, 2014.
Derrick Harris, GigaOm, November 18, 2013, https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom-line/, accessed October 14, 2014.
Top enterprise categories: marketing, intelligence (including analytics solutions), security and authentication, and sales.
Top industry sectors: Retail, adtech, medical and diagnostics, and education.
Top industries:
The biggest funding category by far is those companies building applications for traditional enterprise functions such as marketing and sales. Startups like these have raised nearly $2.5 billion since 2011 (see figure 1), suggesting that the biggest near-term opportunity for cognitive technologies is in using them to enhance current business practices.
Indeed, startups are using cognitive technologies to develop valuable features and capabilities such as intelligent automation, ease of use, and insightful analytics that are superior to what can readily be achieved with conventional information technologies. In the Rethinking Enterprise category, for instance, marketing-focused startups have used machine learning to improve customer targeting and website personalization, natural language processing to understand what consumers are saying about television content on social media, and speech recognition to gauge the quality of inbound telephone leads. The top segments in the enterprise category are marketing, intelligence (including analytics solutions), security and authentication, and sales.
Companies developing applications tailored for specific sectors such as retail, advertising (“adtech”), education, medical/diagnostics, and media have also received major investments—over $2 billion during the same period. In the Rethinking Industry category, startups providing solutions aimed at the medical and diagnostics sectors are using natural language processing to automate the coding of medical charts for insurance reimbursement, machine learning to power mobile care-management apps that tailor their content to better engage patients in their care regimen, and computer vision and machine learning to power a simple, low-cost ultrasound device that can automatically diagnose disorders. Top segments in this category include retail, adtech, medical and diagnostics, and education.
This analysis suggests that the applications of cognitive technologies are broad; they can often resemble traditional enterprise applications—with advanced capabilities and performance—rather than specialized cognitive computing products. A principal way that cognitive technologies can create value for companies is by intelligently automating tasks and surfacing insights that augment human decision making. The opportunities for this are huge, spanning all sectors and business functions. Derrick Harris, “Exclusive: Causata raises $7.5M and steps up its game in targeted ads,” GigaOm, February 6, 2013, https://gigaom.com/2013/02/06/exclusive-causata-raises-7-5m-and-steps-up-its-game-in-targeted-ads/, accessed September 19, 2015.
Jolie Katz, “Better recommendations are worth $500M,” Rich Relevance, March 31, 2015, www.richrelevance.com/blog/2015/03/better-recommendations-worth-500m/, accessed September 19, 2015.
Mike Isaac, “Why Twitter dropped close to $90 million on Bluefin Labs,” All Things D, February 12, 2013, http://allthingsd.com/20130212/why-twitter-dropped-close-to-90-million-on-bluefin-labs/, accessed September 19, 2015.
Convirza, “Convirza closes $20M of Series B funding for call analytics and automation,” www.convirza.com/press-releases/convirza-closes-20m-of-series-b-funding-for-call-analytics-and-automation/, accessed September 19, 2015.
Marketing vendors have attracted $590 million; analytics and intelligence vendors: $570 million; security and authentication startups: $480 million; sales technology vendors: $350 million.
Data from Capital IQ and Quid Inc., as of September 10, 2015. Investments in US-based companies that have raised at least $10 million.
Apixio, www.apixio.com/solutions/#, accessed September 19, 2015.
PRWeb, “Wellframe closes $8.5 million in Series A financing,” September 8, 2014, www.prweb.com/releases/2014/09/prweb12149420.htm, accessed September 19, 2015.
Davey Alba, “The startup that’s bringing AI to ultrasounds and MRIs,” Wired, November 4, 2014, www.wired.com/2014/11/butterfly-network/, accessed September 19, 2015.
Startups with solutions aimed at the retail sector have raised $520 million; adtech startups have raised $510 million; medical + diagnostics: $440 million; education: $270 million.
CALLOUT
Speech: From medical dictation to millions of web searches [** Image: clerk with headset; someone doing voice search on a phone: like this.]
Vision: from industrial automation to consumer applications (pictures) [**Image: image of “machine vision”, to amazon Flow
Watson: from Jeopardy to medicine, financial services, recipe design
Intelligent automation [**image: automated process flow?]
