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

AI 101 for km washington november 2018 km world workshop

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a AI 101 for km washington november 2018 km world workshop

Semelhante a AI 101 for km washington november 2018 km world workshop (20)

Mais de Gordon Vala-Webb

Mais de Gordon Vala-Webb (11)

Último

Último (20)

AI 101 for km washington november 2018 km world workshop