A presentation on the past and future aspects of Machine Learning.

1. Machine Learning
Machines that learn
By: Mayank Shekhar

2. What is Machine Learning?
A comic strip by Tom Gauld

3. Why is Machine Learning important?
A visual representation of Yoshua Bengio's "learning
model," where machines "think" about faces and emotions.
Image: Courtesy Yoshua Bengio
NETFLIX recommender system knows you better than
you know yourself!
Source: https://www.wired.com/2013/06/yoshua-bengio/

4. How does Machine Learning work?
Supervised Learning - input data and
corresponding output data
Unsupervised Learning - Only input data

5. What’s the difference between AI and
Machine Learning?
Artificial Intelligence is the broader concept of machines being able to carry out
tasks in a way that we would consider “smart”.
And,
Machine Learning is a current application of AI based around the idea that we
should really just be able to give machines access to data and let them learn
for themselves.
Source: https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#40878cdf2742

6. Drawbacks
Limited hardware support
Large input data requirements
Unsupervised learning still in development
phase
Needs GPU

7. What can be expected in the future?
The most powerful form of machine learning being used today, called “deep
learning”, builds a complex mathematical structure called a neural network
based on vast quantities of data. Designed to be analogous to how a human
brain works, neural networks themselves were first described in the 1930s. But
it’s only in the last three or four years that computers have become powerful
enough to use them effectively.
If deep learning will be as big as the internet, it’s time for everyone to start
looking closely at it!
Source: https://www.theguardian.com/technology/2016/jun/28/google-says-machine-learning-is-the-future-so-i-tried-it-myself

8. Google’s DeepMind AI, developed
by researchers at Google, learns
without human input.
DeepMind AI is a big deal because
it was able to beat a “human”
champion at the game of “Go” (a
Chinese game), which has
10^1023 possible situations. If the
number doesn’t make sense then
there are “only” 10^82 atoms in
the observable universe.
DeepMind Technologies' goal is to
"solve intelligence", which they are
trying to achieve by combining
"the best techniques from machine
learning and systems neuroscience
to build powerful general-purpose
learning algorithms".
Image source: https://xkcd.com/894/

9. Thank you!