Artificial Intelligence is a burgeoning technology that is just beginning to transform the world and the way we live & work. This is an introductory overview and presentation on Artificial Intelligence, how it's being leveraged in business today, and an intro to its overall potential.

1. Intro to
Artificial
Intelligence
- AI Primer & Overview
Geoff Works

2. A R T I F I C I A L
I N T E L L I G E N C E
If someone were to ask you what
AI is – what would you say?
Numerous Definitions:
From Quartz:
“Artificial intelligence is a software or a computer
program with a mechanism to learn. It then uses that
knowledge to make a decision in a new situation, as
humans do. The researchers building this software try to
write code that can read images, text, video, or audio,
and learn something from it. Once a machine has
learned, that knowledge can be put to use elsewhere.
In other words:
AI is the ability of machines to use algorithms to learn
from a variety of different inputs (data), apply that data
to a given scenario, and make a decision based on what it
has learned. Just like a human would.
WHAT EXACTLY IS

3. Key Moments in the Modern
History of Artificial Intelligence
- 1942 - Isaac Asimov publishes his “3 Laws of Robotics” as part of his
series of science-fiction novels.
- 1950 - Alan Turing, considered the “father” of modern computer
science, proposes ”The Turing Test.”
• The Turing Test is a test of a machine's ability to exhibit
intelligent behavior equivalent to, or indistinguishable from,
that of a human.
- 1956 – The Dartmouth Conference, organized by John McCarthy, is
convened at Dartmouth University where the term “Artificial
Intelligence” was first used.
• Dartmouth Conference was brought together by John McCarthy,
Marvin Minsky,
• Mission Statement: “Every aspect of learning or other feature of
intelligence can in principle be so precisely described that a
machine can be made to simulate it.”
- 1996 – ”Deep Blue” (IBM Supercomputer) defeats world chess
champion for first time.
- 2009 – Google builds first autonomous car
- 2009 – Present – Widespread adoption & implementation of AI
throughout the business world. Acceleration of AI capabilities.
- 2011-2014 – Siri, Google Now, and Cortana use Natural Language to
answer questions, make recommendations, and perform actions.
- 2018 – Google Duplex, AI assistant used to book appointments comes
closest to passing Turing test for first time.

4. Why is AI adoption gaining traction & accelerating now?
• Overwhelming prevalence of
available data (especially with the
advent of the Internet & Cloud-
based applications/services
• Increasing computational
capabilities
• Growing progress in available
algorithms & theories developed by
researchers
• Increasing support from business
world

5. Components of
Artificial Intelligence
ARTIFICIAL INTELLIGENCE:
The ability of machines to use algorithms to learn from data, and
use what has been learned to make decisions like a human would.
MACHINE LEARNING:
One of the primary approaches to artificial intelligence, where machines
have the ability to learn without being explicitly programmed.
DEEP LEARNING:
A subset of machine learning that has networks which are
capable of learning unsupervised from data that is
unstructured or unlabeled.
Key Takeaway: Machine Learning & Deep Learning are what make AI possible.

6. What is Machine Learning?
• “The practice of using algorithms to parse data, learn from it, and then
make a determination or prediction about something in the real world.”
(Nvidia)
• “Machine learning is the science of getting computers to act without
being explicitly programmed.” – Stanford
• “Machine learning is based on algorithms that can learn from data
without relying on rules-based programming.”- McKinsey & Co.
• “Machine learning algorithms can figure out how to perform important
tasks by generalizing from examples.” – University of Washington
• “The field of Machine Learning seeks to answer the question “How can
we build computer systems that automatically improve with experience,
and what are the fundamental laws that govern all learning processes?”
– Carnegie Mellon University

7. Types of Machine Learning
Algorithms use data that has already
been labeled or organized. Human
Input required to be able to provide
feedback.
SUPERVISED
Implements algorithms in which data is not
labeled or organized ahead of time.
Relationships must be discovered without
human intervention.
UNSUPERVISED
Algorithms are able to learn from
experience. Reinforcement algorithms
usually learn optimal actions through trial
and error.
REINFORCEMENT
ML

8. • Currently being used in speech recognition, NLP, Computer Vision,
autonomous driving vehicles.
• One of the most powerful and fastest
growing applications of AI.
• Being used to solve problems which
were previously considered too
complex and/or involved huge
amounts of data.
• Occurs through the use of neural networks,
which are layered to recognize complex
relationships & patterns in data.
• Deep Learning requires HUGE DATASETS and powerful
computational power in order to work
DEEP
LEARNING

