In our digital world, the key to gaining a competitive advantage is learning to see data as currency. We are producing more data than ever, roughly 2.5 quintillion bytes of data every day, and that number is only expected to grow. While all this data may seem to be merely the exhaust or remnants of our digital trail, data scientists at Intel® and other tech companies have been turning this data into much more through advanced data science, including the adoption of machine learning and artificial intelligence.
View to learn:
• How AI is possible with IT modernization and Cloud
• Why enterprises are going data-centric
• Best Business Use Cases for AI
• AI’s biggest advantages and challenges
4. THE EFFECT OF
POP CULTURE
Pop culture sci-fi has given us AI
characters—this may affect or color
out attitudes toward AI. How should
we view AI today? What is positive
and possible with AI?
5. WE USE AI
AND MACHINE
LEARNING
EVERYDAY
Common examples
• GPS applications that approximate
time of travel or the quickest route
based on live traffic data
• Online shopping which offers you a
list of recommendations based on
previous purchases and preferences
• Learning thermostat which uses
behavioral algorithms to customize your
home’s temperature and energy usage
6. The key to gaining a competitive
business advantage is to see
data as currency
WHAT
DID THESE
COMPANIES
FIGURE OUT?
7. .
DATA NEVER SLEEPS
Roughly 2.5 quintillion bytes of
data are produced every day
Amazon makes $250,000
in sales every minute
Twitter users send 465,000
tweets every minute
Over 15 million texts are sent
every minute
8. Ask Yourself:
DOES YOUR ORGANIZATION USE
DATA SCIENCE FOR A COMPETITIVE
ADVANTAGE?
a) Yes, and we are investing more
b) Yes, but we are just beginning
c) Maybe, I see advantages for others, but not yet for my business
d) No, data science does not apply to my business
9. .
AI VS MACHINE LEARNING
Core Analytics:
Descriptive, diagnostic, predictive and
prescriptive analytics that describe what
happened, why it happened, what will happen
next, and what should be done about it.
Machine Learning and
Cognitive Computing:
Use of computational algorithms to
learn from the data we feed it
without programming.
Deep Learning:
Set of convolutional neural networks
that allow for analytics in multiple
granular stages with a higher and
higher degree of accuracy of insight.
Artificial Intelligence:
The outsourcing of human cognition to
the thinking machine that operates at
internet speed. Examples include
autonomous driving.
11. • Generate profits or productivity
• Gain market insight
• Personalize services or healthcare
• Identify new opportunities for your
business
YOU CAN
HARNESS THE
POWER OF
DATA TO:
12. .
BIG INVESTMENTS COME
WITH BIG EXPECTATIONS
80% businesses have some form
of AI in production today.
30% plan to expand AI
investments in the next 2-3 years.
62% plan to hire a Chief AI
Officer in the future.
Expected rates of ROI
per dollar invested:
$1.23 in 3years
$1.99 in 5 years
$2.87 in 10 years
14. Ask Yourself:
WHERE IS YOUR BUSINESS ON THE
JOURNEY TO DATA-CENTRICITY ?
a) Already data-centric and plan to invest more
b) Initiated a data-centric strategy
c) Starting to consider the possibilities
d) Not yet begun
15. ENERGY SECTOR: AI could meet the
global demand for low-carbon, green
electricity by solving the problem of
renewable energy’s inconsistency.
LIFE SCIENCES: AI could provide a better
understanding of an individual patient’s
needs, resulting in personalized
immunotherapy or the development of
pharmaceuticals to treat specific ailments
EXAMPLES OF
BUSINESS USE
CASES
16. THE
TALENT GAP
AI’S BIGGEST
CHALLENGE
By 2020, there will be more than
1.4 million computing-related jobs
in the US alone.
BUT, only about 30% of those jobs
will be filled by US graduates.
17. THE FUTURE OF AI
Increase in Citizen
Data Scientists
AI benefits for
diverse populations
Expansion of
core infrastructure