👓 Stonehenge builders used Pythagoras' theorem 2,000 years before Greek philosopher was born, say experts | The Telegraph

Read Stonehenge builders used Pythagoras' theorem 2,000 years before Greek philosopher was born, say experts  by Sarah Knapton (The Telegraph)
The builders of Britain’s ancient stone circles like Stonehenge were using Pythagoras' theorem 2,000 years before the Greek philosopher was born, experts have claimed.

I’ll be bookmarking the book described in this piece for later. The author doesn’t get into the specifics of the claim in the title enough for my taste. What is the actual evidence? Is there some other geometrical construct they’re using to come up with these figures that doesn’t involve Pythagoras?

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👓 The role of information theory in chemistry | Chemistry World

Read The role of information theory in chemistry by Philip Ball (Chemistry World)
Is chemistry an information science after all?

Discussion of some potential interesting directions for application of information theory to chemistry (and biology).

In the 1990s, Nobel laureate Jean-Marie Lehn argued that the principles of spontaneous self-assembly and self-organisation, which he had helped to elucidate in supramolecular chemistry, could give rise to a science of ‘informed matter’ beyond the molecule.

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👓 Andrew Jordan reviews Peter Woit’s Quantum Theory, Groups and Representations and finds much to admire. | Inference

Read Woit’s Way by Andrew Jordan (Inference: International Review of Science)
Andrew Jordan reviews Peter Woit's Quantum Theory, Groups and Representations and finds much to admire.

For the tourists, I’ve noted before that Peter maintains a free copy of his new textbook on his website.

I also don’t think I’ve ever come across the journal Inference before, but it looks quite nice in terms of content and editorial.

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🔖 Complexity: An interdisciplinary forum for complexity research | PLOS

Bookmarked Complexity: An interdisciplinary forum for complexity research by PLOS (channels.plos.org)

Most of today’s global challenges, from online misinformation spreading to Ebola outbreaks, involve such a vast number of interacting players that reductionism delivers little insight. Systems are often non-linear, exhibiting complexity in temporal and spatial domains over large scales, which is a challenge to predictability and comprehension. Strategies must be found to look at the problem as a whole, in all its complexity. Representing the associated data as a complex network, in which nodes and connections between them form complicated patterns, is one such strategy. Network science provides novel tools for analyzing, visualizing and modeling this data thanks to the cross-fertilization of fields as diverse as statistical physics, algebraic topology and machine learning, among the others.

This Channel brings together all aspects of complexity research and includes interdisciplinary topics from network theory to applications in neuroscience and the social sciences.

hat tip:

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🔖 Behave: The Biology of Humans at Our Best and Worst by Robert M. Sapolsky

Bookmarked Behave: The Biology of Humans at Our Best and Worst by Robert M. Sapolsky (Penguin Press)

From the celebrated neurobiologist and primatologist, a landmark, genre-defining examination of human behavior, both good and bad, and an answer to the question: Why do we do the things we do?

Sapolsky's storytelling concept is delightful but it also has a powerful intrinsic logic: he starts by looking at the factors that bear on a person's reaction in the precise moment a behavior occurs, and then hops back in time from there, in stages, ultimately ending up at the deep history of our species and its evolutionary legacy.

And so the first category of explanation is the neurobiological one. A behavior occurs--whether an example of humans at our best, worst, or somewhere in between. What went on in a person's brain a second before the behavior happened? Then Sapolsky pulls out to a slightly larger field of vision, a little earlier in time: What sight, sound, or smell caused the nervous system to produce that behavior? And then, what hormones acted hours to days earlier to change how responsive that individual is to the stimuli that triggered the nervous system? By now he has increased our field of vision so that we are thinking about neurobiology and the sensory world of our environment and endocrinology in trying to explain what happened.

Sapolsky keeps going: How was that behavior influenced by structural changes in the nervous system over the preceding months, by that person's adolescence, childhood, fetal life, and then back to his or her genetic makeup? Finally, he expands the view to encompass factors larger than one individual. How did culture shape that individual's group, what ecological factors millennia old formed that culture? And on and on, back to evolutionary factors millions of years old.

