1. Future IT Trends
Paul Hofmann PhD
CTO
March, 2017

2. The Big Five IT Mega Trends
• Internet of Things
Assets Turn Into Applications
• Machine Intelligence
AI Could Replace 50M Professional Jobs
• Distributed Ledgers
Block chain is becoming mainstream
• Sharing Economy
We don’t owe anything anymore
• Virtual and Augmented Reality
Remote experience merge visual & digital
world

3. IOT

4. Assets Turn Into Applications
• The Internet of Things is driving
automation in asset-intensive
industries.
• Everything will have a digital twin.
• Everything becomes software.
• Assets become applications.
Examples:
Amazon
Retail experience as application.
Goods, warehouses, delivery trucks
are peripherals.
Uber
Transportation as application.
Drivers, cars are peripherals.

5. Asset Sentinel
Application: Mobile Asset Monitoring
Asset Sentinel Listener is mounted on or in the trailer,
establishing connection with beacons on each pallet or
container. It reports environmental conditions such as
temperature, pressure, barometric pressure and light.
Custody of containers and palettes is maintained
throughout the journey, transferring from the origination,
to the trailer and driver, and then to the destination.
Can discern theft or misappropriation vs. package
not loaded.
Asset Sentinel is able to reconcile the beacons it heard
with the bill of lading as the driver pulls away. If the
real-time load does not reconcile, driver is alerted with a
push notification even before they leave
the yard.
Take custody of entire loads with a push of a button.

6. Asset Sentinel
Application: Mobile Asset Monitoring
Near real-time location and proximity of
trucks / containers / drivers at docks,
and at every point along the route. Real-
time logging of all environmental changes
to trailers and containers. Accelerometers
provide shock and drop information. Chain
of custody with bill of lading.
Take custody of entire loads with a push of a button.
Understand production line impact and
continuously optimize operations
based on condition monitoring of
cargo/container and needed parts.
During oceanic transfer, data is collected
during the entire journey then transmitted
once connectivity is re-established.

7. • End to end supply tracking, from sourced components to
assembly plants
• Connecting to the carriers
• Real-time tracking of cargo, where it is, what condition is it in,
who has possession
• Chain of custody – was it stolen, forgotten, or misplaced?
• Accurately predicting ETA / POD and condition of cargo
BLE Beacons + User/Device Management +
Discovery Service + Location Service + Chat Service
+ Asset Management + Chain of Custody + Machine
Learning
Logistics and Supply Chain

8. • Builds on existing infrastructure to improve service uptime
• Intelligent agents monitor network of sensors and devices
• Instrumentation and health monitoring of track switches
• When critical condition occurs, automatically find nearest
technician with right certifications and right tools and parts
• Automatic dispatch of maintenance crews, repairs, spare
parts
• Integrated with machine learning optimization
Switch Sensors + BLE Beacons + User/Device Management
+ Discovery Service + Location Service + Chat Service +
Asset Management + Chain of Custody + Job Scheduling +
Machine Learning
Rail Switch Health

9. Connect Everything
• Single solution delivers cloud, edge, and
devices
• Device agnostic : Supports any protocol
• Rapid edge device development: robust
SDKs/libraries
• Smart Agents make any device a peer
• Logic in the cloud allows solution recipes
• Operationally simple - deploy on AWS, Google
Cloud Engine, or on premise
• Can be embedded in hardware and systems
Warp IoT Capabilities
Field service and
asset tracking
with auto dispatch
Real-time fleet, driver,
and asset tracking
Mobile work
force
Predict rail switch health
and optimize maintenanceReal-time visualization
and analytics for
operational monitoring
and response

10. Applications for Actionable Insight and Automation
Our Technologies
• Continuous learning on
streaming data for
predictive and
prescriptive application
• IIoT services for
essential connectivity
and data collection
• Real-time actionable
insight visualization

11. Machine
Intelligence

12. By 2019 A $1000 Computer Will Have The Same Processing Power As The
Human Brain

13. Exponential Productivity Growth Due To Cognitive Machines
In the Second Machine Age, Brynjolfsson and
McAfee argue, “we are beginning to automate a
lot more cognitive tasks, a lot more of the control
systems that determine what to use that power
for. In many cases today artificially intelligent
machines can make better decisions than
humans.”
The Next Phase of the Digital
Economy
How we build, use, and live with our digital creations will define
our success as a civilization in the twenty-first century. Will our
new technologies lift us all up or leave more and more of us
behind? The Second Machine Age is the essential guide to
how and why that success will, or will not, be achieved.”
Garry Kasparov, thirteenth World Chess Champion

