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Uploaded byAlessandro LANTERI, PhD, CPA
Smart Business and Artificial Intelligence
The document discusses the role of artificial intelligence (AI) and machine learning in transforming business strategies and models, focusing on their impact on various industries. It highlights the importance of collective intelligence, the collaboration between humans and technology, and the exponential growth of AI capabilities. The text also references key partnerships and projects aimed at exploring future innovations in banking, mobility, and product design.
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Smart Business and Artificial Intelligence
- 1. source: thenextweb.com (2017) SMARTBUSINESS IN AN AGE OF INTELLIGENT MACHINES Dr. Alessandro Lanteri Entrepreneur Day DTEC, Nov 2017 Dubai (UAE)
- 2. Alessandro Lanteri Founded (2009),advised business incubators TEDx speaker (2017) Advisor, Consultant, Coach PhD Philosophy & Economics, Erasmus (NL) MA Economics, Bocconi (IT) Exec.Ed. Said Oxford (UK), MIT (USA) Academics Abu Dhabi University (UAE) Hult International Business School (UK) Professor of Entrepreneurship Ran a marathon | Lived in 15+ global cities Other Stuff https://www.youtube.com/watch?v=HAfLCTRuh7U
- 3. >> universities whereI worked << UCLA NYU HULT AUB ADU ERASMUS BOCCONI HELSINKI
- 4. Open innovation partnership topropose solutions for the future of banking and private banking. Virgin Money (2015/16) Portfolio selected recent projects Open innovation partnership on the future of mobility. Ran multiple design sprints. Ford Motors (2016) Open innovation partnership to define business model and go-to-market strategy for a smart tag. ABB (2016) Open innovation partnership to design new products and a new social business model. Unilever (2017, 2015) Member of the advisory board of PACI. Led workshops in Geneva, Mexico City, London. Contributed to Davos reports. World Economic Forum(2016/17) Open innovation partnership to design a new service and a new business model. www.studentlifestart.com Virgin Money (2016/17)
- 5. Exponential Technologies The Future(and Present) of Work Exponential Leadership AI will Save the World. Or Destroy It Overview Collective Intelligence AI & Machine Learning Autonomous Vehicles AI Strategy for Disruption Trends in AI Startups The Future of AI Natural Language Processing Robots and other Machines
- 6. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Content for today Exponential Technologies
- 7. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Content Exponential Technologies
- 9. Machine Learning (1) tree/not tree type training set features classifica/on value tree/not tree type features + classifica/on valuetraining set
- 10. Machine Learning (2) neural network (deep, convolu/onal, recurrent…) input algorithm output [] 1 0 0 1 … [] 1 0 0 1 …
- 11. source: Lee et al. (2011)
- 13. What do yousee? A group of young people playing a game of Frisbee. Computer caption A group of men playing Frisbee in the park. Human caption source: Google (2016)
- 14.
- 15. What do yousee? http://places2.csail.mit.edu/demo.html A group of young people playing a game of Frisbee. Computer caption A group of men playing Frisbee in the park. Human caption
- 16. Machine Learning (3) source: MGI (2017)
- 17. “Almost all ofAI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B).” “ source: Ng (2016) - Andrew Ng -
- 18. “Can a problembe solved with ML?” “Are there enough data to train ML?” “Is the solution predictable enough for patterns to be reliable?” ?
- 19. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
- 20. A Cambrian Explosionin AI NetworksData Algorithms Exponential TechsCloud source: McAfee & Brynjolfsson (2017)
- 21. source: Kurzweil (2001), Singularity University (2017)
- 22. Progress Time 30 1 billion linear exponential source: Kurzweil (2001), Singularity University (2017) linear: 1, 2, 3, 4… 30 exponential: 1, 2, 4, 8… 109
- 23. Progress Time 30 1 billion linear exponential source: Kurzweil (2001), Singularity University (2017) linear: 1, 2, 3, 4… 30 exponential: 1, 2, 4, 8… 109 😕 😍 😮 DECEPTIVE GROWTH DISRUPTIVE GROWTH
- 24. “The Law ofAccelerating Returns: Price, performance, and capacity of information technology progress at a predictable, exponential rate” “ source: Kurzweil (2001) - Ray Kurzweil -
- 25. source: Diamandis & Kotler (2016), Singularity University (2017) The 6 D’s of exponential techs 3 EFFECTS 3 PHASES
- 26. “What amazing ‘nextbig thing’ does not seem to be giving any results?” “What physical product or service can be digitized?” “What expensive product will change the world when it becomes widely available?” ?
