Uploaded byLee Schlenker
Technology and Innovation - Introduction
This document provides an introduction to a training module on digital technologies and innovation. It discusses artificial intelligence (AI) and its role in innovation. The training will cover topics like the building blocks of AI, digital economics, the internet of value, decision making, data ethics, and how AI can be used as a lever for innovation. It outlines the course administration, including grading based on a final exam and an innovation project. The introduction discusses using data to help management make better decisions and defines the key elements involved in applying analytics, including defining the problem, making predictions, evaluating outcomes, and taking action. It also outlines the machine intelligence continuum from rule-based automation to developing human-like intelligence.
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Technology and Innovation - Introduction
- 1. Digital Technologies andInnovation Introduction April 2019 http://DSign4Methods.com
- 2. “For every complexproblem, there's a solution that is simple, neat, and wrong." H.L. Mencken Is AI the future of innovation?
- 3. • Data isnothing more than answers waiting for you to ask the right question • Behind one door I’ve hidden the key to a great career, and a beer behind the other two • You can choose any door • To lend you a hand, I will show you one bottle of beer. • What are your chances of finding the key? What do we mean by a better decision?
- 4. ©2019 L. SCHLENKER Agenda Introduction AdministrativeDetails Artificial Intelligence and Innovation The Building Blocks Introduction
- 5. Module Facilitator I workwith managers to help them understand how enterprise applications, web and mobile technologies can enrich their careers. The client portfolio in the ICT industry includes Microsoft, Apple, Ernst & Young, France Telecom, HP, IBM, Oracle and SAP . The work with the IT industry in Europe has included fifty partner and customer conferences, a dozen case studies, and various marketing support activities. Prof. Lee SCHLENKER, The Business Analytics Institute Mail : lee@lhstech.com Skype : leeschlenker Web : www.leeschlenker.com Introduction
- 6. Working to help managementtake better decisions People-based approach to Data Science Applied Research, Consulting, ExecEd modules, Boot Camps
- 8. Course Portal: http://DSign4Methods.com ©2019 BusinessAnalytics Institute The objective of this course is to build the students’ knowledge of the practice of innovation in a variety of industrial settings Introduction
- 9. This a placewhere managers and students of management can discuss and debate best practices in the digital economy, new developments in data science and decision making. Ask questions and get practicable answers, and learn how to use data in decision making. Analytics for Management https://www.linkedin.com/ groups/13536539 Introduction
- 10. 10©2019 LHST sarl •Analyze the context of each case to document the key processes of the organization or the market • Qualify the data at hand to understand the nature of the business challenges • Apply the appropriate methodologies in your predictive and prescriptive analyses, and • Integrate elements of visual communications in transforming the data into a call for collective action In this module , you will www.Dsign4methods.com Adminstration
- 11. 11 Innovation is aState of Mind ©2019 LHST sarl Introduction Session 1 The Building Blocks Session 2 Digital Economics Session 3 The Internet of Value Session 4 Decision Making Session 5 Innovation Session 6 Data Ethics
- 12. Grading Scale Participation: 50%of your grade will be based upon your innovation project Final exam: 50% of your grade will be based upon your results on the final multiple choice exam. Confirm or infirm the hypothesis that AI is a lever of innovation using an example from your experience. Insure that your analysis includes: • the conflict or the opportunity (why should your audience care about your story?) • the context (what skills, knowledge or experience has permitted this problem/opportunity to arise?) • the roadmap (how does this product, service, idea influence customer experience) • the happy end (how will your audience evaluate your story?) Introduction
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- 15. • Management isall about taking better decisions • What do better decisions mean (faster, more impressive, more precise) ? • Is it observable – how is something more precise answer to a problem? • The challenge is deciding what we want to measure Lewis Mumford, Technics and Civilization Decision Making ©2019 L. SCHLENKER
- 16. To help usunderstand the motivations, experience and objectives of the internal and external clients of the organization ROI Real time data ... Stockholders Competition “made in” “made by” ... The State Peu de barrières d’entrée Acquisitions, OPA... Partners Loyalty Real costs ... Clients The Enterprise Mobility Empowerment ... Employees Introduction
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- 18. • Inputs • Prediction •Evaluation • Actions • Outcomes
- 19. • Scan thecontext • Qualify the data at hand • Choose the right method • Transform data into action Introduction
- 20. Introduction What is AI? MachineLearning Artificial Intelligence Nature Knowledge Intelligence Vision Self-learning Algorithms Mimic human Behavior Use Scenario Learns from data Solve Complex Problem Aim Sufficient Solution Optimal Solution Evaluation Metrics Accuracy Success
- 21. Introduction What does bettermean? • Rule-based automation • Produce probabilistic predictions based on the data • Combine prediction with judgement to learn • Generate art and design • Extract and quantify emotional states • Develop mental constructs • Adapt to environmental needs The Machine Intelligence Continuum
- 22. Application Technology and Innovation Perception Howdo define the problem? Prediction What evidence will allow us to act? Evaluation How do we judge what better means? Action How does our decision translate into action? Training How does past data support our analysis? Input What types of data do we need to capture? Feedback How do we correct our errors Explication What have we learned from practice? Challenge : Data : Human Inteligence Machine Learning HI/AI ©2018 BAI
- 23. • Properties -digital experiences put in place to enrich organizational conversations • Platforms – digital technologies that create proximity between those that produce, and those that consume, experience • People – the managerial mindset • Practice - the operational realities of management Schlenker (2015) Introduction
Editor's Notes
- #7 Foundation of digital economy isn’t data but the ability to analyze the data to improve decision-making Training, research and consulting assignments in fields of digital economics, machine learning, AI and data storytelling The paradox of trust : more and more data, less and less trust in companies, organizations, and the State Data Science – decision making based on past best practices to focus on scenarios for the future Condition of trust - work on something that matters
- #21 Judgement : How much is it worth to respond quickly? How costly is it to not respond if it turns out that there was an intruder in the home?
- #23 it will help you clarify what the AI will contribute (the prediction), how it will interface with humans (judgment), how it will be used to influence decisions (action), how you will measure success (outcome), and the types of data that will be required to train, operate, and improve the AI.