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How good are you working with intelligent machines?

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How good are you at working with intelligent machines?

Our technologies are an extension of us. We have now crossed over to technologies smarter than some of us, but not all of us.

In this presentation Victoria G. Axelrod will give an overview of current technology with an emphasis on the questions we need to be asking to intentionally shape a future already augmented by smart machines and algorithms. Utilizing “systems thinking” and network analysis will be central to framing the discussion.

KM Cafe 10/5/16 Axelrod Becker Consulting

Published in: Business
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How good are you working with intelligent machines?

  1. 1. How  good  are  you  working  with  intelligent   machines?    
  2. 2. “Are  you  good  at  working  with  intelligent   machines  or  not?  Are  your  skills  a   complement  to  the  skills  of  the  computer,  or   is  the  computer  doing  be;er  without  you?”  
  3. 3. Overview   Social  DisrupAon    -­‐  Some  data/research   •   how  work  gets  done  within  companies     •   loss  of  jobs/acAviAes  and  changing  nature  of  work   •   augmented  rather  than  fully  replaced    Systems  Thinking  and  IntenAonal  Networks  –  ExplanaAon  and   Examples   •   enhanced  decision  making   •   become  informed  and  engaged  in  use  or  understanding  of   network  analysis  at  scale  (individual,  group/project,  organizaAonal)   as  automaAon  transforms  work.   Ethics   •   social  research  without  our  knowledge  
  4. 4. ExponenAal  Rate  Change    
  5. 5. Oxford  University  report  2011  and  McKinsey  research     Key  findings  Oxford:   •     47%  of  all  US  jobs  were  at  risk  from  automaAon   Key  findings  McKinsey:   •     Less  than  5%  of  of  jobs  can  be  fully  automated   •     Below  the  job  or  occupaAon  level  to  work  acAviAes  45%  of  work   is  automatable  by  current  technologies.  Included  were  high  wage,   high  skilled  jobs.   h;p://bits.blogs.nyAmes.com/2015/11/06/automaAon-­‐will-­‐ change-­‐jobs-­‐more-­‐than-­‐kill-­‐them/?_r=0  
  6. 6. …  while  sophisAcated  algorithms  and  developments  in  Mobile  RoboAcs   (MR),  building  upon  with  big  data,  now  allow  many  non-­‐rouAne  tasks  to   be  auto-­‐mated,  occupaAons  that  involve     complex  percepAon  and  manipulaAon  tasks,   creaAve  intelligence  tasks,  and  social  intelligence  tasks  are  unlikely  to  be   subsAtuted  by  computer  capital  over  the  next  decade  or  two.     The  probability  of  an  occupaAon  being  automated  can  thus  be  described   as  a  funcAon  of  these  task  characterisAcs  …   h;p://www.oxfordmarAn.ox.ac.uk/downloads/academic/ The_Future_of_Employment.pdf  
  7. 7. More  specifically,  our  research  suggests  that  as  many  as  45  percent  of  the  acAviAes   individuals  are  paid  to  perform  can  be  automated  by  adapAng  currently  demonstrated   technologies.4  In  the  United  States,  these  acAviAes  represent  about  $2  trillion  in  annual   wages.  Although  we  oeen  think  of  automaAon  primarily  affecAng  low-­‐skill,  low-­‐wage   roles,  we  discovered  that  even  the  highest-­‐paid  occupaAons  in  the  economy,  such  as   financial  managers,  physicians,  and  senior  execuAves,  including  CEOs,  have  a  significant   amount  of  acAvity  that  can  be  automated.  
  8. 8. The  Four  Fundamentals:   1.  AutomaAon  of  acAviAes   2.  RedefiniAon  of  jobs  and   business  acAviAes   3.  Impact  on  high-­‐wage   occupaAons   4.  Future  of  creaAvity  –  4%   and  meaning  –  29%  (emoAon)  
  9. 9. ConnecAons  below  the  surface  are  where  tacit  informaAon  is   mined,  machine  learning  begins  and  is  applied  via  algorithms  at   massive  scale.  
  10. 10. h;ps://research.facebook.com/  
  11. 11. Everything  we  do  at  Facebook  is  seen  as  a  graph.  (2012)   Cameron  Marlow  Former  Head  and  Founder,  Data  Science  Facebook   h;p://www.scienAficamerican.com/arAcle.cfm?id=social-­‐scienAsts-­‐ might-­‐gain-­‐access-­‐facebooks-­‐data-­‐use  
  12. 12. Predict  2  week  market  adopAon  lead  Ame!     TradiAonal   Network  Science   Friend  Paradox   TED  -­‐  Christakis  
  13. 13. It  may  not  qualify  as  a  lightning-­‐bolt  eureka   moment,  but  Jeffrey  R.  Immelt,  chief   execuAve  of  General  Electric,  recalls  the  June   day  in  2009  that  got  him  thinking.  He  was   speaking  with  G.E.  scienAsts  about  new  jet   engines  they  were  building,  laden  with   sensors  to  generate  a  trove  of  data  from   every  flight  —  but  to  what  end?   That  data  could  someday  be  as  valuable  as   the  machinery  itself,  if  not  more  so.  But  G.E.   couldn’t  make  use  of  it.   “We  had  to  be  more  capable  in  soeware,”   Mr.  Immelt  said  he  decided.  Maybe  G.E.  —  a   maker  of  power  turbines,  jet  engines,   locomoAves  and  medical-­‐imaging  equipment   —  needed  to  think  of  its  compeAtors  as   Amazon  and  IBM.   Predix  Soeware   When  he  lee  Apple,  Mr.  Haas   was  head  of  cloud   engineering,  managing  the   compuAng  engine  behind   Siri,  iTunes  and  iCloud.   At  GE  Digital,  Mr.  Haas  has  a   similar  Atle,  head  of  plasorm   cloud  engineering,  but  in  a   different  setng.  He   describes  his  job  as  applying   modern  soeware  technology   —  machine  learning,  arAficial   intelligence  and  cloud   compuAng  —  to  the   industrial  arena.  “I’ve  got  my   work  cut  out  for  me,”  he  said.   GE  Backstory     OrganizaAonal  Business  Case   Individual  who  automates   work  
