A monthly digest of Data Science, Machine learning and tech-related events

1. Curated by Antoine Moyroud

2. Link to Article

3. Video explaining the
operation:

4. Link to Article

5. Aim:
Faster cloud adoption
Compete against
AWS, AZURE, GCP
Pros:
IBM long-time investor in
Linux Ecosystem
Cons:
Possible culture clash
between the two companies.
More info

6. Aim:
Merge O-data
(operational data)
With X-data
(experience data)
Takeaway:
Companies that didn’t become
data driven, that didn’t demand
immediate access to O-data,
eventually got left behind. Most
companies do pretty well with
O-data. But O-data is no longer
a competitive advantage.
Companies need X-data.
Interview from both CEOs

8. The research undertaken for this
report identifies seven segments of
Americans (or “tribes”) who are
distinguished by differences in their
underlying beliefs and attitudes.
Membership in these tribes was
determined by each individual’s
answers to a subset of 58 core
belief and behavioral questions that
were asked together with the rest of
the survey
Link to the report

10. UPS NPT - New Network planning tools using Machine Learning
800 Million shipments - Saving $100M-200M/year
More info

11. Why Big Tech pays poor Kenyans to teach self-driving cars
This article and short documentary
presents an insider view of
Samasource, a company which
works with big tech firms to tag
images for machine learning
algorithms such as the ones used
by autonomous vehicles.
More info (video + article)

12. For the first, an algorithm has provided
better results than human performance
when playing Montezuma’s Revenge.
As a subsection of Deep Reinforcement
Learning, Curiosity-driven machine learning,
turns the agent into a self-learner.
More info
Beating Montezuma’s Revenge
using Curiosity-driven
Machine Learning

13. What is reinforcement learning?
Understanding why solving
Montezuma's Revenge is
challenging

14. OpenAI article on how
they achieved this

15. Link to article

16. Link to interactive graph

17. $432,50045 times its high estimate
“We fed the system with a data set
of 15,000 portraits painted between
the 14th century to the 20th. The
Generator makes a new image
based on the set, then the
Discriminator tries to spot the
difference between a human-made
image and one created by the
Generator. The aim is to fool the
Discriminator into thinking that the
new images are real-life portraits.
Then we have a result.”
More info
Generative
Adversarial
Networks
explained

19. DATAVIZ

20. Source

22. Analyzing the frequency of “Ums” in a Ted Talk
Access the visualisation