How  good  are  you  working  with  intelligent  machines?    

“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?”  

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  

ExponenAal  Rate  Change    

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  

…  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  

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.  

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)  

ConnecAons  below  the  surface  are  where  tacit  informaAon  is  mined,  machine  learning  begins  and  is  applied  via  algorithms  at  massive  scale.  

h;ps://research.facebook.com/  

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  

Predict  2  week  market  adopAon  lead  Ame!    

TradiAonal   Network  Science  

Friend  Paradox  

TED  -­‐  Christakis  

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  

biochemicaldiagnostics

onlinerecruiting

music

fi nancial

payments

e-commerce

networks securitysecurity

cloud storagecloud storage

dataanalytics

telecom

health carehealth careIT

semiconductors

biologicsbiologics

search

biofuels

education

wind

solar

smart grid

travelreal estate

geolocation

imaging

medical devices

batteries

lighting LEDs

Locating Your Next Strategic Opportunity

To map semantic clus-ters, Quid software fi rst identifi es 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 fi lings, 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 fi rms 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 fi nd 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 fi nd 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

Vision Statement

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 identifi es 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 o! er fi rms strategic opportunities to connect companies operat-ing in di! erent markets, to take existing products into new sectors, or to innovate with products and services no one has even dreamed up yet."

HBR Reprint F##$%Z

The Pharma-Gaming Connection One of the most intriguing white spaces on this map is surrounded by some industry sectors that at fi rst 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

InsilicosProximic

gaming social media

genomicsbiopharma

ad targeting

Profi ling 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 Insilicosrequires similarly intense graphics processing, Nvidia has edged into the drug discovery space.

Solving Business Problems SociallyFoldit is an online social game for science geeks based on the challenge of fi nding the most e& cient way to fold proteins. But the thousands who play it can help solve real protein- folding challenges for bio pharma companies, which have begun putting the gaming platform to work.

Scientifi c Social NetworkingVivo 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

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.  

Sales  to  physicians  confirmed  at  95%  rate  using  Nugget  

Ethics  –  Who  is  minding  the  transforma<on  on  the  Future  of  Work?    

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  

What’s  your  comfort  level  working  with  intelligent  machines?