Artificial Intelligence is popularised in fiction films such as “The Terminator” and “AI: Artificial Intelligence”. Now, artificial intelligence is becoming closer to being a part of our daily lives through the use of technologies like virtual assistants such as Cortana, smart homes, and automated customer service.
Now, we are running the Red Queen’s race not just to win, but to survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and futurist ideas are developing into reality at accelerated rates.
How can you help your your company to evolve, adapt and succeed using Artificial Intelligence to stay at the forefront of the competition, and win the Red Queen’s Race? What are the potential issues, complications and benefits that artificial intelligence could bring to us and our organisations?
In this keynote, Jen Stirrup explains the quick wins to win the Red Queen’s Race, using demos from Microsoft technologies such as AutoML to help you and your organisation win the Red Queen’s race.
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Artificial Intelligence: Winning the Red Queen’s Race Keynote at ESPC with Jen Stirrup
2. Artificial Intelligence: Winning the
Red Queen’s Race
Jen Stirrup
Data Whisperer, Data Relish Ltd, UK
Microsoft MVP, Regional Director
3. JenStirrup
• Boutique
Consultancy Owner
of Data Relish
• Postgraduate
degrees in Artificial
Intelligence and
Cognitive Science
• Twenty year career
in industry
• Author
JenStirrup.com
DataRelish.com
7. What is the Red Queen’s Race?
"It takes all the running you can
do, to keep in the same place.Ifyou
wanttogetsomewhereelse,you
mustrunat leasttwiceasfastas
that!"-TheRedQueen,ThroughtheLooking-Glass
31. Don’t know which
model to choose?
•Auto ML helps
choose the
right model(s)
for your data
•Using AI to do
AI!
32. This tutorial was developed especially for..
• You!
• I worked with the Auto ML
team to produce a demo… and
they published it online for
you to try.
• SO a HUGE THANK YOU to the
Auto ML team for their efforts
and help.
• They would love your
feedback!
34. The model chose
MinMaxScaler as the best
• Transforms features by scaling each feature to a
given range.
• This estimator scales and translates each feature
individually such that it is in the given range on
the training set, e.g. between zero and one.