The result is one of the most dazzling tours d'horizon of the science of human behavior ever attempted, a majestic synthesis that harvests cutting-edge research across a range of disciplines to provide a subtle and nuanced perspective on why we ultimately do the things we do...for good and for ill. Sapolsky builds on this understanding to wrestle with some of our deepest and thorniest questions relating to tribalism and xenophobia, hierarchy and competition, morality and free will, and war and peace. Wise, humane, often very funny, Behave is a towering achievement, powerfully humanizing, and downright heroic in its own right.

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📺 Open science: Michael Nielsen at TEDxWaterloo | YouTube

Watched Open science: Michael Nielsen at TEDxWaterloo by Michael NielsenMichael Nielsen from YouTube

Michael Nielsen is one of the pioneers of quantum computation. Together with Ike Chuang of MIT, he wrote the standard text in the field, a text which is now one of the twenty most highly cited physics books of all time. He is the author of more than fifty scientific papers, including invited contributions to Nature and Scientific American. His research contributions include involvement in one of the first quantum teleportation experiments, named as one of Science Magazine's Top Ten Breakthroughs of the Year for 1998. Michael was a Fulbright Scholar at the University of New Mexico, and has worked at Los Alamos National Laboratory, as the Richard Chace Tolman Prize Fellow at Caltech, as Foundation Professor of Quantum Information Science at the University of Queensland, and as a Senior Faculty Member at the Perimeter Institute for Theoretical Physics. Michael left academia to write a book about open science, and the radical change that online tools are causing in the way scientific discoveries are made.

Sadly this area of science hasn’t opened up as much as it likely should have in the intervening years. More scientists need to be a growing part of the IndieWeb movement and owning their own data, their content, and, yes, even their own publishing platforms. With even simple content management systems like WordPress researchers can actively practice academic samizdat to a much greater extent and take a lot of the centralized power away from the major journal and textbook publishing enterprises.

I can easily see open web technology like the Webmention spec opening up online scientific communication and citations drastically even to the point of quickly replacing tools like Altmetric. If major publishing wants something to do perhaps they could work on the archiving and aggregation portions?

What if one could publish a research paper or journal article on one’s own (or one’s lab’s) website? It could receive data via webmention about others who are bookmarking it, reading it, highlighting and annotating it. It could also accept webmention replies as part of a greater peer-review process–the equivalent of the researcher hosting their own pre-print server as well as their own personal journal and open lab notebook.

We need to help empower scientists to be the center of their own writing and publishing. For those interested, this might be a useful starting point: https://indieweb.org/Indieweb_for_Education



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👓 Lost in Math | Peter Woit

Read Lost in Math by Peter Woit (math.columbia.edu)
Sabine Hossenfelder’s new book Lost in Math should be starting to appear in bookstores around now. It’s very good and you should get a copy. I hope that the book will receive a lot of attention, but suspect that much of this will focus on an oversimplified version of the book’s argument, ignoring some of the more interesting material that she has put together. Hossenfelder’s main concern is the difficult current state of theoretical fundamental physics, sometimes referred to as a “crisis” or “nightmare scenario”. She is writing at what is likely to be a decisive moment for the subject: the negative LHC results for popular speculative models are now in. What effect will these have on those who have devoted decades to studying such models?

I love that he calls out the review in Science.

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🔖 NSF Workshop on Multidisciplinary Complex Systems Research – NSF Workshop on Multidisciplinary Complex Systems Research

Bookmarked NSF Workshop on Multidisciplinary Complex Systems Research – NSF Workshop on Multidisciplinary Complex Systems Research (nsfws.ece.drexel.edu)

This workshop will bring together a diverse group of experts in complementary areas of complex systems and will be preceded by a series of weekly webinars. The overarching goal of the activity is to address scientific issues that are relevant to the scientific community and bring to surface possible areas of opportunity for multidisciplinary research in the study of complex systems. The specific goals of the workshop include:

  1. identifying the most substantive research questions that can be addressed by fundamental complex systems research;
  2. recognizing community needs, knowledge gaps, and barriers to research progress in this area;
  3. identifying future directions that cut across disciplinary boundaries and that are likely to lead to transformative multidisciplinary research in complex systems.

