Digital Transformation

The rise of smart machines … the dawn of automated workers

Tom Austin presented on the rise of smart machines and their application in the work place, and society at large. I think this is pretty cool technology, and will revolutionize the way man and machine interact over the coming decades. It will probably come faster than we keep up with and Uncle Sam will probably get in the way. So no self-driving cars, or fleets of trucks, or even freight trains, any time soon. Even though these would help reduce CO2 emissions and make roads safer.

Smart machines are not general purpose computing – they are not really smart – they operate in a specific environment (i.e. volvo vs airbus).
Smart machines may scare us – under what conditions will we trust technology to make decisions that we used to make?

  • they operate autonomously
  • appear to understand abstract concepts

Example of machine learning: Google deep neural network – analyzed 10 million frames from random youtube videos and was able to identify 20,000 classes of images in 72 hours – detected cat faces, two people kissing, dancing – with a very high degree of accuracy.

The algorithms based on 2005 – 2008

Microsoft demo – Rick Rashid spoke English, yet audience heard Mandarin

  • Deep neural nets are one key to learning and understanding
  • images
  • faces
  • emotions, etc
  • large bodies of unstructured content

Autonomous movers – robot in amazon warehouse, Caterpillar truck (autonomous driving), google cars (self driving) – autonomous cars on streets of gothenburg,
x47 drone

Sages – virtual personal assistances (focus on context)
Apple Knowledge Navigator (you tube video)
Apple Newton
Siri (precursor to smart personal assistance)
Search: Eric Jorvtix TEDx 2013”
Google Now:
Knowledge Graph
Deep Neural Network
End of search

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Smart Advisors (focus on content)
IBM Watson
not a personal assistant
deep but narrow knowledge of content
Watson app (Memorial Sloan Kettering)
clinical oncology
breast cancer treatment recommendations
E-discovery
advanced natural language techniques
Natural language generation
fool the reader or listener
narrative science / yseop (easy-op)

Doers
Cornell Robot – predictive physical assistant
People and technology working together
Laggards lose
collaborate with machines you trust
replace people
PEDs – performance enhancing devices
47% of US jobs are at risk over the next decade or two – Frey & Osborn, Oxford, September 2013

34% of careers will be enhanced by smart machines

IT matters a lot
BYO smart machines will thrive
privacy, security and innovation are at odds
single vendor will be at a major strategic disadvantage

Smart Machines
engage, empower and delight employers
drive while intoxicated

Smart Advisors First

  • should you exploit a smart advisor
  • should you buy one?
  • how can these make the highest paid workers perform better
  • Virtual Personal Assistance
  • Leverage information explosion
  • Many assistants coming
  • Users will discover by doing if they can
  • Innovation, privacy, and security are at odds

Actions:

  • Consider consumer-grade business opportunities now
  • 2017 will be the year these technologies take off at work
  • Your users need to start experimenting heavily by 2015

 

Non-routine jobs are increasing
Routine jobs are in decline (60% in 1976 – 40% 2014)

New “Worksplace” Strategy,
man-machine collaboration
helping you excel at difficult tasks

Recommendations

  • Get Smart
  • calls for IT leadership, not just management
  • Engage the business
  • Respect the impact on people
  • impact of software and robots on employment, work, and careers of people will be profound
  • Read: “Cool Vendors in Smart Machines in 2014” – Gartner Paper
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