I’ve seen a few places in the text where he references “group(s) of Japanese scientists” in a collective way where as when the scientists are from the West he tends to name at least a principle investigator if not multiple members of a team. Is this implicit bias? I hope it’s not, but it feels very conspicuous and regular to me and I wish it weren’t there.
Extracted from The Inner Life of a Cell by Cellular Visions and Harvard (http://www.studiodaily.com/2006/07/cellular-visions-the-inner-life-of-a-cell/)
I’m pretty certain that I’ve seen this or something very similar to it in another setting. (Television perhaps?)
How does life create order from chaos? And just what is life, anyway? Leading physicist Paul Davies argues that to find the answers, we must first answer a deeper question: 'What is information?' To understand the origins and nature of life, Davies proposes a radical vision of biology which sees the underpinnings of life as similar to circuits and electronics, arguing that life as we know it should really be considered a phenomenon of information storage. In an extraordinary deep dive into the real mechanics of what we take for granted, Davies reveals how biological processes, from photosynthesis to birds' navigation abilities, rely on quantum mechanics, and explores whether quantum physics could prove to be the secret key of all life on Earth. Lively and accessible, Demons in the Machine boils down intricate interdisciplinary developments to take readers on an eye-opening journey towards the ultimate goal of science: unifying all theories of the living and the non-living, so that humanity can at last understand its place in the universe.
Paul Davies thinks combining physics and biology will reveal a pattern of information management
Very nice profile by @iansample in the @guardian of Paul Davies and his new book The Demon in the Machine. Paul’s book is a very nice statement of intent to answer Schrödinger’s question, and honest about what we don’t know. https://t.co/9FeHArKaA5
— Philip Ball (@philipcball) January 26, 2019
This book is built on a simple premise: Most companies don't know what creativity really is, so they can't benefit from it. They lack creative clarity.
Creative clarity requires you to do four things:
1. Choreograph a creative strategy, describing a clear future even among the blurry business landscape.
2. Grow teams that include those creative, unpredictable outcasts; give them the space to produce amazing work; and build a unique form of trust in your company culture.
3. Institutionalize an iterative process of critique, conflict, and ideation.
4. Embrace chaos but manage creative spin and stagnation.
This book is primarily for people in charge of driving strategic change through an organization. If you are a line manager responsible for exploring a horizon of opportunity, the book will help you establish a culture of creative product development in which your teams can predictably deliver creative results. You'll learn methods to drive trust among your team members to enable you to critique and improve their work. And as an organizational leader, you'll complement your traditional business strategies with the new language and understanding you need to implement creativity in a strategic manner across your company.
In a creative environment, chaos is the backdrop for hidden wonderment and success. In this book, you'll gain clarity in the face of that chaos, so you can build great products, great teams, and a high-performing creative organization.
How does chaos influence creativity? How can “flow states” help teams manage feedback and achieve creativity?In this episode, Haley interviews designer, educator and author, Jon Kolko. Kolko shares details from his new book Creative Clarity: A Practical Guide for Bringing Creative Thinking into Your Company, which he wrote to help leaders and creative thinkers manage the complexity and chaos of the creative process. During his interview, he explains how elements of complex systems science, including emergence, constraints, feedback and framing, influence the creative process. He also provides many helpful tips for how to foster a culture of creativity within an organization.
Quotes from this episode:
“A constraint emerges from the creative exploration itself….these constraints become a freeing way for creative people to start to explore without having rules mandated at them.” - Jon Kolko
“Framing is the way in which the problem is structured and presented and the way that those constraints start to manifest as an opportunity statement.” - Jon Kolko
“The rules around trust need to be articulated.” - Jon Kolko
“Chaos is the backdrop for hidden wonderment and success.” - Jon Kolko
I’ve seen the sentiment of “thought spaces” several times from bloggers, but this is one of the first times I’ve heard a book author use the idea:
Often when I write, it’s to help me make sense of the world around me.
In this episode, Haley interviews Natalia Komarova, Chancellor's Professor of the School of Physical Sciences at the University of California, Irvine. Komarova talks with Haley at the Ninth International Conference on Complex Systems about her presentation, which explored using applied mathematics to study the spread of mutants, as well as the evolution of popular music.
Study explores the micromechanisms underlying regional economic diversification.
In this episode, Haley interviews TK Coleman to discuss how humans allow their conflicting mental models to influence the way they handle controversial topics like racism. TK also shares how understanding context and patterns within human systems ultimately empowers us to actively contribute to human progress.
