1.
Artificial Intelligence For Business
Learning Decision Makers
paul@paulbarter.com www.paulbarter.com @paulbarter.com

2.
My area of focus is how technology will enable business
and societal change 3 years in the future.

3.
50’s & 60’s
60’s & 70’s
70’s & 80’s
90’s & 2000’s
2000’s
2010’s
Beginning now
The Innovation sweet spot for
the next generation will be
artificial intelligence and
machine learning
Technology Waves

4.
General Purpose Technologies
? ?

5.
What Exactly Is Artificial Intelligence?
‘A good way to think about artificial intelligence is as computer
software that’s indistinguishable from human intelligence’.
• Brendon Frey, Co-founder of the Vector Institute
Or said another way;
Artificial intelligence is the study of how to make computers do things
that people are better at … Or would be better at if:
They had access to a World Wide Web-sized amount of data and …..
Not make any mistakes !

6.
Algorithm
•Arguably the single most important concept in
our world!
•A methodical set of steps that can be used to
make calculations
•Not a particular calculation but the method
followed when making the calculation!

7.
Simple Mathematical Algorithms
• Calculating the average of two numbers
• Add the two numbers together
• Divide the numbers by two
• Works for any two numbers

8.
Artificial General Intelligence?

9.
What Is Machine Learning? ( Narrow AI)
Building systems that enable a computer system to
learn some intelligent behavior by training it on
massive amounts of data.
Said another way; Replacing systems where
algorithm are programed in advance with systems
where algorithms learn from data.

10.
90% of ML Algorithms Do One Thing
Takes an input A and delivers an output B
• Picture Is it you?
• Loan application Should you get it?
• Advertisement Will you click?
• Audio Transcript
• English French
• Image What it is
• Manufactured product Is it acceptable

11.
The Five Major Techniques of Machine Learning 2018
Adapted from Pedro Domingos; The Master Algorithm

12.
Until recently Connectionists (neural
networks or Deep Learning) were all
but shunned by the AI research
community. They had been around
since the earliest days of AI, and had
produced very little in the way of
“intelligence.” The problem was even
the most basic neural networks were
very computationally intensive, it just
wasn’t a practical approach. Still, a
small research group led by Geoffrey
Hinton at the University of Toronto
kept at it, finally parallelizing the
algorithms for supercomputers to run
and proving the concept. With the
deployment of GPUs (and now TPU’s)
for AI the promise was realized

13.
Sample complex machine learning task 2016 $240,000.00
Similar task 2018 $12.75

14.
Three Major Types of Machine Learning 2019
• Supervised
• Data labelled
• Goal – “Classify” new data – most of what we’ll discuss here
• Unsupervised Learning
• Data without labels
• Goal – Find patterns
• Reinforcement Learning
• No data no labels
• Goal – classify a reward

15.
What Can Machine Learning Do Now?
Image recognition
Loan approvals
Resume’ screening
Targeted online ads
Speech recognition
Language translation
Self driving cars
Preventative maintenance
Manufacturing quality control
Medical diagnosis
Combinations of the above
………….
More things literally every day

16.
AI will boost global GDP by nearly $16 trillion by 2030

17.
And / Or

18.
“If [self-driving cars] make the world safer, it’s going to be a very good thing,
but it won’t be a good thing for auto insurers.” – Warren Buffet

19.
One More (Really Important)
Thing

20.
David Hume’s Problem of Induction
• “How can we generalize from what we’ve
seen to what we haven’t?”
• One of the most important questions in
philosophy
• By extension one of the most important
questions in machine learning.
• The problem of overfitting or hallucinating
patterns that aren’t really there.
• Also what if ‘what we’ve seen’ is not
accurate in the first place?

21.
When humans
(data) trains
the machine …

22.
Innovation
Opportunities!

23.
Today Every Company is a Tech Company Anything a typical person can do in less than 1 second
of thinking can probably now or soon be automated
Every company has AI opportunities Brainstorm the above
Be Data Savvy – It all starts with Data Train. Experiment. Measure success. Commercialize.
Build a centralized data warehouse – 50 different
databases makes AI difficult (impossible?)
Create a centralized AI team matrixed to BU’s to help
execute?
How Should Organizations (people) Prepare?

24.
Artificial Intelligence For Business
Learning Decision Makers
paul@paulbarter.com www.paulbarter.com @paulbarter.com