🔖 Information theory, predictability, and the emergence of complex life

Information theory, predictability, and the emergence of complex life by Luís F. Seoane and Ricard Solé (arxiv.org)

Abstract: Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.

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🔖 Information theory, predictability, and the emergence of complex life was originally published on Chris Aldrich | Boffo Socko

Statistical Physics, Information Processing, and Biology Workshop at Santa Fe Institute

Statistical Physics, Information Processing, and Biology Workshop at Santa Fe Institute

I just found out about this from John Carlos Baez and wish I could go! How have I not managed to have heard about it?

Stastical Physics, Information Processing, and Biology

Workshop

November 16, 2016 – November 18, 2016
9:00 AM
Noyce Conference Room

Abstract.
This workshop will address a fundamental question in theoretical biology: Does the relationship between statistical physics and the need of biological systems to process information underpin some of their deepest features? It recognizes that a core feature of biological systems is that they acquire, store and process information (i.e., perform computation). However to manipulate information in this way they require a steady flux of free energy from their environments. These two, inter-related attributes of biological systems are often taken for granted; they are not part of standard analyses of either the homeostasis or the evolution of biological systems. In this workshop we aim to fill in this major gap in our understanding of biological systems, by gaining deeper insight in the relation between the need for biological systems to process information and the free energy they need to pay for that processing.

The goal of this workshop is to address these issues by focusing on a set three specific question:

  1. How has the fraction of free energy flux on earth that is used by biological computation changed with time?;
  2. What is the free energy cost of biological computation / function?;
  3. What is the free energy cost of the evolution of biological computation / function.

In all of these cases we are interested in the fundamental limits that the laws of physics impose on various aspects of living systems as expressed by these three questions.

Purpose: Research Collaboration
SFI Host: David Krakauer, Michael Lachmann, Manfred Laubichler, Peter Stadler, and David Wolpert

Statistical Physics, Information Processing, and Biology Workshop at Santa Fe Institute was originally published on Chris Aldrich

🔖 Quantum Information Science II

Quantum Information Science II(edX)

Learn about quantum computation and quantum information in this advanced graduate level course from MIT.

About this course

Already know something about quantum mechanics, quantum bits and quantum logic gates, but want to design new quantum algorithms, and explore multi-party quantum protocols? This is the course for you!

In this advanced graduate physics course on quantum computation and quantum information, we will cover:

  • The formalism of quantum errors (density matrices, operator sum representations)
  • Quantum error correction codes (stabilizers, graph states)
  • Fault-tolerant quantum computation (normalizers, Clifford group operations, the Gottesman-Knill Theorem)
  • Models of quantum computation (teleportation, cluster, measurement-based)
  • Quantum Fourier transform-based algorithms (factoring, simulation)
  • Quantum communication (noiseless and noisy coding)
  • Quantum protocols (games, communication complexity)

Research problem ideas are presented along the journey.

What you’ll learn

  • Formalisms for describing errors in quantum states and systems
  • Quantum error correction theory
  • Fault-tolerant quantum procedure constructions
  • Models of quantum computation beyond gates
  • Structures of exponentially-fast quantum algorithms
  • Multi-party quantum communication protocols

Meet the instructor

bio for Isaac ChuangIsaac Chuang Professor of Electrical Engineering and Computer Science, and Professor of Physics MIT

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🔖 Quantum Information Science II was originally published on Chris Aldrich | Boffo Socko

Hector Zenil

Hector Zenil

I’ve run across some of his work before, but I ran into some new material by Hector Zenil that will likely interest those following information theory, complexity, and computer science here. I hadn’t previously noticed that he refers to himself on his website as an “information theoretic biologist” — everyone should have that as a title, shouldn’t they? As a result, I’ve also added him to the growing list of ITBio Researchers.

If you’re not following him everywhere (?) yet, start with some of the sites below (or let me know if I’ve missed anything).

Hector Zenil:

His most recent paper on arXiv:
Low Algorithmic Complexity Entropy-deceiving Graphs | .pdf

A common practice in the estimation of the complexity of objects, in particular of graphs, is to rely on graph- and information-theoretic measures. Here, using integer sequences with properties such as Borel normality, we explain how these measures are not independent of the way in which a single object, such a graph, can be described. From descriptions that can reconstruct the same graph and are therefore essentially translations of the same description, we will see that not only is it necessary to pre-select a feature of interest where there is one when applying a computable measure such as Shannon Entropy, and to make an arbitrary selection where there is not, but that more general properties, such as the causal likeliness of a graph as a measure (opposed to randomness), can be largely misrepresented by computable measures such as Entropy and Entropy rate. We introduce recursive and non-recursive (uncomputable) graphs and graph constructions based on integer sequences, whose different lossless descriptions have disparate Entropy values, thereby enabling the study and exploration of a measure’s range of applications and demonstrating the weaknesses of computable measures of complexity.

Subjects: Information Theory (cs.IT); Computational Complexity (cs.CC); Combinatorics (math.CO)
Cite as: arXiv:1608.05972 [cs.IT] (or arXiv:1608.05972v4 [cs.IT]

YouTube

Yesterday he also posted two new introductory videos to his YouTube channel. There’s nothing overly technical here, but they’re nice short productions that introduce some of his work. (I wish more scientists did communication like this.) I’m hoping he’ll post them to his blog and write a bit more there in the future as well.

Universal Measures of Complexity

Relevant literature:

Reprogrammable World

Relevant literature:

Cross-boundary Behavioural Reprogrammability Reveals Evidence of Pervasive Turing Universality by Jürgen Riedel, Hector Zenil
Preprint available at http://arxiv.org/abs/1510.01671

Ed.: 9/7/16: Updated videos with links to relevant literature

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Hector Zenil was originally published on Chris Aldrich | Boffo Socko

NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016

NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016 by NIMBioS (nimbios.org)

This tutorial will review the basics of theory in the field of evolutionary quantitative genetics and its connections to evolution observed at various time scales. Quantitative genetics deals with the inheritance of measurements of traits that are affected by many genes. Quantitative genetic theory for natural populations was developed considerably in the period from 1970 to 1990 and up to the present, and it has been applied to a wide range of phenomena including the evolution of differences between the sexes, sexual preferences, life history traits, plasticity of traits, as well as the evolution of body size and other morphological measurements. Textbooks have not kept pace with these developments, and currently few universities offer courses in this subject aimed at evolutionary biologists. There is a need for evolutionary biologists to understand this field because of the ability to collect large amounts of data by computer, the development of statistical methods for changes of traits on evolutionary trees and for changes in a single species through time, and the realization that quantitative characters will not soon be fully explained by genomics. This tutorial aims to fill this need by reviewing basic aspects of theory and illustrating how that theory can be tested with data, both from single species and with multiple-species phylogenies. Participants will learn to use R, an open-source statistical programming language, to build and test evolutionary models. The intended participants for this tutorial are graduate students, postdocs, and junior faculty members in evolutionary biology.

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NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016 was originally published on Chris Aldrich | Boffo Socko