I was invited to an Ask Me Anything session in 2017 available here
which thanks to my readers is in the top 10 all time Reddit/PLOS Science AMAs (2018)
which thanks to my readers is in the top 10 all time Reddit/PLOS Science AMAs (2018)
Looking at Mt Saint-Michel
from St Malo
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I consider myself a 'computational natural scientist' (Greg Chaitin described me as a "new kind of practical theoretician"). I co-lead the Algorithmic Dynamics Lab at SciLifeLab and the Karolinska Institute (one of the institutions that awards the Nobel Prize) in Stockholm, Sweden, where I team up with experimental scientists such as molecular biologists, immunologists, oncologists, toxicologists, and other mathematicians to understand biological processes and living systems.
I am also the head of the Algorithmic Nature Group, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project (triggering wide media coverage). Previously, I was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science at the University of Oxford as a Senior Researcher (faculty member) and director of Oxford Immune Algorithmics. When not traveling, I spend must of my time in Oxford, London/Reading and Stockholm where I conduct fundamental research at the intersection of information theory, computability and algorithmic complexity in application to areas such as graphs and networks, the challenge of causation. By introducing model-based approaches rooted in concepts of algorithmic information and computability my team and I are merging two of the most exciting areas in contemporary science: complexity science and artificial intelligence (both machine and deep learning) in particular with applications to molecular, genetic, evolutionary and behavioural biology. I am also the Editor of Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987. I am member of the Editorial Board of publications and book series such as the Springer series on Emergence, Complexity and Computation, the journals Philosophies and Frontiers in Robotics and AI for its Computational Intelligence section, among other journals. I also serve as consultant or advisor for labs and organisations such as the Living Systems Lab, Intuition Machine, and the Lifetime Foundation. |
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One year before passing away, Marvin Minsky, widely considered the founding father of Artificial Intelligence, made an astonishing claim describing what turns out to be exactly my research aim and purpose in a closing statement at a prime venue (see video on the right):
"It seems to me that the most important discovery since Gödel was the discovery by Chaitin, Solomonoff and Kolmogorov of the concept called Algorithmic Probability which is a fundamental new theory of how to make predictions given a collection of experiences and this is a beautiful theory, everybody should learn it, but it’s got one problem, that is, that you cannot actually calculate what this theory predicts because it is too hard, it requires an infinite amount of work. However, it should be possible to make practical approximations to the Chaitin, Kolmogorov, Solomonoff theory that would make better predictions than anything we have today. Everybody should learn all about that and spend the rest of their lives working on it."
Marvin Minsky Panel discussion on The Limits of Understanding World Science Festival, NYC, Dec 14, 2014 |
On the other hand, these excerpts from a review article by Sydney Brenner — the 2002 Nobel prize in Physiology or Medicine laureate awarded by the Karolinska Institute -- completes the picture of what I and my lab at the Karolinska Institute strive:
"[on biological research] in Alan Turing’s work there is much to guide us
. . . Although many believe that ‘more is better’, history tells us that ‘least is best’. We need theory and a firm grasp on the nature of the objects we study to predict the rest . . . The concept of the gene as a symbolic representation of the organism — a code script — is a fundamental feature of the living world and must form the kernel of biological theory." (my brackets) Sydney Brenner
'Turing centenary: Life's code script' Nature 482(7386):461, 2012 |
My research consists in cracking the universe (quite literally) based on these ideas from algorithmic complexity producing candidate computer programs as generative models of natural phenomena constructed from small pieces of code as the result of conducting the largest ever searches undertaken in some of the fastest supercomputers in the world. We then try to match models to data by literally putting the pieces together. Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is thus a new type of discrete calculus to study causation by algorithmic modelling to help find first principles behind natural processes building-up the next generation of algorithmic data analysis, algorithmic machine learning and algorithmic deep learning. Teaming up with immunologists, bioinformaticians, toxicologists, oncologists, cognitive scientists and molecular biologists, the lab I co-lead applies this approach to areas of behavioural, evolutionary and molecular biology.