Speech: From medical dictation to millions of web searches [** Image: clerk with headset; someone doing voice search on a phone: like this.]
Vision: from industrial automation to consumer applications (pictures) [**Image: image of “machine vision”, to amazon Flow
Watson: from Jeopardy to medicine, financial services, recipe design
Intelligent automation [**image: automated process flow?]
All or part of a task, job, or workflow requires low or moderate level of skill plus human perception: Forms processing, first-tier customer service, warehouse operation
Large data sets: Investment advice, medical diagnosis, oil exploration
Expertise can be expressed as rules: Scheduling maintenance operations
Workers’ cognitive abilities or training are underutilized: Writing company earnings reports; e-discovery; driving/piloting
Business process has high labor costs: Medical insurance utilization management
Expertise is scarce; value of improved performance is high: Medical diagnosis; aerial surveillance; trading
If you have a business process performned by abundant, low-cost people, little benefit
Kraft iFood Assistant (SR) - voice control
L'oreal Diagnost my hair (NLP) - natural text response
Sharp cororobo vaccum (SR, Rob) - voice control
Aether cone (SR, ML) - simplicity - machine learning and no nobs
Guess True Fit (ML)
L'Oreal Makeup Genius (CV)
Pepper robot (CV, SR, R)
Industry-standard performance requires use of cognitive technologies: Online retail product recommendations
A service cannot scale relying on human labor alone: Fraud detection; Media sentiment analytics – what are people saying about us? Do they tend to experss positive, negative, or neutral sentiment. How is this changing over time?
Translation example:
Do away with human translators
Machine translation plus human cleanup
Give routine translation to machines; focus on higher end stuff like marketing copy
Translation assistant – scan corpus; recommend phrases
Replace. With the cost strategy, organizations replace workers with cognitive computing systems that perform equivalent work. The financial appeal of this choice is clear, but limited to the cost savings that it might achieve. Organizations may produce greater value by reassigning workers to new roles, or expanding their roles. Or they might seek to deploy cognitive systems that not only substitute for human workers but provide superior performance, measured in speed or quality, for instance. These are examples of the value strategy.
Automize. Automizing work to reduce labor costs is an example of the cost strategy. But automizing can be disempowering and alienating to creative people, the highly skilled, or artisans. A value strategy might automize to create new lower-cost offerings that serve the needs of a new market segments. For instance, translation service providers could offer a range of qualities at different prices by varying by the level of automation used in the translation and using less experienced translators to perform post-editing.
Relieve. A cost strategy might realize the benefits of efficiency with this automation choice by reducing headcount. An example is call centers that automate first-tier customer support in order to reduce staffing levels. A value strategy, on the other hand, might expand or shift the focus of the workers to higher-value tasks. For instance, when a new automated engineering planning system saved the expert engineers of the Hong Kong subway system two days of work per week, they reallocated their time to harder problems that require human interaction and negotiation.
Empower. A cognitive system may empower lower-skilled workers to perform tasks that were formerly performed by higher-skilled workers. This is an example of the cost strategy at work. A value strategy might employ a system not only to empower lower-skilled workers but also to train them and build their skills. It might also be designed to enhance the performance of even highly skilled workers.
It should be noted that cognitive automation, even in systems intended to empower workers, may meet with resistance. An illustration of this can be found at Intel, which, as mentioned earlier, developed a cognitive system to improve sales productivity. The system used machine learning to classify customers and guide sales people on what to offer different customers. Some members of the sales team were initially resistant to following the advice of the machine learning system, possibly because they resented that their salesmanship was being subordinated to a machine. But after an initial group of sales people adopted the system and saw a dramatic improvement in sales productivity, the rest of the sales team was quick to follow. If the essence of a sales person’s work is building and maintaining relationships with customers, a little automated assistance that prioritizes customer calls and recommends offers may be an empowering use of technology. David Schatsky, Craig Muraskin, & Ragu Gurumurthy, “Cognitive technologies: The real opportunities for business.”
Rachel King, "How Intel’s CIO Helped the Company Make $351 Million."
Workers with spreadsheet skills likely receive higher pay than clerks working with pencil and paper before them, for instance. Construction workers skilled with power tools and sophisticated machinery command higher wages than unskilled manual laborers.