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Cognitive/AI: views, perspectives & directions

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Intervento di Giancarlo Vercellino, Research & Consulting Manager di IDC Italia, all'IDC Cognitive & AI Summit del 22 novembre 2018 a Milano

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Cognitive/AI: views, perspectives & directions

  1. 1. Cognitive/AI: views, perspectives & directions IDC Cognitive Conference Milan, 21 November 2018
  2. 2. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly Agenda A different approach to the problem of Truth When machines start learning human tasks Between rock stars and sorcerers 2
  3. 3. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly A simple acid test for data science: who’s who 3
  4. 4. Who fears an algorithmic society? 4 Learning Networks/ Deep Learning Reading & Writing Abstraction & Integration Detection & Visualization Speaking & Listening "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim" (Edsger Dijkstra) "Any AI smart enough to pass a Turing test is smart enough to know to fail it“ (Ian McDonald) "All this talk about artificial intelligence is really just hype, it will take at least fifty years before we have to let them vote" (Kenneth Boulding) “By their very nature, heuristic shortcuts will produce biases, and that is true for both humans and artificial intelligence, but the heuristics of AI are not necessarily the human ones” (Daniel Kahneman)
  5. 5. 5 From the realm of “really helpful” to the realm of “pretty strange” Counterfeiting vs. Anti- counterfeiting Chatting with bots everywhere Changing the way of doing banking Big Data for enhanced government
  6. 6. © IDC 6 What are we talking about? Machine Learning/ Deep Learning Conversational Technologies + + Vertical Applications MACHINE INTELLIGENCE ARTIFICIAL INTELLIGENCE AUGMENTED INTELLIGENCE
  7. 7. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly 7 From data to knowledge: key success factors I. New predictive technologies (ML is the king) II. Data governance (collection, retention, discovery, reuse) across boundaries III. Tie structured and unstructured data sources together IV. Organizational culture valuating information as a key asset But an important factor is often underestimated, the decision-making style V. Not all the decision makers are created equal!
  8. 8. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly A paradigm shift in the way we produce new knowledge 8 SERENDIPITY (N.) FINDING SOMETHING GOOD WITHOUT LOOKING FOR IT DISCOVERY (N.) NAVIGATING DATA THROUGH INTUITIVE INTERFACES vOLD STYLE NEW STYLE
  9. 9. © IDC 9 Building the Intelligent Core on the right Platform Cognitive Processes, Fueled by Data The long-term shot: intelligent business automation
  10. 10. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly Steps towards the Intelligent Enterprise 10 Pondering Transformation Automation First Analytics for immediate competitive edge Analytics for long-term value “Automation, optimization? Why? We have so many things to fix, automation is the last of our problems. We need to cut IT costs!” “We already implemented some data platforms for analytics, we are not investing anymore in the brief term” “Everyday we are struggling with our information systems. We are not so confident that data is actually a competitive asset” “Why everyone is talking about Big Data? It could be really useful only for few companies” “We are focused on an hazy goal: transforming our business model!” “Automation at all costs, no matter what!” “We are evaluating different analytic platforms for better automation” “Data could be a competitive asset, but we are not so sure” “Our future certainly does not depend on Big Data!” “Our business model is pretty good, we don’t need to change it” “We collect data and information, but not for automation purpose” “We regularly use our analytic platform, sometimes investing for upgrade” “Transforming the way we manage data is of paramount importance” “We don’t look too far in the future and have no time to play with words. You can call it Big Data or whatever, it doesn’t matter” “Well, our market is well-established. We change our business model with a great deal of caution ” “We use intelligent KPIs to ensure the efficiency of our processes, products and services” “We heavily invested over the years to build our analytics capabilities” “Any winning digital transformation strategy depends on the way you use data and information” “Big Data is the future for any intelligent company” “We already changed many times. Transformation is the journey!”
  11. 11. © IDC 11 Managing the budget between IT and innovation Source: IDC Italy, 2017 (n = 500, weighted extrapolation)
  12. 12. © IDC 12 Value for money, and for data too RELATIONAL CAPITAL STRUCTURAL CAPITAL HUMAN CAPITAL •TRUST & REPUTATION •CUSTOMERS & SUPPLIERS RELATIONSHIPS •STAKEHOLDERS’ RELATIONSHIPS •ORGANIZATION & INFORMATION SYSTEMS •PROCESS DESIGN & ORGANIZATIONAL ROUTINES •INTELLECTUAL PROPERTIES & INTANGIBLE ASSETS •PEOPLE •KNOW-HOW/ EXPERTISE •TACIT KNOWLEDGE BRAND EQUITY GOODWILL COGNITIVE EDGE BIG DATA PLATFORMS SHIFTING UNDERLYING ECONOMIC ASSUMPTIONS ON THE ROOTS OF VALUE FROM RETURN ON INVESTMENTS TO RETURN ON OFF-THE-BOOK ASSETS DATA & INFORMATION ARE REIFYING INTANGIBLE ASSETS
  13. 13. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly From CDO to Chief Monetization Officer • Revenue leaks • Infer customer satisfaction/ churn risk & scientific marketing • Moving from product to service (from solving production problems to data logistic) • Fraud/ piracy detection and other abusive behaviors 13
  14. 14. © IDC 14 What the heck are they doing all the time? Build/ run machine learning services improving products/ workflows Do research that advances the state of the art of machine learning Build/ run data infrastructure for storing, analyzing, and operationalizing data Build prototypes to explore applying machine learning to new areas Analyze and understand data to influence product or business decisions 0% 5% 10% 15% 20% 25% 30% 35% World Wide (n=14.282) Italy (n=234) Most common activities Source: IDC elaboration on Kaggle Survey 2018
  15. 15. © IDC 15 The challenges data scientists are facing today Source: IDC elaboration on Kaggle Survey 2017 Unused results Protect insights from company politics Clarity of biz issues and conclusions Need for analytical data talent Need to deal with dirty/messy data 0% 10% 20% 30% 40% 50% 60% Italy (n=111) World Wide (n=6.183) Key Challenges for Data Scientists
  16. 16. © IDC 16 Beyond numbers, what kind of data? Source: IDC elaboration on Kaggle Survey 2018 Video Data Geospatial Data Sensor Data Image Data Categorical Data Time Series Data Text Data 0% 5% 10% 15% 20% 25% 30% 35% World Wide (n=14.282) Italy (n=234) The raw matter for insight
  17. 17. © IDC 17 The future of primary alphabetization Source: IDC elaboration on Kaggle Survey 2018 Visual Basic/VBA C#/.NET Bash/ Shell MATLAB Java C/C++ R Python 0% 10% 20% 30% 40% 50% 60% World Wide (n=14.282) Italy (n=234) Most required languages for data science
  18. 18. IDC FutureScape: a global perspective on the future of ML/AI 18© IDC Note: The size of the bubble indicates complexity/cost to address. Source: IDC, 2019
  19. 19. © IDC Visit us at IDCitalia.com and follow us on Twitter: @IDCItaly Concluding remarks Transforming a geek passion in a serious game Reality check: the power struggle behind decision making Against conformism: a community of artisans and polyglots
  20. 20. 20 IDC Italia Viale Monza 14 20127 Milano Tel: +39 02 28457339 gvercellino@idc.com Giancarlo Vercellino Research & Consulting Manager IDC Italy www.idc.com

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