Algorithmic Information Dynamics MOOC – intro video

Algorithmic Information Dynamics MOOC – intro video


The ultimate goal of science is to find the
causes behind complex phenomena and to change systems at will For example, one problem in molecular biology is to understand and
manipulate genes that are the cause of complex
diseases The value of causal discovery has been known
since ancient times. Centuries ago, people asked why does the Sun
shine everyday or why things fall down Today, we still ask all sorts of questions such as why the universe has structure or why some people develop diseases like
cancer What if you had a tool to reprogram cells to
attack cancer? Incredibly, all these questions are similar in
essence they are all related to a chain of cause and
effect that can be changed with access to the right
information. In this course, we will introduce a framework
to properly ask and approximate, in some sense, optimal
answers to these questions
from an algorithmic perspective We will present established concepts and
methods from mathematics particularly the theories of computation and
information that can be used to unveil causes and steer
systems We will also have fun with our personal AI
assistant, AlgoDyn that will ask us questions throughout the course
about everything you wanted to know but you didn’t dare to ask. AlgoDyn will also answer some of your most
pressing questions for everybody to enjoy We will explore surprising applications to graphs and networks
in areas such as
biology and, unlike mainstream tools in statistics and traditional machine learning the tools based on algorithmic complexity will give us access to a set of methods to find ultimate causes to unveil generating mechanisms, and to
produce mechanistic models fundamental in science. For example, by using
these tools, we have shown
that the human mind is best equipped to produce randomness when we reach the age of 25 The course will enable students to view life
from a computational
perspective and will empower them
to use these powerful tools in
the form of a novel
algorithmic calculus to better understand and manipulate complex
systems The course will have several units the first ones devoted to the basics needed to understand the more advanced topics. The course itself will be self-contained but it will be mostly devoted and driven by results and research
rather than based on a textbook, so you will experience the feeling of doing science. Unlike other online courses, here we will
translate
current cutting-edge research into understandable and applicable knowledge today We will introduce you to the use of our methods
in the Wolfram Language. You will learn to write your own code and perform practical explorations on networks, images, and biological data. You will be able to apply all these ideas and
methods to areas as diverse as economics, psychology, ecology, physics, chemistry, biology, and virtually
any other area of science. At the end of the course, we will be giving away some memorabilia from the
London Mathematical Society, also some books from Springer relevant to the
course, and even some Wolfram Mathematica licenses
to people finishing the course on top of their class We hope that you enjoy our course.

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2 thoughts on “Algorithmic Information Dynamics MOOC – intro video”

  • Universal Simplexity 普單 says:

    By working through this course, I hope to make further inroads into my research question of what might a universal simplexity in enterprise transformation be.

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