- 27. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
- 28. The role(s) ofhumans Creating Software Selecting Applications Performing Human-‐only TasksFixing Problems source: Malone (2017) Providing Examples Providing Feedback
- 29. The role(s) oftechnology Tool Tech performs the task Humans monitor tech Assistant Peer spreadsheets, cruise control Tech with some supervision Tech takes some initiative KLM’s social media bots, StichFix IBM’s Watson Tech/Humans perform similar tasks Humans solve complex cases Lemonade insurance Amazon package control Manager Tech assigns tasks, evaluates, trains humans CrowdForge, Cogito source: Malone (2017)
- 30. Collective Intelligence source: Malone (2017) How can humans and machines be connected so that collec%vely they act more intelligently than any person, group, or machine can? Input Output Machine Humans Learning Loop e.g.
- 31. Collective Intelligence cases ? https://www.youtube.com/watch?v=fIw7BwIoGus taking the robot out of the human https://www.youtube.com/watch?v=6KRjuuEVEZs making the human into a robot? Assistant Peer
- 32. “What jobs entailmany repetitive and predictable tasks?” “Can they be automated?” “Will a computer take these job?” ?
- 33. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
- 34. Tailoring products to narrowmarket segments. Niche Serving customers at a lower cost than competitors. Cost Leadership Providing customers with unique value. Differentiation AI & Business Strategy source: Porter (1979), Martin (2015) Robotic Process Automation Loan default Axa “large loss” Google search algorithms IBM Watson TellusLab Clustering to identify niches Netflix & Amazon recommendation systems
- 35.
- 36. “What industry hasthe indicators of potential for disruption?” “What archetype of disruption would work in that industry?” “What disruption will ensue?” ?
- 37. AI & MachineLearning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
- 38. source: Corea (2017) Trendsin AI startups
- 39. source: Corea (2017) Trendsin AI startups
- 40. Trends in AIstartups source: Corea (2017) open source (academics, raise bar for competition, lower bar for adoption, multiple crowd/platform effects) Unprofitable similar to pharma (long-term, uncertain returns to expensive R&D) data is the new oil Pharma-like + Oil exit in 1-3 years poaching of scarce AI talent Early acqui-hire
- 41. source: CB Insights, in: Corea (2017) Trends in AI startups
- 42. source: Corea (2017) Trendsin AI startups
- 43. thank you source: www.slidemash.com AlessandroLanteri alelanteri@gmail.com
- 44. References Corea, F. (2017). Artificial Intelligence and Exponential Technologies: Business Models Evolution and New Investment Opportunities. Springer. Diamandis, P. & S. Kotler (2016). Bold. How to Go Big, Create Wealth and Impact the World. Simon & Schuster. Kurzweil, R. (2001). “The law of accelerating returns”, www.kurzweilai.net/the-‐law-‐of-‐accelerating-‐returns Lee, H., Grosse, R., Ranganath, R. & A. Ng (2011). “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”. Comm. ACM 2011. Malone, T. (2017). MIT Sloan & MIT CSAIL Artificial Intelligence: Implications for business strategy program 2017-‐10-‐30. MIT. Martin, R. (2015). “There Are Still Only Two Ways to Compete”, Harvard Business Review, April. McAfee, A. & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. Norton & Company. McKinsey Global Institute (2016). The age of analytics: Competing in a data-‐driven world. www.mckinsey.com/business-‐ functions/mckinsey-‐analytics/our-‐insights/the-‐age-‐of-‐analytics-‐competing-‐in-‐a-‐data-‐driven-‐world Ng, A. (2016). What AI Can and Can't Do. https://hbr.org/2016/11/what-‐artificial-‐intelligence-‐can-‐and-‐cant-‐do-‐right-‐now Porter, M. (1979). “How Competitive Forces Shape Strategy”, Harvard Business Review, 57. Singularity University (2017). Understanding Exponentials. https://beta.su.org/courses/understanding-‐exponentials