The document discusses the evolution of machine intelligence and its applications in various fields, highlighting the importance of sharing knowledge and the responsibility of both teachers and learners in this process. It reviews key concepts in machine learning, including supervised and unsupervised learning, and mentions significant market trends and the role of data in AI development. The author emphasizes that advancements in AI are driven by data availability rather than just algorithmic superiority.

1. Machine Intelligence
and how it evolved

2. Wilder Rodrigues
Software Engineer
Crazy about A.I.
And pretty much lots of other things
As geek as it can get, chiefly
about the X-Men
Proud to be a family man and
father of three
@wilderrodrigues

3. One doesn’t need to be a subject matter expert to
share knowledge. Whilst the one sharing must be
open to criticism, the one listening is responsible
to fact check, give feedback and spread what has
been learned.
The sharing process must be organic.
Wilder Rodrigues

4. From 2015 to 2017
Machine Learning

5. What is it about?

6. Computing Machinery & Intelligence
Original question:
Can machines think?
Alternative question:
Are there imaginable digital
computers which would do
well in the imitation game?
Alan Turing

7. A look at today’s A.I.
Supervised Learning
Spam classifiers
Everything recognition
Diseases diagnosis
Speech-to-text
Driving economics
38% of enterprises are already using
A.I.
$8 bilion market in 2016

8. A look at today’s A.I.
Job Displacement
Blue River
Agriculture
CV, ML and Robotics
Goldman Sachs
Trading
45 % of trading is done
electronically

9. Machine Learning
Arthur Samuel (1959)
Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed.
Tom Mitchell (1998)
Well-posed Learning Problem: A computer
program is said to learn from experience E with
respect to some task T and some performance
measure P, if its performance on T, as measured
by P, improves with experience E.
Classifying emails as spam or not spam.
(T)
Watching you label emails as spam or not
spam. (E)
The number (or fraction) of emails
correctly classified as spam/not spam. (P)

10. A look into tomorrow’s A.I.
[Hyped] Unsupervised
Learning
Mostly done on research still
Economics
68% of enterprises using A.I.
by 2018
300% increase in investment
in 2017
More than $47 billion
market by 2020

11. Defensive Barriers
Data is the only barrier one can
build!
Community is open and
algorithms pop up every day
There is an open data movement.
The idea is to speed up the A.I.
evolution.
“It’s not who has the best algorithm that wins. It’s who has the
most data.”
Banko en Brill, 2001

12. A.I. as a Product
Anything a typical human can do within a second of
thought, can probably be automated, now or soon,
with A.I.
Andrew Ng.
Desire Feasibility

13. Demo

14. Feature Extraction
Extract Convert Normalise
Feed propagation
Back propagation
Initialise weights
Implement forward propagation
Implement cost functions
Implement back propagation
Overfitting

15. References and Sources
Scaling to Very Large
Corpora
MIT Technology
Forbes
WikiPedia

16. Thank You!