The Algorithmic Information Dynamics course promoted and distributed by the Santa Fe Institute is coming to an end. Sponsored by Wolfram Research, the course students made heavy use of the Wolfram Language to follow lectures, read, write and share code from the cloud. This has been an enriching experience for both instructors and students and people may want to share their thoughts about it.
Probability and statistics have long helped scientists make sense of data about the natural world — to find meaningful signals in the noise. But classical statistics prove a little threadbare in today’s landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky.
The data have changed. Maybe it's time our data analysis tools did, too.
During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome.
Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning.
The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.
I'm taking a couple of courses from Complexity Explorer of the Santa Fe Institute and they are a great way to have access to technics and knowledge at the forefront of science. Thus there are two types of courses , first it would be the courses already established with a lot of previous experiences that have let them correct common and particular mistakes that come with teaching such a new subject. The second type is what I experienced in the Algorithmic Information Dynamics: From Networks to Cells course, where they made a lot of effort to give us access to an ongoing project and a book that is on the writing at this very moment. This is something that I very much appreciate because this kind of access is usually only available in a direct personal way and on site , which means people like myself that are doing research in developing countries will see in 10 to 15 years, with no participation in the development with all the drawbacks that come with it. So if you are interested in a really new way to approach causality in complex dynamical systems this is the place.
For me particularly was difficult to assimilate because I'm still getting used to algorithms and coding in general. And here's something additional to point out , as part of the course for at least two weeks we had coding tutorials that reviewed some topics with real applications, and thanks to Wolfram support they gave us access to the latest Mathematica software for which I'm very thankful that has a user-friendly interface which for me is a best way to start coding again.
Hector and Narsis did an amazing job at communicating their research and explaining the core of Algorithmic Information Dynamics. The topics disussed during the MOOC will have profound implications in the study of complex systems. Specially in medicine and artificial intelligence, where new techniques are required beyond purely statistical methods in order to reprogram and understand behavior of adaptive systems. Moreover, the use of Wolfram Language enhanced learning by providing a transparent way of modelling real world phenomena and testing new ideas. Overall, I enjoyed the course and it motivated me to apply AID in my masters' thesis: birdsong analysis. I strongly recommend teachers to keep improving / sharing / creating new study materials and keep contact with students. I am sure eventually every field will find an interesting use for AID. Maybe in some years there will be enough projects for an International Conference.
Fascinating course, which delves into a range of interesting topics like Entropy, Information, Networks, Turing Machines, Randomness a.o. and weaves a coherent picture of the subject of Algorithmic Information Dynamics that enables us to reason causally about dynamic systems. The authors do a good job in bringing this together. I am sure this will improve further over time e.g. more complete slide deck, finished book, more practical examples, more code with real data. A big thank you to the team to making that theory & topic accessible!!
Excellent Course. Thank you !
During the last three months I met Wolfram during the algodyn period and enjoyed using it heavily. This is a complete application of its kind. There are many features.
Interesting course. It would have been more useful to me if the text had been available at the start of the course, rather than at the end. Perhaps this will be fixed for the next iteration.
I really enjoyed the course, it explained a lot of ideas and braided them together to create elegant explanations of the interdependency of algorithmic complexity and probability, amongst other core concepts. it was well structured and covered a lot of ground but still, manage to keep up with current areas of interest and share new research while also be pragmatic by showing how we could use the wolfram language to test what we were taught in the lectures. I would recommend this course to anyone who has an interest in complexity theory or general desire to learn fundamental approaches to understand causality in different systems - it was great!
I got my first introduction to Woflram programming language during the Algorithmic Information Dynamics course offered by the Sante Fe institute within the complexityexplorer.org platform. Its level of functionality and the applicability to solving real problems in studying the behaviour of complex systems is next to none. Thank you!
Nice course! I found it very inspiring and very connected with my research too. I hope to use AID in many of the topics of cancer and ecological dynamics of cell systems in which I am currently working.