  14. 14. biochemical diagnostics online recruiting music financial payments e-commerce networks securitysecurity cloud storagecloud storage data analytics telecom health carehealth care IT semiconductors biologicsbiologics search biofuels education wind solar smart grid travel real estate geolocation imaging medical devices batteries lighting LEDs Locating Your Next Strategic Opportunity To map semantic clus- ters, Quid software first identifies hundreds of key phrases associated with individual companies and organizations, or their “n-grams.” Applying algo- rithms and other analyti- cal tools, the technology parses text in millions of corporate documents, from patent filings, to press releases, to Twitter posts. The software then creates a map with lines connecting companies whose n-grams are alike. The lines act like gravita- tional pull: The more lines there are between com- panies, the more tightly together those companies are drawn. Similar firms become clustered into industry sectors. The result is a multi- dimensional industry map like the one below. It represents 4,000 tech- nology enterprises—from venture-backed start-ups to established public companies—that received media coverage and Where and how do strategists find growth opportunities? Sometimes by literally drawing a map, using a technique called semantic-clustering analysis. Such maps can reveal not only which sectors are thick with competition but where in the market white spaces are open for the taking. For example, while it may seem odd to find opportunity in the nexus between gaming and biopharma, seeing is believing. Data and visualization by Sean Gourley of Quid; graphic design by Open gaming social media genomicsbiopharma ad targeting IDEA WATCH 34 Harvard Business Review March 2011 VisionStatement
  15. 15. Semantic-clustering software locates and analyzes the documents in a company’s digital footprint. Documents are catego- rized and weighted for importance. The software then identifies the company’s n-grams, or key phrases. The company’s n-grams are then compared with other companies’ n-grams. The process is then repeated for every company in the sample to generate the map. When at least 80% of their n-grams are similar, companies are linked on the map. How N-Gram Mapping Works showed capital growth last year. Such maps expose surprising relationships between and across sectors and, even more tantalizing, the white spaces among them—which can offer firms strategic opportunities to connect companies operat- ing in different markets, to take existing products into new sectors, or to innovate with products and services no one has even dreamed up yet. HBR Reprint F1103Z The Pharma-Gaming Connection One of the most intriguing white spaces on this map is surrounded by some industry sectors that at first glance may seem unlikely to be connected: biopharma, gaming, social media, and ad targeting. As shown in the box below, Selventa, Proximic, Vivo, Insilicos, Foldit, and Nvidia are some of the ventures seizing the strategic opportunities in this space. Sean Gourley is CTO and cofounder of Quid, in San Francisco. Open is a design studio in New York. Nvidia Foldit Vivo Selventa Insilicos Proximic gaming social media genomicsbiopharma ad targeting Profiling and Per- sonalized Medicine Selventa makes targeted drug discoveries by analyzing large amounts of patient data and statistically identify- ing patient cohorts that will respond well to special- ized treatments. To do so it borrows mathematical techniques from ad targeting companies like Proximic. Gaming Meets Drug Discovery Nvidia builds graphics pro- cessing units used in video games, among other things. Recognizing that work done by biomarker discovery and diagnostic development companies like Insilicos requires similarly intense graphics processing, Nvidia has edged into the drug discovery space. Solving Business Problems Socially Foldit is an online social game for science geeks based on the challenge of finding the most efficient way to fold proteins. But the thousands who play it can help solve real protein-folding challenges for biopharma companies, which have begun putting the gaming platform to work. Scientific Social Networking Vivo jumped into the white space between social gaming and pharma by building a Facebook-like online collabo- ration platform that helps scientists connect and share research and data. March 2011 Harvard Business Review 35 HBR.ORG
  16. 16. We  believe  that  the  same  AI  technology  that  gives  big  tech  companies  a   compeAAve  edge  should  be  available  to  developers  or  businesses  of  any  size  or   budget.  That’s  why  we  built  our  new  Custom  Training  and  Visual  Search   products  –  to  make  it  easy,  quick,  and  inexpensive  for  developers  and   businesses  to  innovate  with  AI,  go  to  market  faster,  and  build  be;er  user   experiences.  
  17. 17. Sales  to  physicians  confirmed  at  95%  rate  using  Nugget  
  18. 18. Ethics  –  Who  is  minding  the  transforma<on  on  the  Future  of  Work?    
  19. 19. Thank  You!   Victoria  G.  Axelrod     Principal,  Axelrod  Becker  ConsulAng   445  East  86th  Street   New  York,  NY  10028   212-­‐369-­‐2885   vaxelrod@axelrodbecker.com   www.axelrodbecker.com     Blog:  21st  Century  OrganizaAon   h;p://c21org.typepad.com  
  20. 20. What’s  your  comfort  level  working  with  intelligent   machines?  

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