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Cognitive/AI: views, perspectives & directions

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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

Notes de l'éditeur

  • David Cournapeau

    Hadley Wickam

    Max Kuhn



    ”Basically, our goal is to organize the world’s information and to make it universally accessible and useful”
    Larry Page

    “People have really gotten comfortable not only sharing more information and different kinds, but more openly and with more people - and that social norm is just something that has evolved over time”
    Mark Zuckerberg

    “It's not an experiment if you know it's going to work”
    Jeff Bezos

    “Software is also eating much of the value chain of industries that are widely viewed as primarily existing in the physical world.”
    Marc Andreessen

    “You will always learn more on the automation of the process than on the product itself”
    Elon Musk

    WSJ 2011, SW is eating the world
    Marc Andreessen (Cedar Falls, 9 luglio 1971) è un informatico e imprenditore statunitense.
    È anche meglio conosciuto come coautore di Mosaic[1], il primo web browser ad essere vastamente utilizzato, e cofondatore di Netscape Communications Corporation. Era amministratore delegato della Opsware, una impresa di software fondata originariamente con il nome di Loudcloud, quando questa è stata acquisita da Hewlett-Packard. Inoltre, è anche cofondatore di Ning, un'impresa che fornisce una piattaforma per la creazione di siti di reti sociali.


  • A machine can read millions of essays or see millions of eyes within minutes. We have no chance of competing against machines on frequent, high-volume tasks. But there are things we can do that machines can't do. Where machines have made very little progress is in tackling novel situations. 

    And it's important to recognize that this is true by virtue of speed alone. Right? So imagine if we just built a superintelligent AI that was no smarter than your average team of researchers at Stanford or MIT. Well, electronic circuits function about a million times faster than biochemical ones, so this machine should think about a million times faster than the minds that built it. So you set it running for a week, and it will perform 20,000 years of human-level intellectual work, week after week after week. How could we even understand, much less constrain, a mind making this sort of progress?
  • In a new paper, researches at Rutgers University in New Jersey and the Atelier for Restoration & Research of Paintings in the Netherlands examined how machine learning can be harnessed to more effectively spot fakes. (source: PSFK) Given audio of President Barack Obama, researchers synthesized a high quality video of him speaking with accurate lip sync, composited into a target video clip. (University of Washinghton) Larger entities, such as Alibaba, are utilizing big data techniques to identify fakes and stop them at their source, ameliorating supply chain issues. The company's software scans roughly 10 million products a day Entrupy, a startup that uses a microscopic camera connected to a smartphone to take photos of the product for analyzation, recently added a mechanism that uses machine-learning algorithms to detect whether a product is real or not.

    Domino’s lets you easily build a new pizza (or reorder your favorite pizza) and track your order all from Facebook Messenger Tell H&M’s Kik chatbot about a piece of clothing you have and they’ll build an outfit for you Brands including AirBnB, Evernote, and Spotify started using chatbots on Twitter to provide 24/7 customer service Banks have created chatbots to let you check in on your account, such as your current balance and most recent transactions. And there are tax bots that help you track your business and deductible expenses Simply request a new meeting to Meekan and this Slack chatbot will look at everyone’s calendars to find times when everyone is available Chatbots will let you search for and compare flights based on price and location. Kayak’s chatbot even lets you book your flights and hotels entirely from inside Facebook Messenger You can get the latest headlines from mainstream media sources like CNN, Fox News, or the Guardian. Or you can get the latest tech headlines from TechCrunch or Engadget

    Big Data Applied to Tax Evasion Detection (IEEE Research Paper, 2016): the review resulted in the finding of 56 works of which 5 were identified as primary study. Categorized by the studies that address the problem by the use of pattern recognition methodologies, natural language processing and data analytics in auditing. In Maryland, the people charged with rooting out false refund claims are members of the Questionable Return Detection Team (QRDT). Like their counterparts in many other states, these experts use software to identify suspicious returns. They then investigate the returns to pinpoint which ones are fraudulent. Insights from analytics will help Indiana pursue six public policy goals: Increase private sector employment; attract new investment to the state; improve the quality of the state’s workforce; improve the health, safety and well-being of families; increase high school graduation rates; and improve the math and reading skills of elementary students. The state of Indiana acquired an in-memory computing platform to accomplish these goals. The University of Chicago’s Computation Institute builds solutions for big data projects like genomics analysis. Needing a cost-effective way to provide always-on service to labs around the world, it hosts its Globus Transfer and Globus Genomics services on AWS. The Municipal Property Assessment Corporation runs its core property valuation engine 5,000 percent faster at one-tenth the cost using AWS instead of its older IT architecture. MPAC is a public sector organization responsible for providing valuations for more than 5 million properties across Canada. HMRC has replaced conventional debt processing systems with an innovative IT solution capable of mass customizing debt collection interventions based on insights into customer behaviour. Flexible, analytics-based collections have underpinned a key part of HMRC’s Debt Management Change Programme, which is well on its way to delivering targeted benefits of over £3billion additional debt collected by March 2015.

    American Express starts looking for indicators that could predict loyalty and developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months “BankAmeriDeals” provides cash-back offers to credit and debit-card customers based upon analyses of their prior purchases. Mint.com is a free web-based personal financial management service for the US and Canada. Mint.com uses big data to provide users information about their spending by category and have a look where they spent their money in a given week, month or year With a full history of mergers & acquisitions, Nordea needed a financial performance environment allowing end-to-end data lineage and governance ensuring quality, transparency and traceability. Social lending platforms and other Fintech such as Lenddo, Lending Club, Zest, etc. use Big Data platforms to assess solvability of customers

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