Machine intelligence to free human intelligence: How automation helps you win

Machine intelligence to free human intelligence: How automation helps you win

Roger Chen @rgrchen [email protected] O’Reilly Solid 2015 June 25, 2015

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"

Contrary to what some would have you believe…

…robot AIs are not going to end humanity anytime soon

So don’t be awkward about it

And get to know the machines that can help you

What’s going on?

How did this happen?

What’s new this time around?

Rules of engagement for automation

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

…but these ones are

Big companies are innovating too

Source: BCG

Robotics is accelerating

0

50

100

150

200

250

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Indu

stria

l rob

ots

(000

s)Global industrial robot sales

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

How did this happen?

Smartphones

How did this happen? Smartphones

Sensors

Novel, cheaper sensors


Sensors

Actuators

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)


Sensors

Actuators

Software

Open source software: Robot Operating System (ROS)

¡ Collaboration

¡  Standardization

¡  Lowering technical barriers ¡  “From 20 man-years to 18 months”

ROS proliferating in technical world

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200

400

600

800

1000

1200

1400

1600

1800

Cita

tions

Total citations over time

0

10000

20000

30000

40000

50000

60000

Jan-0

7

Nov-07

Sep-08

Jul-0

9

May-10

Mar-11

Jan-1

2

Nov-12

Sep-13

Jul-1

4

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

ROS global


Sensors

Actuators

Software

Market

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



It’s not just hardware

Rise of data and machine learning

¡ Machine learning algorithms (deep learning)

¡  Explosion of training data

¡  Hardware (GPUs)

¡  Interest and resources

Deep learning

Explosion in training data

Perception automation

GPUs


Connectivity

Connect, measure, control

DevOps in the physical world

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Deploy


Flexible automation

Flexible

No days off! Keep working!

Pick items for shipping

Pack manufactured goods

Clean Roger’s car

Do some research

Transport some boxes

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

Fixed to flexible to programmable

Production volume

Low Medium High

Va

riety

High

Medium

Low

Programmable

Flexible

Fixed

Too hard

Poor ROI


Cost and compliance

Lower cost and compliance open up new applications

$25K $34K

Fetch Robotics

Logistics

Warehousing

Material handling

Transportation

Manufacturing

Plethora Labs Manufacturing

Emerald Cloud Lab Life science lab robotics

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

Rules of engagement for automation

It has to work

2+2=3?!

ROI “If it doesn’t make dollars, it doesn’t make sense” ¡ Cost savings

¡  Productivity

¡ Quality

¡  Turnaround

¡  Scalability

¡  Reproducibility

¡ New opportunities

¡ 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!

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!

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

Challenges

¡  Technology ¡  Perception, deployment, programming

¡  Implementation ¡  Safety, security, cultural acceptance

¡  Society ¡  Impact on labor and economy

¡  Machines are reinventing work…

What does the future of work look like?

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

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

There’s also plenty of room at the top.

Thanks! Roger Chen @rgrchen [email protected]