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On the right is an image showing how a deep neural network trained with a large set of fine art paintings 'sees' me. My current research consists in helping machine and deep learning see in more clever ways beyond statistical pattern matching by introducing algorithmic complexity and algorithmic probability to artificial intelligence. Known to underperform in tasks requiring abstraction and logical inference, deep neural networks are currently very limited. For example, Judea Pearl recently pointed out that To Build Truly Intelligent Machines, Teach Them Cause and Effect. See examples of our research in this direction here and here. |
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We have found, as reported in this paper (also available in the arXiv here), by exhaustive exploration of rule composition, two 4-colour cellular automata that we've proved to be Turing universal.
This means that these CAs can, in principle, run MS Windows and any other software. These new CAs helped us show how the Boolean composition of two and three ECA rules can emulate rule 110. This also means that these new CAs can be decomposed into simpler rules and thus illustrates the process of causal composition and decomposition. It also constitutes a form of sophisticated causal coarse-graining that I have explored in other papers such as this one. In the same paper we introduce a minimal set of ECA rules that can generate all others by Boolean composition. |
Composition of ECA rules 50 ◦ 37 with colour remapping leading to a 4-colour Turing universal CA emulating rule 110.
Composition of ECA rules 170 ◦ 15 ◦ 118 with colour re-mapping mapping leading to a 4-colour Turing universal CA emulating rule 110.
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Latest news
Anniversary:
- The journal Complex Systems, which recently made me its Editor-in-chief, was founded 30 years ago and was the first in the field founded by Stephen Wolfram. To celebrate we commissioned a very nice poster that you can download and print in high resolution by clicking on it:
Online course:
- The Santa Fe Institute will be opening the course that I have prepared with my friend and colleague Narsis A. Kiani this June 12, 2018: Algorithmic Information Dynamics---A Computational Approach to Causality and Molecular Biology: From Complex Networks to Reprogramming Cells (to be also later released on EdX). Almost 300 people enrolled in only the first 3 days. Enrol here.
Course poster, trailer and content module dependencies:
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Module dependencies of the course and of the new field of Algorithmic Information Dynamics. Orange link means conceptual/motivational dependency. Pink link means weak dependency. Blue link means strong dependency.
Journal guest editing:
- I am guest-editing a special issue of the journal Philosophies, together with friend and colleague Prof. Selmer Bringsjord, on Philosophy and Epistemology of Deep Learning, if you are interested in submitting a manuscript please contact me, submission deadline will be likely by the end of the year.
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Book season time, four books coming out in the next months:
- Our book Algorithmic Information Dynamics: A Computational Approach to Causality and Molecular Biology. From Networks to Cells has been approved for publication by Cambridge University Press and will be available later this year (with N.A. Kiani and J. Tegnér)
- Our book Methods and Applications of Algorithmic Complexity: From Sequences to Graphs is also coming out soon published by Springer Verlag later this year (with F. Soler-Toscano and N. Gauvrit).
- Two more books will see the light next year: Algorithmic Cognition (with N. Gauvrit and J. Tegnér) and Graph Complexity (with N.A. Kiani and J. Tegnér), both to be published by Springer Verlag. Here the line-up:
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Other authored and edited books:
'Lo que cabe en el espacio' a short book I prepared right after my BSc degree, is available for Kindle and for free in mobi and pdf.
As contributor:
I contributed to the final materialization of the Leibniz-Chaitin medallion after Leibniz' original design 300 years ago to celebrate his discovery of binary arithmetic
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The Leibniz-Chaitin medallion story in celebration of the works of Greg Chaitin and the discovery of binary arithmetic from which, according to Leibniz, everything can be created
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I have visited more than 250 cities in about 50 countries giving talks related to my
research in about half of them, and as invited speaker in 13:
research in about half of them, and as invited speaker in 13:
Histograms of countries and continents:
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