Mika Karaila - Ai chatbots industry cases

AI cases in Industry,
Binding real-time data
and AI/chatbots together
Mika Karaila
Research Director
Valmet

Agenda
Ÿ Principles
Ÿ Tools & languages
Ÿ Use cases
Ÿ Vision for future
21 February, 2017 © Valmet | Mika Karaila / AI & Chatbots in Industry2

Principles
Ÿ Infra-structure and systems providing information:
– Big data: information stored
– Machine learning: creating new value from the existing information
Ÿ Domain knowledge:
– Failure -> cause understanding
Ÿ Dashboards:
– Basic visualization for the key performance indicators (KPI)
– Alarm / failure analysis
Mathematics, programming & knowledge combination

The data science: Hierarchy of needs
https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007

Machine learning & predictive maintenance
Source: Roger Attic: https://www.linkedin.com/pulse/intelligent-things-its-all-machine-learning-roger-attick

Autonomous AI
Assistive AI
Example: drilling engineer
Ÿ Observing the world: In this case, AI can literally look around at years of
operational and geological data related to a company’s wells and drilling
rights, as well as public data such as geophysical data related to the land
Ÿ Reasoning about it to move toward some goal or goals: “Reasoning”
describes how a question of whether to drill an exploratory well on one of
these leases is answered. Assistive AI allows a human to ask literal questions
of a machine in an attempt to derive answers to their problem.
Ÿ Making justified and explainable recommendations to users: AI algorithms
then create models that provide answers to those questions and can help the
drilling engineer recommend whether to drill an exploratory well based on 10
years' worth of data. A human can not in any reasonable time evaluate and
analyze that much data, but AI models provide those recommendations to the
drilling engineer.
https://www.forbes.com/sites/forbestechcouncil/2017/12/21/assistive-ai-not-autonomous-ai-is-the-path-to-improved-operational-efficiencies/#8cfacc41efb1
7 May 2018 © Valmet | Mika Karaila / Future Avenues6

Assistive AI, Not Autonomous AI
=> The Path To Improved Operational Efficiencies
Ÿ Supporting the user to explore hypotheticals: Based on the results
of this exploratory well, the machine can help the engineer look at
what returns a production well might provide on the investment.
Ÿ Recording decisions and actions taken: The AI algorithms record
the decisions and actions taken by the drilling engineer and then the
engineer either accepts the recommendation made by the models or
rejects them and makes his or her own decision.
Ÿ Measuring impacts and outcomes to learn and improve over time:
AI’s greatest value is its learning attribute. It measures the outcome
and KPIs based on the decisions and actions taken by the drilling
engineer and uses that to learn and improve recommendations for
better impact on the KPIs. The reasoning process builds up a bank of
knowledge -- an answer (solution) to a question (problem).

Assistive AI: How?
What is needed?
Ÿ Models, Not Static IT Applications, Offer Improvements In
Operational Efficiencies
Ÿ Assistive AI Helps Humans Demonstrate Business Value More
Efficiently

Human-like AI

Status of AI: Autonomous Vehicles, AR & VR

AI investments – US & China leading

Selection of the fittest
Ÿ Best tools are in many cases expensive
Ÿ Good for R&D research & finding the solution / model
Ÿ Just to name some:
– SPSS
– Matlab
Ÿ Languages:
– Python (batteries included)
– R (statistical operations)
Selection is in many cases based on available support for the domain problem.
Algorithms, data and similarity to existing solutions in domain.

Anomaly detection
Adaptive model

Paper sheet defects – Cluster modeler
Kohonen SOM: Self Organising Map

Paper sheet defects – Cluster modeler
T-SNE: T-distributed Stochastic Neighbor Embedding

Valmet’s predictive roll-cover analysis
• Roll cover lifetime modelled using big
data and predicting future behaviour
• Operational parameters can be
optimized to minimize roll-cover costs
Critical part lifetime (days)
0 5 10 15 20 25 30 35
How many running days
remaining?
Can lifetime be extended?
Lifetime with alternative
component?
Critical Vibration Level
10 days
5 days 5 days

Path analysis of paper machine events
Event A, Event B, Event C Event A, Event B, Event C
First time occurrence Second time occurrence
Top 200 “three-event-paths” leading
to unplanned shutdown
And 200
predicting no-shutdown (normal operation)
Problem fingerprint
Using tree-event paths
(sequence of three events)
9:35 AM
Unplanned shut
down
5:35 AM
Guide roll 4 torque
from low to normal
5:35 AM
Guide roll 5 torque
from low to normal
5:40 AM
Nip 2 edge temp DS
from normal to high
”Triplet”
reoccurance
5 times
”Triplet”
reoccurance
15 times
~6:15 AM ~7:00 AM
3 h 20 min

Node-red Telegram chatbot
Example of flow based chatbot

Telegram at mobile phone
Screenshot -> live demo!

Alexa – Valmet expert
Bind Valmet DNA real-time values to Alexa

Valmet HoloMill – XR application
Own skins for Avatars: Valmet / customer and own for AÌ
7 May 2018 © Valmet | Mika Karaila / Collaboration with XR24

Avatar movements & actions
Multiple users – all can see each others talking and moving
7 May 2018 © Valmet | Mika Karaila / Collaboration with XR25

Future
Ÿ Technology is maturing
Ÿ Valmet Industrial Internet provides data & models
Ÿ Dashboards for operators
Ÿ Machine Learning on Cloud & Edge
• Real AI in industry: AI Assistant
Thank you!
Questions & discussion
27 21 February, 2017 © Valmet | Mika Karaila / AI & Chatbots in Industry

Mika Karaila - Ai chatbots industry cases

Mika Karaila - Ai chatbots industry cases

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