The outcomes of the workshop will include the preparation of a report to inform the scientific community at large of the current status and challenges as well as future opportunities in multidisciplinary complex systems research as perceived by the participants of the workshop.

The workshop is motivated by the observation that many processes in natural, engineered, and social contexts exhibit emergent collective behavior and are thus governed by complex systems. Because challenges in understanding, predicting, designing, and controlling complex systems are often common to many domains, a central objective of the workshop is to facilitate the exchange of ideas across different fields and avoid disciplinary boundaries typical of many traditional scientific meetings. The workshop participants will include experts both in theory and in applications as well as a selection of postdoctoral researchers and graduate students from various domains. Because of the cross-disciplinary nature of the workshop, the participants themselves will become aware of the latest developments in fields related to but different from their own. This environment will foster discussions on the state of the art, potential issues, and most promising directions in multidisciplinary complex systems research. The inclusion of early-career researchers will help to promote the transfer of this expertise to the next generation of engineers, mathematicians, and scientists.

Downloadable [.pdf] copy of the report

h/t to @adilson_motter

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❤️ stevenstrogatz tweet

Liked a tweet by Steven Strogatz on TwitterSteven Strogatz on Twitter (Twitter)
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👓 Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells | Complexity Explorer | Santa Fe Institute

Read Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells (Complexity Explorer | Santa Fe Institute)

About the Course:

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.

Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.

Course Outline:

  • The course will begin with a conceptual overview of the field.
  • Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
  • Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
  • Students will be able apply the tools to their own data and problems. The instructors will explain  in detail how to do this, and  will provide all the tools and code to do so.

The course runs 11 June through 03 September 2018.

Tuition is $50 required to get to the course material during the course and a certificate at the end but is is free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering  new courses.

In addition to all course materials tuition includes:

  • Six-month access to the Wolfram|One platform (potentially renewable by other six) worth 150 to 300 USD.
  • Free digital copy of the course textbook to be published by Cambridge University Press.
  • Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.

Best final projects will be invited to expand their results and submit them to the journal Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987.

About the Instructor(s):

Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group at LABoRES, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he 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, University of Oxford as a faculty member and senior researcher.

Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow and Assistant Professor in Sweden. She co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. Narsis is also a member of the Algorithmic Nature Group, LABoRES.

Hector and Narsis are the leaders of the Algorithmic Dynamics Lab at the Unit of Computational Medicine at Karolinska Institute.

Alyssa Adams has a PhD in Physics from Arizona State University and studies what makes living systems different from non-living ones. She currently works at Veda Data Solutions as a data scientist and researcher in social complex systems that are represented by large datasets. She completed an internship at Microsoft Research, Cambridge, UK studying machine learning agents in Minecraft, which is an excellent arena for simple and advanced tasks related to living and social activity. Alyssa is also a member of the Algorithmic Nature Group, LABoRES.

The development of the course and material offered has been supported by: 

  • The Foundational Questions Institute (FQXi)
  • Wolfram Research
  • John Templeton Foundation
  • Santa Fe Institute
  • Swedish Research Council (Vetenskapsrådet)
  • Algorithmic Nature Group, LABoRES for the Natural and Digital Sciences
  • Living Systems Lab, King Abdullah University of Science and Technology.
  • Department of Computer Science, Oxford University
  • Cambridge University Press
  • London Mathematical Society
  • Springer Verlag
  • ItBit for the Natural and Computational Sciences and, of course,
  • the Algorithmic Dynamics lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, The Karolinska Institute

Class Introduction:Class IntroductionHow to use Complexity Explorer:How to use Complexity Explorer

Course dates: 11 Jun 2018 9pm PDT to 03 Sep 2018 10pm PDT


  1. A Computational Approach to Causality
  2. A Brief Introduction to Graph Theory and Biological Networks
  3. Elements of Information Theory and Computability
  4. Randomness and Algorithmic Complexity
  5. Dynamical Systems as Models of the World
  6. Practice, Technical Skills and Selected Topics
  7. Algorithmic Information Dynamics and Reprogrammability
  8. Applications to Behavioural, Evolutionary and Molecular Biology


Another interesting course from the SFI. Looks like an interesting way to spend the summer.