14. Machine Intelligence
• Machines will talk to each other
• Understand, learn, predict, adapt and
operate autonomously
• AI Could Replace 50M Professional Jobs
~ 40% of employment
Martin Ford in The Lights in the Tunnel:
Automation, Accelerating Technology and the
Economy of the Future
Software Will Eat The World

15. Three Examples

16. Applying Probabilistic Graphs To Time
Series
- Pricing Optimal Battery Warranty
- Commodities Trading
- Case Study Predictive &
Prescriptive
Windpark Maintenance

17. Use Time Series To Predict Nonlinear Battery Failure
Innovative machine learning to find hidden
patterns in time series
Predict capacity degradation without having
seen it in the wild
Combine Hidden Markov Model with
Hierarchical Mixture Models
Why
Reduce accruals by reducing
financial risk of warranties
How
Predict degradation over time
Find optimal policy for
warranties

18. CBM with
Prediction
Assumes failure at A:
lower asset life, increased
repair/replacement costs
Time in Operation
Advanced machine learning for optimized asset lifecycle
Failure
SpaceTime
Machine Learning
● Predict forward in time
without loss of confidence
● “See over the hill” to extend
asset life
● Produce better optimization
for lower costs and
increased productivity
A
Optimization
Zone
Probability of
Failure
Predict Failure
Optimize
Operations
Detect
Anomalies
Extended Asset Life
B

19. The failure prediction
gives you the probability
of failure into the future -
at any point in time
Battery Capacity Prediction Example
Look Into The Future From Any
Point In Time
Example Of Capacity Prediction
30% Rated
Capacity
Depletion
70% Capacity

20. The Holy Grail Of Time Series Beating The EMH
Innovative machine learning to find hidden
patterns in time series
Combine deep learning with hierarchical
dynamic Bayesian Models to predict price
Use stochastic optimization for money
management
 Learn optimal trading policy
EMH
Efficient Market Hypothesis
Market reflects all relevant
info
Systematics Trading
Make money beating the
EMH

21. Futures Contracts
Energy
BRENT CRUDE
WTI CRUDE
US NATURAL
GAS
UK NATURAL
GAS
ETHANOL
Brent CrudeMetals
GOLD
SILVER
COPPER
PALLADIUM
Crops
CORN
WHEAT
SOYBEANS
OIL
SOYBEANS
MEAL

22. Helping Largest Wind Farm Operator
Make Decisions Under Uncertainty
• Reduced crew hours: $2.3 million
savings/location
• Optimized crew schedule
• Improved crew safety and regulatory compliance
• Solution – Crew Optimization – 250 users
Success Story: Predictive Maintenance & Optimization
● Largest wind farm operator in the world; 19
states and 4 Canadian provinces
● 100+ sites; 10,000+ turbines; 1,000
teammates
“Using advanced analytics to
optimize resources and efficiency
allowing us to reclaim thousands of
lost hours of productivity”
General Manager
Largest Windfarm Operator Energy Resources

23. Optimization
Weather Forecasts
Crew Availability
Work Order List
Sensor
Data
Crew Schedule
Crew Route
Work Order
List
Traffic
Value of Activities
Performed
Risk and Cost
Managed
Crew Skills
Asset Failure
Model
Other Models
DATA INPUTS OUTPUTS/ACTION
Remaining
Useful Life
SpaceTime can perform optimization even when
inputs involve uncertainty, like weather or
traffic, and constantly changing inputs like the
probability of asset failure.
Global Optimization of Operations

24. Hub Optimization
Reinforcement learning to optimize
throughput over time subject to constraints
service level by product
available workforce
system capacity
System enables dynamical real-time
reassignment based on latest IoT updates
Why
Better throughput, improved
preventive maintenance,
reduce inventory
How
Queuing Theory

25. Reasoning Under Uncertainty Over Graphs
Speech Recognition Computer Vision
Assets As ApplicationsGames
Time in
Operation
Failur
e
A
Optimiza
tion
Zone
Probability
of Failure
Predict
Failure
Optimize
Operation
s
Detect
Anomalies
Extended Asset Life
B

