Presentation from O'Reilly Solid Conference 2015 Looking back on our industrial past, we see countless examples of how technology has helped businesses reinvent organizations and processes to become more productive and outcompete. We are now at an inflection point in robotics and machine learning, two technology areas particularly poised to reshape industries like never before. In fact, it’s already happening. Robotic systems are automating warehouses. Machine learning and deep learning are helping firms handle massive and unwieldy datasets by automating analytics and decision-making. In the biotechnology world, companies are leveraging machines and data to build more productive R&D pipelines. Atoms and bits are both being automated en masse. With much more on the way, businesses will have to reinvent themselves once again, but how? In this talk, we will briefly review the current state of robotics and machine learning and why the timing is right. We will walk through a few case studies of both large companies and startups that have successfully ingrained these technologies into their everyday operations to become more productive and agile. Along the way, we will landscape relevant industries and opportunities and the ambitious startups chasing them.

1. Machine intelligence
to free human
intelligence:
How automation
helps you win
Roger Chen
@rgrchen
roger@oatv.com
O’Reilly Solid 2015
June 25, 2015

2. There’s been a lot of excitement about
robotics & AI recently
“the development of full artificial
intelligence could spell the end of
the human race.”
“I think it’s a distraction from the
conversation about…serious issues,”
"With artificial intelligence, we
are summoning the demon"

3. Contrary to what some would have
you believe…

4. …robot AIs are not going to end
humanity anytime soon

5. So don’t be awkward about it

6. And get to know the machines
that can help you

7. What’s going on?
How did this happen?
What’s new this time around?
Rules of engagement for
automation

8. So what’s all the fuss about?
Robots aren’t new…

9. …but these ones are

10. Big companies are innovating too
Source: BCG

11. Robotics is accelerating
0
50
100
150
200
250
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Industrialrobots(000s)
Global industrial robot sales

12. Globally
South Korea
Japan
Germany
US
China
Industrial robots
operating by region
Industrial robots per 10,000
manufacturing employees
30
437
323
282
152
307K
237K
182K
176K
175K

13. How did this happen?

14. Smartphones

15. How did this happen?
Smartphones
Sensors

16. Novel, cheaper sensors

17. How did this happen?
Smartphones
Sensors
Actuators

18. Actuator & servo innovation
¡ Compliance (safety)
¡ Cost
¡ Performance
Series elastic
actuator (1993)
Further actuator
innovation (1993 – …)
First low-cost
robotics startups
(Baxter, 2008)
New startups
pushing envelope
(Modbot, 2015)

19. How did this happen?
Smartphones
Sensors
Actuators
Software

20. Open source software:
Robot Operating System (ROS)
¡ Collaboration
¡ Standardization
¡ Lowering technical barriers
¡ “From 20 man-years to 18 months”

21. ROS proliferating in technical world
0
200
400
600
800
1000
1200
1400
1600
1800
Citations
Total citations over time

22. 0
10000
20000
30000
40000
50000
60000
Jan-07
N
ov-07
Sep-08
Jul-09
M
ay-10
M
ar-11
Jan-12
N
ov-12
Sep-13
Jul-14
IPs
Unique IPs downloading packages
Switchyard
(Feb 2007)
ROS at Willow
Garage
(Nov 2007)
ROS at ICRA
(May 2009)
ROS 0.4 release
(Feb 2009)
ROS being put to use

23. ROS global

24. How did this happen?
Smartphones
Sensors
Actuators
Software
Market

25. Consumers
Online retail sales
$262B (US, 2012)
10% CAGR
Logistics and
transportation
Total spending
$1.33 trillion (US, 2013)
8.5% of US GDP
Customers (brands)
Want more for less to satisfy
consumer demand
Competitors
Are undercutting one
another on price
Faster
Xiaomi
$12B sales
135% ñ
Cheaper
Personalized
Intense market forces
Workforce
600K unfilled mfg jobs
25% turnover in MH&L

26. What’s new this time around?

27. What’s new this time around?
It’s not just hardware

28. Rise of data and machine learning
¡ Machine learning algorithms (deep learning)
¡ Explosion of training data
¡ Hardware (GPUs)
¡ Interest and resources

29. Deep learning

30. Explosion in training data

31. Perception automation

32. GPUs

33. What’s new this time around?
Connectivity
Connect, measure, control

34. DevOps in the physical world
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Deploy

35. What’s new this time around?
Flexible automation

36. Flexible
No days off!Keep working!
Pick items for shipping
Pack manufactured goods
Clean Roger’s car
Do some research
Transport some boxes

37. Why flexible automation matters
ROI ~ (ResultsA + ResultsB + ResultsC)/($A + $B + $C)
Project B
Project A
Achieve ROI across multiple deployments
Project C
Machine’s
work
capacity

38. Fixed to flexible to programmable
Production volume
Low Medium High
Variety
High
Medium
Low
Programmable
Flexible
Fixed
Too hard
Poor ROI

39. What’s new this time around?
Cost and compliance

40. Lower cost and compliance open
up new applications
$25K $34K

41. Fetch Robotics
Logistics
Warehousing
Material handling
Transportation
Manufacturing

42. Plethora Labs
Manufacturing

43. Emerald Cloud Lab
Life science lab robotics

44. Riffyn
R&D data automation
Design
Measure
Improve
Make it reproducible
Stack it
Build
protocols
like Legos
Automate data
acquisi=on
Automate error
checking & root
cause analysis
Share and
version like Git
Collaborate
Riffyn

45. Rules of engagement for
automation

46. It has to work
2+2=3?!

47. ROI
“If it doesn’t make dollars, it doesn’t make sense”
¡ Cost savings
¡ Productivity
¡ Quality
¡ Turnaround
¡ Scalability
¡ Reproducibility
¡ New opportunities

48. ¡ Machines for problem reduction, not the entire solution
Human + machine
Machine
• Perfect memory
• Complex statistical
analysis
• Repeatability
• Endurance
Human
• Intuition
• Creativity
• Inference
• Abstraction
But this matters!

49. Human + machine + process
“Weak human + machine + better process was superior to a strong
computer alone and, more remarkably, superior to a strong
human + machine + inferior process.”
-Garry Kasparov, Chess Grandmaster
The Chess Master and the Computer
Everyone should read this!

50. Use machines, intelligently
Machine’s
work
capacity
ROI ~ Work/$ ROI ~ Results/$
Garbage
work
Useful
work
¡ Garbage in, garbage
out
¡ Process matters – work
smart, not just hard
¡ What problem are you
really solving?
¡ If you struggle to fill
capacity, automation
may not be right for
you

51. Challenges
¡ Technology
¡ Perception, deployment, programming
¡ Implementation
¡ Safety, security, cultural acceptance
¡ Society
¡ Impact on labor and economy
¡ Machines are reinventing work…

52. What does the future of work look like?

53. “There’s plenty of
room at the bottom.”

54. “There’s plenty of
room at the bottom.”
There’s also plenty of
room at the top.

55. Thanks!
Roger Chen
@rgrchen
roger@oatv.com