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Origin Story: A Big History of Everything by David Christian

Wished for Origin Story: A Big History of Everything by David ChristianDavid Christian (Little, Brown and Company)

"I have long been a fan of David Christian. In Origin Story, he elegantly weaves evidence and insights from many scientific and historical disciplines into a single, accessible historical narrative." --Bill Gates

A captivating history of the universe -- from before the dawn of time through the far reaches of the distant future.

Most historians study the smallest slivers of time, emphasizing specific dates, individuals, and documents. But what would it look like to study the whole of history, from the big bang through the present day -- and even into the remote future? How would looking at the full span of time change the way we perceive the universe, the earth, and our very existence?

These were the questions David Christian set out to answer when he created the field of "Big History," the most exciting new approach to understanding where we have been, where we are, and where we are going. In Origin Story, Christian takes readers on a wild ride through the entire 13.8 billion years we've come to know as "history." By focusing on defining events (thresholds), major trends, and profound questions about our origins, Christian exposes the hidden threads that tie everything together -- from the creation of the planet to the advent of agriculture, nuclear war, and beyond.

With stunning insights into the origin of the universe, the beginning of life, the emergence of humans, and what the future might bring, Origin Story boldly reframes our place in the cosmos.

As many will know, I’m enamored of Christian’s thesis of Big History, so this is going to be a must-read, though I suspect it will be a shorter and more accessible version covering a lot of similar ground to his prior heroic effort Maps of Time.

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👓 The stress of the fathers: epigenetics | The Economist

Read The stress of the fathers: epigenetics (Economist Espresso)
Abused or neglected children are more likely to have health problems as adults.

It seems like this has been known for a while or at least I’ve read relate research.

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👓 Hiding Information in Plain Text | Spectrum IEEE

Read Hiding Information in Plain Text (IEEE Spectrum: Technology, Engineering, and Science News)
Subtle changes to letter shapes can embed messages

An interesting piece to be sure, but I’ve thought of doing this sort of steganography in the past. In particular, I recall having conversations with Sol Golomb about similar techniques in the past. I’m sure there’s got to be prior art for similar things as well.

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👓 Does Donald Trump write his own tweets? Sometimes | The Boston Globe

Read Does Donald Trump write his own tweets? Sometimes (The Boston Globe)
It’s not always Trump tapping out a tweet, even when it sounds like his voice.

I wonder how complicated/in-depth the applied information theory is behind the Twitter bot described here?

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Acquired The Perfectionists: How Precision Engineers Created the Modern World by Simon WinchesterSimon Winchester (Harper)

The revered New York Times bestselling author traces the development of technology from the Industrial Age to the Digital Age to explore the single component crucial to advancement—precision—in a superb history that is both an homage and a warning for our future.

The rise of manufacturing could not have happened without an attention to precision. At the dawn of the Industrial Revolution in eighteenth-century England, standards of measurement were established, giving way to the development of machine tools—machines that make machines. Eventually, the application of precision tools and methods resulted in the creation and mass production of items from guns and glass to mirrors, lenses, and cameras—and eventually gave way to further breakthroughs, including gene splicing, microchips, and the Hadron Collider.

Simon Winchester takes us back to origins of the Industrial Age, to England where he introduces the scientific minds that helped usher in modern production: John Wilkinson, Henry Maudslay, Joseph Bramah, Jesse Ramsden, and Joseph Whitworth. It was Thomas Jefferson who later exported their discoveries to the fledgling United States, setting the nation on its course to become a manufacturing titan. Winchester moves forward through time, to today’s cutting-edge developments occurring around the world, from America to Western Europe to Asia.

As he introduces the minds and methods that have changed the modern world, Winchester explores fundamental questions. Why is precision important? What are the different tools we use to measure it? Who has invented and perfected it? Has the pursuit of the ultra-precise in so many facets of human life blinded us to other things of equal value, such as an appreciation for the age-old traditions of craftsmanship, art, and high culture? Are we missing something that reflects the world as it is, rather than the world as we think we would wish it to be? And can the precise and the natural co-exist in society?

One of my favorite all-time writers and somehow I had missed any news about its pending release and hadn’t seen it despite having just been to the bookstore. What a fantastic surprise!

Received as the most perfect gift. Originally purchased at Vroman’s.

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