Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Smart Business and Artificial Intelligence

599 views

Published on

This is the deck for my workshop at DTEC, the technological incubator of the Government of Dubai, during Entrepreneur Day 2017. #DTEC #AI #ED17

Published in: Business
  • Sharpen your mind with brain pill. learn more info.. ➤➤ https://tinyurl.com/brainpill101
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

Smart Business and Artificial Intelligence

  1. 1. source:  thenextweb.com  (2017) SMART BUSINESS IN AN AGE OF INTELLIGENT MACHINES Dr. Alessandro Lanteri Entrepreneur  Day   DTEC,  Nov  2017   Dubai  (UAE)
  2. 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. 3. >> universities where I worked << UCLA NYU HULT AUB ADU ERASMUS BOCCONI HELSINKI
  4. 4. Open innovation partnership to propose 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. 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. 6. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Content for today Exponential Technologies
  7. 7. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Content Exponential Technologies
  8. 8. Machine Learning (1) tree/not   tree  type training  set features classifica/on value tree/not   tree  type features  +  classifica/on valuetraining  set
  9. 9. Machine Learning (2) neural  network   (deep,  convolu/onal,  recurrent…)   input algorithm output [] 1   0   0   1   … [] 1   0   0   1   …
  10. 10. source:  Lee  et  al.  (2011)
  11. 11. What do you see? 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)
  12. 12. source:  Google  (2016)
  13. 13. What do you see? 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
  14. 14. Machine Learning (3) source:  MGI  (2017)
  15. 15. “Almost all of AI’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 -
  16. 16. “Can a problem be solved with ML?” “Are there enough data to train ML?” “Is the solution predictable enough for patterns to be reliable?” ?
  17. 17. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
  18. 18. A Cambrian Explosion in AI NetworksData Algorithms Exponential TechsCloud source:  McAfee  &  Brynjolfsson  (2017)
  19. 19. source:  Kurzweil  (2001),  Singularity  University  (2017)
  20. 20. 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
  21. 21. 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
  22. 22. “The Law of Accelerating Returns: Price, performance, and capacity of information technology progress at a predictable, exponential rate” “ source:  Kurzweil  (2001) - Ray Kurzweil -
  23. 23. source:  Diamandis  &  Kotler  (2016),  Singularity  University  (2017) The 6 D’s of exponential techs 3  EFFECTS 3  PHASES
  24. 24. “What amazing ‘next big 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?” ?
  25. 25. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
  26. 26. The role(s) of humans Creating  Software Selecting  Applications Performing  Human-­‐only  TasksFixing  Problems source:  Malone  (2017) Providing  Examples Providing  Feedback
  27. 27. The role(s) of technology 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)
  28. 28. 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.
  29. 29. 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
  30. 30. “What jobs entail many repetitive and predictable tasks?” “Can they be automated?” “Will a computer take these job?” ?
  31. 31. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
  32. 32. Tailoring products to narrow market 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
  33. 33. source:  MGI  (2017)
  34. 34. “What industry has the indicators of potential for disruption?” “What archetype of disruption would work in that industry?” “What disruption will ensue?” ?
  35. 35. AI & Machine Learning Collective Intelligence AI Strategy for Disruption Trends in AI Startups Exponential Technologies Content
  36. 36. source:  Corea  (2017) Trends in AI startups
  37. 37. source:  Corea  (2017) Trends in AI startups
  38. 38. Trends in AI startups 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
  39. 39. source:  CB  Insights,  in:  Corea  (2017) Trends in AI startups
  40. 40. source:  Corea  (2017) Trends in AI startups
  41. 41. thank you source:  www.slidemash.com Alessandro Lanteri alelanteri@gmail.com
  42. 42. 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

×