The intersection of AI and IoT presents new opportunities to create value for your business, capturing new insights from the vast amounts of IoT data available, which results in stronger customer relationships and new efficiencies. In this session, we discuss the future of operations and product development when AI and IoT meet to make autonomous decisions faster and better.

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AIoT: AI Meets IoT
I O T 2 0 4
Dávid Lakatos
Chief Product Officer
Formlabs
Sarah Cooper
GM
AWS IoT Analytics & Apps
James Gosling
Distinguished Engineer
AWS

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Related breakouts
IOT358-R1- Operationalizing Analysis
With IoT Analytics
THURSDAY 3:15 PM – 4:15 PM
MGM, Level 1, Grand Ballroom 113IOT218-L - Leadership Session: AWS IoT
WEDNESDAY 3:15 PM – 4:15 PM
Venetian, Level 5, Palazzo O
IOT219 - IoT Analytics Customer Showcase
TUESDAY 2:30 PM – 3:30 PM
Mirage, Montego D

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/topic_coverage
Machines monitoring machines
Machines learning
Machines collaborating
Machines manufacturing machines for people

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Digital Transformation means trillions of connected
devices making data, decisions, and giving directions

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How big is a trillion?
1 million
Seconds
1 billion
Seconds
1 trillion
Seconds
Last week
St Patrick’s Day, 1987
Cro-Magnon man
paints cave

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Machines don’t sleep or blink
Machines stream billions of data points
Monitoring a machine requires another machine

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Machines that look out for other machines
Machines must operate together in multi-vendor, low-trust ecosystems. Monitoring provides both system-
wide state information and decision feedback loop

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Machines monitoring machines with AWS IoT
On-machine events Decision verification Policing
On-site data collection Complex event detection Policy enforcement
When machines get their own credit cards, how will
they choose to monitor themselves?

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Machines today spend more time watching each other
Industrial systems are increasingly deploying ML-driven cameras to replace
and augment digital control system monitoring

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What do we mean by learning?
Machine learning is when computers create models of system behavior based on data from
historical or current systems. The denser the information in the data, the better the model

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Examples of learning
Classification
Prediction & forecasting
Route optimization
Anomaly detection
Object identification
Language processing

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Machine learning on machine data is Hhard
Lack of labels, situational context & relationship: device
data is simple. Unlike application data it doesn’t carry
information needed to interpret it. Context must be built
elsewhere and added to the raw data.
Learning is all about the data.
Machine data is a hot mess because the
physical world is messy, dirty and often
unpredictable.
Devices must report data in simple formats to
be flexible for function abstraction.
T1 : measurement time
T2 : server time
V : value (ex: 5)
URL : source unique ID
Crappy data quality & integrity: many devices have limited
local resources like memory, signal processing, connection
management or cheap sensor quality.
High volumes of data carrying sparse information: terabytes
of streaming raw operations data may contain only a few
kilobytes relevant to any one process or analysis.
Distinguishing deviation from variation: sources of variability
abound in the physical world, especially in operations that
involve us humans. Detecting meaningful deviation requires
a broad analytical toolbox.
High data dimensionality: data dimension refer to the
number of independent parameters in an analysis.
Techniques like ML are very compute expensive when
crunching high dimensional analysis.

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AWS IoT Analytics brings together data preparation for machine data, optimized
performant storage, data visualization, machine learning, bring your own analysis,
scheduling and automation for continuous analysis.
Aggregate across multiple
machine data sources,
structure and collate data by
time window
Separate signal from noise,
clean, enrich, convert and
prepare IoT data
Store and query processed
data, analyze time series,
archive & reuse raw data
HISTORICAL
Amazon S3
STREAMING
Amazon Kinesis
PUB/SUB
AWS IoT
Train machine learning with
Amazon Sagemaker,
containerize custom analysis,
explore results in Amazon
QuickSight
Predict Failures
Detect Anomalies
Forecast Output
Machine learning on machine data with AWS IoT Analytics
Collect & collate Clean & contextualize Optimize structure Analyze
Automate

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Central intelligence or
distributed edge:
Two models of learning

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What machines don’t
learn well… yet
Machines don’t have mental
models the way we do
Training bias
Positive reinforcement learning
Machines can appear
shockingly brilliant and
extremely stupid at the
same time.

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Completing complex tasks takes intelligent coordination
Machines are
specialists

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IoT technology evolution
2007 Connected device
2012 Connected product
2016 Connected product line
2019 Connected process
2025 Connected ecosystem
Value
Complexity
The machine network effect
The larger and more diverse the
network of devices, the greater the
additive value of the network
Additive value creation

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Machine collaboration
Microgrid demo
Supported By:
Bob Edmiston
AWS IoT User Researcher

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Machine collaboration in power arbitrage
Autonomous power supply optimization

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Machines democratizing
manufacturing for us all…
using AWS IoT

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Dávid Lakatos
Chief Product Officer
Formlabs

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Dávid Lakatos
@dogichow
How to help make
anyone make one
of anything?

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29. “We see the computers
everywhere
but in the productivity statistics.”
—Robert Solow

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32. Initial benefits from electrification
● More reliable speed
● Marginally lower energy costs

33. Initial benefits from electrification
● More reliable speed
● Marginally lower energy costs

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37. Initial benefits from electrification
● More reliable speed
● Marginally lower energy costs

38. Benefits from electrification after restructuring
● More reliable speed
● Marginally lower energy costs
● Safer, brighter factories
● Efficient single-floor factories designed around
flow of materials and labor rather than energy
● Flexible reconfiguration and improved
reliability

39. Electrification didn’t make a
difference until managers
rebuilt factories around it.

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Custom Earbuds
Benefits of a Perfect Fit
Comfortable
Noise canceling
Safer
Great for active users

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One platform from idea to production

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Visit Formlabs additive manufacturing demo at the Builders Fair
Quality analysis
AWS IoT Analytics
AR visualization
engine
Amazon Sumerian
3D printed dice
Amazon Polly
Digital assistant
Continuous ML identifying
quality faults & assessing
impact on dice roll
probability
formlabs
Form 2
Aria Level 1 QUAD, Area Q1

54. Thank you!
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