10. What capabilities form the basis of AI today?
Computer Vision:
Self-Driving Cars (Tesla) or
Facial Recognition (Clear)
SEE
Speech Recognition:
Alexa, Siri, Google Home
HEAR
Natural Language Processing:
Chatbots, Sentiment analysis, etc..
COMPREHEND

11. How/Where is AI being leveraged today?
AI is beginning to be leveraged across almost every industry at
this point.. Here are some awesome & eye-opening examples:
Contact Centers
• Online Self Service
• Chatbots
• Business Insights:
• Use NLP to better understand customer topics,
determine customer intent, and improve
customer handling & business insights.
• ”Virtual” Agents:
• Offload mundane Tier 1 calls, handle common
customer requests automatically, preprocess
calls before human interaction, increase off-
hour support options.
• Automated Agent Assistance:
• AI listens to conversation and helps agent in
real time during the course of the interaction
in the form of knowledge based articles,
documents, or other suggestions.
Finance
• Implementing automation, chatbots, adaptive
intelligence, algorithmic trading and machine
learning into financial processes. Robo-
advisors, or automated portfolio managers, will
begin to use AI and algorithms to scan data in
the markets and predict the best stock or
portfolio based on investor preferences.
Healthcare
• AI-enabled virtual assistants have the potential
to reduce unnecessary hospital visits and giving
nurses, doctors, ER’s incredible time savings;
workflow assistants are helping doctors free up
their schedules; and thanks to Machine/Deep
Learning, pharmaceutical companies are
researching lifesaving medicines in a fraction of
the time and cost it traditionally takes Chatbots

12. How/Where is AI being leveraged today?
Marketing
• Leveraging Insight tools (Google Trends and/or
Facebook Audience Insights) – to capture high-
quality, real-time info that can anticipate different
trends, such as where Customers are spending time,
requesting more info, etc..
• Better competitive info: Insights into revenue
streams, more successful products/services,
personnel, performance on Social media, etc..
• Identification of more effective channels (social
media, websites, SEO, etc..) for reaching particular
markets or influencing specific customer sets.
• Better targeted & more relevant content creation
E-Commerce
• Alexa (as one example from Amazon)
• Improved Product recommendations
• Relationship building & user-experience
personalization.
Social Media
• Tremendous R&D and growth in this realm.
• Chatbots
• Algorithmic Newsfeeds
• Photo-tagging suggestions
• Ad-targeting
• Image recognition capability
• Account monitoring (weeding out bad actors,
terrorists, etc..)
Travel
• AI-powered chatbots can now provide human-
like interaction with customers to enable faster
response times, better booking prices and even
travel recommendations.
• Self-Driving cars
• Smart maps
• Virtual Travel Assistants

13. Where are we heading??

14. Where is AI
taking us?
AI will be one of the primary drivers
behind the Fourth Industrial Revolution.
Just as Electricity was one of the primary
drivers (if not, the main driver) behind
the Second Industrial – so will AI be the
equivalent in the Fourth Industrial
Revolution. According to Andrew Ng,
one of the foremost experts in Deep
Learning, “Artificial Intelligence is the
new electricity.” As electricity became
embedded within society it
fundamentally altered the way people
lived. The same will be true as AI
becomes more embedded and
inseparable from our everyday lives.

15. THANKS!!!
Please let me know any questions/comments
geoff@geoffworks.com

16. Common AI Terms and Concepts
Algorithms:
• The step-by-step method that a
computer uses to complete each task.
Steps are put together as a series of
mathematical equations.
Cognitive Computing:
• Another name for Artificial Intelligence,
typically used by IBM
Computer Vision:
• The technology that enables computers
to have sight and recognize what they
are seeing.
Deep Learning:
• See previous slide
Expert System:
• A computer system that models the
decision-making ability of a human
expert.
Natural Language Generation (NLG):
• Ability of software to turn Structured Data
into understandable written text, similar to
that of a human – but at a much faster
pace. NLG is a form of NLP.
Natural Language Processing (NLP):
• Ability of computers to recognize and
understand human language as it is spoken,
and to take action based on spoken
instructions. (See: Siri, Alexa..)
Speech Recognition:
• The technology that enables computers to
recognize and translate spoken language
into text.
Turing Test:
• The classic AI test, developed my
mathematician Alan Turing, to ascertain
whether a computer has the ability to
“think” like a human. The test consists of a
person being able to determine if they are
talking to a computer or another person.