26. Distributed Ledger

27. Distributed Ledgers – Thriving On Mutual Distrust
• Institutions –> reduce uncertainty
• Informal rules, formal rules, online institutions
• Create trust with technology alone
• Who? Public attestation -> portable ID
• Transparency? Digital token in supply chain
• Reneging? Enforce contract w/o 3rd party
• Unique innovation in CS and business
https://www.youtube.com/watch?v=r43LhSUUGTQ
Autonomous Systems For Exchanging
Value
The Business Wikipedia – Shared
Monopoly

28. The Sharing
Economy

29. The Sharing Economy – Access Economy
• Travel, car sharing, finance, staffing & streaming
• $15 billion in 2014 ~ 5% of the total spending
• $335 billion by 2025 ~ 50% of the total spending
• E.g. UberX produces ~ $6.8 billion social value/a
Using Big Data to Estimate Consumer Surplus -
The Case of Uber, Peter Cohen, Robert Hahn,
Jonathan Hall, Steven Levitt, and Robert Metcalfe
The Tragedy of the Commons Is the Distributed Ledger the
Solution?

30. Virtual Reality

31. Visualization Technologies
Virtual Reality Augmented Reality
Immerse the user in a virtual world
e.g. Oculus Rift – Facebook – Available now
Project virtual content over top of the real world
e.g. Microsoft HoloLens ~ 1 year out

32. Combining LIDAR Data With Ortho Photos - Orthofusion
OrthofusionLIDAR elevation data + an “ortho” =
(aerial) photo

33. Combining VR + Orthofusion + LIDAR = Photorealistic 3D

34. Virtual Reality For Vegetation Management

35. Virtual Augmented Reality
LIDAR Data + Virtual Reality = Virtual Augmented Reality
Reality captured
as a LIDAR
point cloud
Use VR technology to
- Render the point cloud
- Augment it with
- Simple highlights
- Asset Models
- Artificial Intelligence
- Veg Growth models
- What-if
- Risk
- etc
-
Real vegetation
LIDAR snapshot
Virtual Tree
overlaid

36. Vegetation Intelligence – Outer Loop Learning Model using
LIDAR
LIDAR
Data
Update
Growth
Model
Predict
Growth
Areas
(Machine
Learning)
LIDAR
Data
Optimize Trim Plan
Risk/Cost
(Produce
Schedule)
Trim
Operations
Other Data
- Climate
- Outages
- Environment

37. A Day In The Life For Next Gen Vegetation
Planning
Review Trim
Plan
View
Scheduled
Points of
Interest by
Analytics
Risk Score
Click on
browser to
teleport to
LIDAR View
of Area
Adjust plan
or add notes
for crews
Move to
next Point of
Interest
Share Notes
and “Tour” of
schedule
with Crew
Manager
Planners
(central)
Crew
Managers
(central or Remote /
third party)
Crew Manager
Views Notes and is
Taken on spatial
“tour” of trim
schedule

38. Next Gen Vegetation Analytics
Tree &
conductor
data
Growth Study
Data
Weather Data
Vegetation Analytics
LIDAR Data
Click Button on Web Page
For user “teleportation”
Multi-year 3D LIDAR data
used as input for growth
modeling and feature
detection
User can teleport to a location
and compare predicted growth
against exact LIDAR measurements.
Understand situation crew is entering
Trim
History
Outage Data

39. The Big Five IT Mega Trends - Summary
• Internet of Things
Assets Turn Into Applications
• Machine Intelligence
AI Could Replace 50 M Professional Jobs
• Distributed Ledgers
Block chain is becoming mainstream
• Sharing Economy
We don’t owe anything anymore
• Virtual and Augmented Reality
Remote experience merge visual & digital
world

40. The Team
Jason Lenderman
Alan McCord, PhD
Rob Jones, PhD
Zaid Tashman
Marcela Munoz
Recognition

41. Web: spacetimeinsight.com
@spacetimeinsght
linkedin.com/company/space-time-insight
facebook.com/spacetimeinsight
SpaceTime, SpaceTime Insight, and the Galaxy symbol are trademarks of Space Time Insight, Inc.
Thank You
@paul_hofmann
https://www.linkedin.com/in/hofmannpaul
Web: spacetimeinsight.com
@spacetimeinsight
linkedin.com/company/space-time-insight
facebook.com/spacetimeinsight
SpaceTime, SpaceTime Insight, and the Galaxy symbol are trademarks of Space Time Insight, Inc.