While studying cancer biology as a health sciences student at McMaster University in 2016, Andrew Leber started to wonder how artificial intelligence might help diagnose and improve cancer treatments. He brought together 10 friends, also science students, for a reading group focused on technical concepts in machine learning. But it turned out many more students were interested. Leber and friends opened the reading group to a wider audience, and within a month it had 50 members. A few months later, Leber launched the McMaster AI Society, which blossomed into one of McMaster University's largest student-run clubs.

While studying cancer biology as a health sciences student at McMaster University in 2016, Andrew Leber started to wonder how artificial intelligence might help diagnose and improve cancer treatments. He brought together 10 friends, also science students, for a reading group focused on technical concepts in machine learning. But it turned out many more students were interested. Leber and friends opened the reading group to a wider audience, and within a month it had 50 members. A few months later, Leber launched the McMaster AI Society, which blossomed into one of McMaster University's largest student-run clubs. The group received a sponsorship from Microsoft and has since grown to more than 1,000 members, many of whom are from faculties such as business, the humanities and social sciences.

The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself. The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks. With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You'll discover: * The structured path for rapidly acquiring Data Science expertise * How to build your ability in statistics to help interpret and analyse data more effectively * How to perform visualizations using one of the industry's most popular tools * How to apply machine learning algorithms with Python to solve real world problems * Why the cloud is important for Data Scientists and how to use it Along with much more.

A diverse team is also key to avoid and detect bias, discriminatory views and prejudiced opinions reflected in the data the systems are learning from. Googles head of AI, John Giannandrea, recently said "forget killer robots -- bias is the real AI danger". I certainly agree with him. The AI systems learn from data. It all starts with data.

Beyond being the founder of Sci-Hub, the world's largest pirate site for academic papers, and risking arrest as a result, Alexandra Elbakyan is a typical science graduate student: idealistic, hard-working, and relatively poor. After becoming hooked on science at an early age, she discovered a knack for computer hacking when she went to university in Kazakhstan, where she was born. After a stint in Germany working on brain-computer interfaces, she returned home, where frustrations with journal paywalls led her to create Sci-Hub. She is now enrolled in a history of science master's program.

Many writers have observed that default logics appear to contain the "lottery paradox" of probability theory. This arises when a default "proof by contradiction" lets us conclude that a typical X is not a Y where Y is an unusual subclass of X. We show that there is a similar problem with default "proof by cases" and construct a setting where we might draw a different conclusion knowing a disjunction than we would knowing any particular disjunct. Though Reiter's original formalism is capable of representing this distinction, other approaches are not. To represent and reason about this case, default logicians must specify how a "typical" individual is selected. The problem is closely related to Simpson's paradox of probability theory. If we accept a simple probabilistic account of defaults based on the notion that one proposition may favour or increase belief in another, the "multiple extension problem" for both conjunctive and disjunctive knowledge vanishes.