1. Machine learning
and AI
Dr. Helena F. Deus
Women in Tech Summit
Philadelphia, April 2018
Photo by François-Dominique / CC BY-SA 4.0
2. | 2Elsevier Labs
Machine learning is a field of computer science that
gives computer systems the ability to "learn" with data,
without being explicitly programmed.
Deep
Learning
Machine
Learning
AI
3. | 3Elsevier Labs
About Elsevier
• 130 year old company, HQ in Amsterdam
• 2500 scientific journals (e.g. Cell, Lancet) and 30 000 e-
books (e.g. Gray’s Anatomy)
• Today, a global information analytics business with a
mission to 1) advance healthcare; 2) enable open
science and 3) improve professional performance
Only great
science shall
pass
4. | 4Elsevier Labs
Gender distribution at Elsevier
35%
68%
54%
63%
31%
45%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Technology Non Technology All
Gender distribution at Elsevier
Female Male NA
Elsevier's FTE
gender
distribution is:
Female: 54%
Male: 45%
Tech industry average is 25%
Elsevier has a
unique market
position with over
10% more women
in tech roles than
industry average.
This can be used
for recruitment
purposes.
For open positions: Patrick Irwin (p.irwin.1@elsevier.com), https://www.elsevier.com/about/careers/technology-careers
5. | 5Elsevier Labs
About me
• Data Scientist
• BS in Biology, PhD in Bioinformatics
• Deep learning user for a little over a year
• Passionate about using AI for solving health care
WHAT MY FRIENDS THINK I
DO
WHAT I REALLY DO
8. | 8
A brief history of machine learning and AI
1840:Comput
ers can be
programmed
(Ada
Lovelace)
1950: Turing
test (Alan
Turing)
1952:
English-like
programming
languages
(Grace
Hopper)
1956:
"Artificial
intelligence"
is coined
(John
McCarthy)
1957: First
artificial
neural
network
(Frank
Rosenblatt)
1958:
Logistic
regression
(David Cox)
1969: Apollo
11 - learn low
and high
priority tasks
(Margaret
Hamilton)
1970: “AI
winter”
caused by
inflated hype
9. | 9
1982:
Recurrent
Neural Nets
(John Hopfield)
1993: Modern
Support Vector
Machines
(Corinna
Cortes)
1999:
Convolutional
Neural Nets
(Yann LeCun)
2006:
ImageNet (Fei-
Fei Li)
2011: IBW
Watson beat
humans in
Jeopardy
2012: Coursera
AI course
(Daphne Kohler,
Andrew Ng)
2014:
Facebook
publishes
DeepFace
2016: Google's
AlphaGo beats
humans in Go
A brief history of machine learning and AI
10. | 10Elsevier Labs
Big “Structured” Data
2 billion: number of
facebook users
82 million: amazon
reviews
14 million: labelled
ImageNet
12. | 12
How gradient descent works
NEEDS
IMPROVEMENT
ACCEPTABLE
IDEAL
KEEP TRYING
Cost or Loss Function: How far from
reality is the prediction
13. | 13
Regression(s)
If they visited 200 times, how much cash
would they spend?
Regression: pick the line that minimizes the
distance between the points and the line
http://scikit-learn.org/
http://colab.research.google.com/
14. | 14
Classification with Support Vector Machines
New flower has [6.2, 2.9, 4.3, 1,3] – can you
tell me the species?
15. | 15Elsevier Labs
Neural networks are easy with linear algebra
A
B
C
D
E
A
B
C
D
E
A
B
C
D
E
Back Propagation!
distance from target is 0.6
0.2
0.2
0.2
https://keras.io/
17. | 17Elsevier Labs
Deep Learning - neural networks with a lot of layers
https://www.cs.toronto.edu/~frossard/post/vgg16/
Convolutional Neural Networks (CNN) Generative Adversarial Networks (GAN)
“car”
https://towardsdatascience.com/gan-introduction-and-implementation-part1-implement-a-
simple-gan-in-tf-for-mnist-handwritten-de00a759ae5c
For you to Google: MNIST CNN Keras
Good website: https://machinelearningmastery.com/
22. | 22Elsevier Labs
Word Embeddings and Neural Networks
I
am
having
a
lovely
time
here
in
Philadelphia
positive
negative
0.1
0.9
Word2Vec
https://erikbern.com/2015/09/24/nearest-neighbor-
methods-vector-models-part-1.html
23. | 23Elsevier Labs
All mice were maintained in a temperature controlled (22 ± 2 °C) environment
12-h light 12-h dark photocycle and fed rodent chow meal .
The mice were individually placed into an acrylic cylinder (25 cm height 10 cm
diameter) containing 8 cm of water maintained at 22–24 °C
Cold mice and Cancer Research Deus et al 2017, IEEE
Training set: 480 sentences ; Train/Test split: 70/30; <1 min training time
Matching phrases (eg mice .. kept):
24.6% False
Discovery Rate
Using Neural Networks:
4% False
Discovery Rate
25. | 25Elsevier Labs
Why you should be
concerned about AI
“Ill-conceived mathematical
models
now micromanage the economy,
from advertising to prisons.”