Also glad to have the recommendation of General Systems Theory: Beginning With Wholes by Barbara G. Hanson as a more accessible text in comparison to Ludwig von Bertalanffy’s text. The gang at Human Current should set up an Amazon Affiliate link so that when I buy books they recommend (which happens frequently), it helps to support and underwrite their work.
Highlights, Quotes, Annotations, & Marginalia
Reality is objective, but meaning is contextual.
—Barbara Hanson, General Systems Theory: Beginning with Wholes quoted within the episode
In this episode, Haley talks with physicist, complexity scientist, and MIT professor, Cesar Hidalgo. Hidalgo discusses his interest in the physics of networks and complex system science and shares why he believes these fields are so important. He talks about his book, Why Information Grows: The Evolution of Order, from Atoms to Economies, which takes a scientific look at global economic complexity. Hidalgo also shares how economic development is linked to making networks more knowledgeable.
Quotes from this episode:
“Thinking about complexity is important because people have a tendency to jump into micro explanations for macro phenomenon.” — Cesar Hidalgo
“I think complex systems give you not only some practical tools to think about the world, but also some sort of humbleness because you have to understand that your knowledge and understanding of how the systems work is always very limited and that humbleness gives you a different attitude and perspective and gives you some peace.” — Cesar Hidalgo
“The way that we think about entropy in physics and information theory come from different traditions and sometimes that causes a little bit of confusion, but at the end of the day it’s the number of different ways in which you can arrange something.” — Cesar Hidalgo
“To learn more complex activities you need more social reinforcement.” — Cesar Hidalgo
“When we lead groups we have to be clear about the goals and the main goal to keep in mind is that of learning.” — Cesar Hidalgo
“Everybody fails, but not everyone learns from their failures.” — Cesar Hidalgo
“Learning is not just something that is interesting to study, it is actually a goal.” — Cesar Hidalgo
I also appreciated about some of how he expanded on learning in the last portion of the interview. Definitely worth revisiting.
What does a JPEG have to do with economics and quantum gravity? All of them are about what happens when you simplify world-descriptions. A JPEG compresses an image by throwing out fine structure in ways a casual glance won't detect. Economists produce theories of human behavior that gloss over the details of individual psychology. Meanwhile, even our most sophisticated physics experiments can't show us the most fundamental building-blocks of matter, and so our theories have to make do with descriptions that blur out the smallest scales. The study of how theories change as we move to more or less detailed descriptions is known as renormalization.
This tutorial provides a modern introduction to renormalization from a complex systems point of view. Simon DeDeo will take students from basic concepts in information theory and image processing to some of the most important concepts in complexity, including emergence, coarse-graining, and effective theories. Only basic comfort with the use of probabilities is required for the majority of the material; some more advanced modules rely on more sophisticated algebra and basic calculus, but can be skipped. Solution sets include Python and Mathematica code to give more advanced learners hands-on experience with both mathematics and applications to data.
We'll introduce, in an elementary fashion, explicit examples of model-building including Markov Chains and Cellular Automata. We'll cover some new ideas for the description of complex systems including the Krohn-Rhodes theorem and State-Space Compression. And we'll show the connections between classic problems in physics, including the Ising model and plasma physics, and cutting-edge questions in machine learning and artificial intelligence.
Mark Newman is a British physicist and Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, as well as an external faculty member of the Santa Fe Institute. He is known for his fundamental contributions to the fields of complex networks and complex systems, for which he was awarded the 2014 Lagrange Prize.
No recent scientific enterprise has been so alluring, so terrifying, and so filled with extravagant promise and frustrating setbacks as artificial intelligence. But how intelligent—really—are the best of today’s AI programs? How do these programs work? What can they actually do, and what kinds of things do they fail at? How human-like do we expect them to become, and how soon do we need to worry about them surpassing us in most, if not all, human endeavors?
From Melanie Mitchell, a leading professor and computer scientist, comes an in-depth and careful study of modern day artificial intelligence. Exploring the cutting edge of current AI and the prospect of 'intelligent' mechanical creations - who many fear may become our successors - Artificial Intelligence looks closely at the allure, the roller-coaster history, and the recent surge of seeming successes, grand hopes, and emerging fears surrounding AI. Flavoured with personal stories and a twist of humour, this ultimately accessible account of modern AI gives a clear sense of what the field has actually accomplished so far and how much further it has to go.