...Today, you can send a design to a fab lab and you need ten different machines to turn the data into something. Twenty years from now, all of that will be in one machine that fits in your pocket. This is the sense in which it doesn't matter. You can do it today. How it works today isn't how it's going to work in the future but you don't need to wait twenty years for it. Anybody can make almost anything almost anywhere.
...Finally, when I could own all these machines I got that the Renaissance was when the liberal arts emerged—liberal for liberation, humanism, the trivium and the quadrivium—and those were a path to liberation, they were the means of expression. That's the moment when art diverged from artisans. And there were the illiberal arts that were for commercial gain. ... We've been living with this notion that making stuff is an illiberal art for commercial gain and it's not part of means of expression. But, in fact, today, 3D printing, micromachining, and microcontroller programming are as expressive as painting paintings or writing sonnets but they're not means of expression from the Renaissance. We can finally fix that boundary between art and artisans.
Making toast doesn’t sound very complicated -- until someone asks you to draw the process, step by step. Tom Wujec loves asking people and teams to draw how they make toast, because the process reveals unexpected truths about how we can solve our biggest, most complicated problems at work. Learn how to run this exercise yourself, and hear Wujec’s surprising insights from watching thousands of people draw toast.
By studying the movement and bodies of insects such as ants, Sarah Bergbreiter and her team build incredibly robust, super teeny, mechanical versions of creepy crawlies … and then they add rockets. See their jaw-dropping developments in micro-robotics, and hear about three ways we might use these little helpers in the future.
What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think.
Is the War on Drugs doing more harm than good? In a bold talk, drug policy reformist Ethan Nadelmann makes an impassioned plea to end the "backward, heartless, disastrous" movement to stamp out the drug trade. He gives two big reasons we should focus on intelligent regulation instead.
Pollination: it's vital to life on Earth but largely unseen by the human eye. Filmmaker Louie Schwartzberg shows us the intricate world of pollen and pollinators with gorgeous high-speed images from his film "Wings of Life," inspired by the vanishing of one of nature's primary pollinators, the honeybee.
This video explains our research on autonomous unmanned aerial vehicles (UAVs). The research team at the Alpen-Adria University and Lakeside Labs developing a multi-UAV system by four key components: - the multiple UAV platforms,
When you picture the lowest levels of the food chain, you might imagine herbivores happily munching on lush, living green plants. But this idyllic image leaves out a huge (and slightly less appetizing) source of nourishment: dead stuff. John C. Moore details the "brown food chain," explaining how such unlikely delicacies as pond scum and animal poop contribute enormous amounts of energy to our ecosystems.
Carefully considering all well-known worries about privacy, professor Dirk Helbing raises a great concept: The Planetary Nervous System (PNS). Roughly, this idea involves connecting our smartphones worldwide to build a global measurement network and create a flow of information on all kinds of topics.
You may remember neuroscientist Miguel Nicolelis — he built the brain-controlled exoskeleton that allowed a paralyzed man to kick the first ball of the 2014 World Cup. What’s he working on now? Building ways for two minds (rats and monkeys, for now) to send messages brain to brain. Watch to the end for an experiment that, as he says, will go to "the limit of your imagination."
I discuss "simple" dynamical systems on networks and examine how network structure affects dynamics of processes running on top of networks. I'll give an introduction to the idea of social ("complex") contagions, and I'll present a model for multi-stage complex contagions in which fanatics produce greater influence than mere followers. I'll also briefly discuss the use of ideas from topics like persistent homology to examine wavefront propagation versus the appearance of new contagion clusters, and I'll present a model (without network structure) for the adoption of applications on Facebook. The last family of models illustrates how very different time-dependent dynamics can produce quantitatively similar long-time behavior, which poses both very serious challenges and exciting opportunities for the modeling of complex systems.
Renowned public thinker Cass Sunstein defends his groundbreaking nudge theory. When the state seeks to influence our choices in “our best interests” is this liberty-infringing meddling, or simply good government?
How can we begin to address the global, insidious problem of climate change — a problem that’s too big for any one country to solve? Economist Nicholas Stern lays out a plan, presented to the UN’s Climate Summit in 2014, showing how the world’s countries can work together on climate. It’s a big vision for cooperation, with a payoff that goes far beyond averting disaster. He asks: How can we use this crisis to spur better lives for all?
Big Data is everywhere — even the skies. In an informative talk, astronomer Andrew Connolly shows how large amounts of data are being collected about our universe, recording it in its ever-changing moods. Just how do scientists capture so many images at scale? It starts with a giant telescope …
We have known for at least 100 years that a brain is organized as a network of connections between nerve cells. But in the last 10 years there has been a rapid growth in our capacity to quantify the complex topological pattern of brain connectivity, using mathematical tools drawn from graph theory. Here we bring together articles and reviews from some of the world’s leading experts in contemporary brain network analysis by graph theory. The contributions are focused on three big questions that seem important at this stage in the scientific evolution of the field: How does the topology of a brain network relate to its physical embedding in anatomical space and its biological costs? How does brain network topology constrain brain dynamics and function? And what seem likely to be important future methodological developments in the application of graph theory to analysis of brain networks? Clearer understanding of the principles of brain network organization is fundamental to understanding many aspects of cognitive function, brain development and clinical brain disorders. We hope this issue provides a forward-looking window on this fast moving field and captures some of the excitement of recent progress in applying the concepts of graph theory to measuring and modeling the complexity of brain networks.
Complex network theory and the brain Issue compiled and edited by David Papo, Javier M. Buldú, Stefano Boccaletti and Edward T. Bullmore
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