AI 101 for km washington november 2018 km world workshop

AI 101 for KM
KM World Workshop
Gordon Vala-Webb
November 2018
Washington, D.C.
Gordon@BuildingSmarterOrganizations.com
@BuildSmarterOrg www.BuildingSmarterOrganizations.com

What I think KM should be . . .
• Predictions & Bets
• Flows of info, ideas
decisions
• Designing / Nurturing
@BuildSmarterOrg www.BuildingSmarterOrganizations.com 2

Why does AI
matter?
Workforce
Healthcare
Fourth industrial
revolution
@BuildSmarterorg www.BuildingSmarterOrganizations.com 3
World Economic Forum
Mapping Global Transformation
https://www.weforum.org/about/transformation-maps
3

Annual value estimated $3.5 – $6.8 trillion
NOTES FROM THE AI FRONTIER INSIGHTS FROM HUNDREDS OF USE CASES McKinsey Global Institute
https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the
%20ai%20frontier%20applications%20and%20value%20of%20deep%20learning/mgi_notes-from-ai-
frontier_discussion-paper.ashx

Is AI magic?
@BuildSmarterorg www.BuildingSmarterOrganizations.com
“Any sufficiently advanced technology is indistinguishable from magic.”
Arthur C. Clarke
5

Is it a flying car?
By Eslivb [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-
sa/4.0)] , from Wikimedia Commons
By Thesupermat - Own work, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=23885520
Or is it just a car?

Where is Machine Learning on the Gartner Hype Cycle?
https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
?
?
?

What is Machine Learning?
Creating algorithms
that can recognize patterns in large,
evolving data sets, and
drawing conclusions from past
experience by using that data
Machine Learning and Predictive Systems; World Economic Forum
https://toplink.weforum.org/knowledge/insight/a1Gb0000000pTDREA2/explore/dimension/a1Gb00000017L8jEAE/summary

https://youtu.be/scQ3xzWprss?t=4

Components of custom-built AI solution
•Technologies
•Tools /methodologies
•Business knowledge and experience
•AI expertise
•Focus on specific business opportunity

Creating algorithms

IF this THEN that ELSE: Employee or Contactor?
Will the service be provided
from home?
Or will the services be provided in a determined
place (offices or facilities of the hiring company)?
Will the service be subject to a work shift or schedule?
If yes – is employee If yes – is employee
If no - is independent contractor If no:
Will the person be using uniform of the hiring
company?
If yes – is employee
If no:
Will the person be using the tools of the hiring
company?
If yes – is employee

Employee vs. Independent Contractor - Algorithm
https://autom.io/
Employee
Contractor
Contractor
Employee

Creating algorithms

“Recognize patterns in data sets”
Principal Component Analysis (PCA)/SVD
Least Squares and Polynomial Fitting
Constrained Linear Regression
K-Means Clustering
https://dzone.com/articles/ten-machine-learning-algorithms-you-should-know-to

Recognize patterns – e.g. Blue J Legal

Training systems:
False positives
and negatives
A face is a face – isn’t it?
@BuildSmarterorg
www.BuildingSmarterOrganizations.com
17@BuildSmarterorg www.BuildingSmarterOrganizations.com

Is a roc a rock?
By Charles Maurice Detmold (1883-1908) - http://boards.collectors-
society.com/ubbthreads.php?ubb=showflat&Number=4741639, Public Domain,

What about rock?

Supervised Unsupervised
https://www.boozallen.com/assets/boozallen_site/sig/pdf/infographi
c/how-do-machines-learn.pdf
Machine Learning

https://www.boozallen.com/assets/boozallen_site/sig/pdf/infographi
c/how-do-machines-learn.pdf
Machine Learning

Suitable for different problems
Machine Learning

Neural networks – framework for machine
learning algorithms to work together

Caveat: Correlation does not imply causation
http://tylervigen.com/spurious-correlations
24

Caveat:
Cherry picking
By Fjalnes - Own work, CC BY-SA 4.0,
Sven Ove Hansson
Science denial as a form of
pseudoscience
Studies in History and
Philosophy of Science. 63,
2017, pp 39–47.

Caveat – is AI fair?
•US sentencing guidelines – COMPASS – racially biased
•Siri giving inadequate instructions to women’s health
services
•Natural language models that associate ‘woman’ with
‘receptionist’
•US law enforcement crime prediction - PredPol –
racially biased
•Google image search for “CEO” – gender biased

Some KM use cases
• Customized / personalized search
• K (customer) support “bots”
• Text analytics and NLP
• Life sciences – diagnoses and finding cures
• Cybersecurity – attacks and effective responses

Customized / personalized search – e.g. Attivio
“A system . . . begins to
understand how Bob is great
at handling queries about a
problem with mobile while
Mary excels at database
issues . . . a support ticket
can be directed to the right
person in real time based . .
. on an intake call or email.”
28
https://www.attivio.com/blog/post/solving-customer-issues-
intelligent-answers-and-insights

10 Best Chatbots [services] of 2018 Consumers Advocate https://www.consumersadvocate.org/chatbots/a/best-chatbots
Knowledge support – e.g. Dialogflowhttps://cloud.google.com/blog/products/gcp/dialogflow-enterprise-edition-is-now-generally-available
https://medium.com/swlh/how-to-build-a-chatbot-with-dialog-
flow-chapter-1-introduction-ab880c3428b5
https://hackernoon.com/how-to-train-your-robot-ai-for-
everyone-69b96ad943e5

Build a bot
https://landbot.io/
More advanced!
Language Understanding (LUIS)
https://www.luis.ai/welcome
• Domain
• Intents
• Utterances
• Entities

Text analytics
and NLP
– e.g. Diligen

Data is
everything!
• Structured
• Time series
• Text
• Audio
• Video
• Image
https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artifi
cial%20intelligence/notes%20from%20the%20ai%20frontier%20applicatio
ns%20and%20value%20of%20deep%20learning/mgi_notes-from-ai-
NOTES FROM THE AI FRONTIER
INSIGHTS FROM HUNDREDS OF USE
CASES McKinsey Global Institute
32

Some key challenges
•Massive data sets
• Labelling training data
• Large and comprehensive enough
• Organizational change
•Explaining results
•Focused – not generalizable learning
•Potential bias
NOTES FROM THE AI FRONTIER INSIGHTS FROM HUNDREDS OF USE CASES McKinsey Global Institute
https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20ai%20frontier%20applications%20and%20value%20of%20deep%20learning/mgi_notes-from-ai-

Augmented
Intelligence
3
4
• Machines and humans
• Working together
• Limiting each other
@BuildSmarterorg
www.BuildingSmarterOrganizations.com

Why smarter organizations?
Old vs. VUCA-Digital world

VUCA-Digital world
cc: frogthroat - https://www.flickr.com/photos/22980078@N04
“The Specter of an Accidental China-U.S. War”
“Sears tanked because the company
failed to shift to digital.”
36

Our organizations optimized for “old” world
Old world
Grow big
Reliably repetitive
Control
VUCA – Digital world
Grow adaptable
Radically responsive
Predict

Volatility = Obsolescence squeeze
https://www.thoughtworks.com/insights/blog/why-lean-enterprise-transformation-hard

Uncertainty / Ambiguity / Complexity =
What is happening?
Classifying Experimental Designs
Source: https://www.socialresearchmethods.net/kb/expclass.php

“Try or try not. There is only do not with no try."
Gordon Vala-Webb – Building Smarter Organizations 2016 Slide 40
“Try or try not.
There is only
do not with no
try."
40

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