'The Embarrassment of Complexity' is what unavoidably happens as silos which have externalised more than they sustainably can try to reconnect to tackle real world issues through oversimplified interfaces which ignore non-linearity and worse.
...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.
Not long ago, after you bought a new vehicle, the manufacturer had very little contact with you for years until it was time to sell you another car. The Internet of Things is changing all that. The IoT-enabled “connected car” turns the vehicle itself into a hub for an entire ecosystem of connected services that offer consumers a wealth of benefits including enhanced safety and security, a richer user experience and a new suite of product offerings. From the manufacturer’s perspective, this also helps establish an ongoing customer relationship as well as incremental revenue streams over the life of the vehicle.
Researchers at Ohio State University have developed a method for building a machine learning algorithm from data gathered from a variety of connected devices. There are two cool things about their model worth noting. The first is that the model is distributed and second, it can keep data private.
The researchers call their model Crowd-ML and the idea is pretty basic. Each device runs a version of a necessary app, much like one might run a version of SETI@home or other distributed computing application, and grabs samples of data to send to a central server. The server can tell when enough of the right data has been gathered to “teach” the computer and only grabs the data it needs, ensuring a relative amount of privacy.
The model uses a variant of stochastic (sub)gradient descent instead of batch processing, to grab data for machine learning, which is what makes the Crowd-ML effort different. Stochastic gradient descent is the basis for a lot of machine learning and deep learning efforts. It uses knowledge gleaned from previous computations to inform the next computations, making it iterative, as opposed to something processed all at once.
The paper goes on to describe how one can tweak the Crowd-ML model to ensure more or less privacy and process information faster or in greater amounts. It tries to achieve the happy medium between protecting privacy and gathering the right amount of data to generate a decent sample size to train the machine learning algorithm.
NASA and Microsoft have teamed up to develop software called OnSight, a new technology that will enable scientists to work virtually on Mars using wearable technology called Microsoft HoloLens.
Developed by NASA's Jet Propulsion Laboratory in Pasadena, California, OnSight will give scientists a means to plan and, along with the Mars Curiosity rover, conduct science operations on the Red Planet.
"OnSight gives our rover scientists the ability to walk around and explore Mars right from their offices," said Dave Lavery, program executive for the Mars Science Laboratory mission at NASA Headquarters in Washington. "It fundamentally changes our perception of Mars, and how we understand the Mars environment surrounding the rover."
OnSight will use real rover data and extend the Curiosity mission's existing planning tools by creating a 3-D simulation of the Martian environment where scientists around the world can meet. Program scientists will be able to examine the rover's worksite from a first-person perspective, plan new activities and preview the results of their work firsthand.
"We believe OnSight will enhance the ways in which we explore Mars and share that journey of exploration with the world," said Jeff Norris, JPL's OnSight project manager.
Until now, rover operations required scientists to examine Mars imagery on a computer screen, and make inferences about what they are seeing. But images, even 3-D stereo views, lack a natural sense of depth that human vision employs to understand spatial relationships.
The OnSight system uses holographic computing to overlay visual information and rover data into the user's field of view. Holographic computing blends a view of the physical world with computer-generated imagery to create a hybrid of real and virtual.
To view this holographic realm, members of the Curiosity mission team don a Microsoft HoloLens device, which surrounds them with images from the rover's Martian field site. They then can stroll around the rocky surface or crouch down to examine rocky outcrops from different angles. The tool provides access to scientists and engineers looking to interact with Mars in a more natural, human way.
"Previously, our Mars explorers have been stuck on one side of a computer screen. This tool gives them the ability to explore the rover's surroundings much as an Earth geologist would do field work here on our planet," said Norris.
Quantum computers are experimental devices that promise exponential speedups on some computational problems. Where a bit in a classical computer can represent either a 0 or a 1, a quantum bit, or qubit, can represent 0 and 1 simultaneously, letting quantum computers explore multiple problem solutions in parallel. But such “superpositions” of quantum states are, in practice, difficult to maintain.
In a paper appearing this week in Nature Communications, MIT researchers and colleagues at Brookhaven National Laboratory and the synthetic-diamond company Element Six describe a new design that in experiments extended the superposition time of a promising type of qubit a hundredfold.
In the long term, the work could lead toward practical quantum computers. But in the shorter term, it could enable the indefinite extension of quantum-secured communication links, a commercial application of quantum information technology that currently has a range of less than 100 miles.
The researchers’ qubit design employs nitrogen atoms embedded in synthetic diamond. When nitrogen atoms happen to be situated next to gaps in the diamond’s crystal lattice, they produce “nitrogen vacancies,” which enable researchers to optically control the magnetic orientation, or “spin,” of individual electrons and atomic nuclei. Spin can be up, down, or a superposition of the two.
To date, the most successful demonstrations of quantum computing have involved atoms trapped in magnetic fields. But “holding an atom in vacuum is difficult, so there’s been a big effort to try to trap them in solids,” says Dirk Englund, the Jamieson Career Development Assistant Professor in Electrical Engineering and Computer Science at MIT and corresponding author on the new paper.
“In particular, you want a transparent solid, so you can send light in and out. Crystals are better than many other solids, like glass, in that their atoms are nice and regular and their electronic structure is well defined. And amongst all the crystals, diamond is a particularly good host for capturing an atom, because it turns out that the nuclei of diamond are mostly free of magnetic dipoles, which can cause noise on the electron spin.”
Will our self-programming computers send out hostile orders to the chips we've added to our everyday objects? Or is this just another disruptive moment, similar to the harnessing of steam or the splitting of the atom?...
It’s hard to imagine an encryption machine more sophisticated than the human brain. This three-pound blob of tissue holds an estimated 86 billion neurons, cells that rapidly fire electrical pulses in split-second response to whatever stimuli our bodies encounter in the external environment. Each neuron, in turn, has thousands of spindly branches that reach out to nodes, called synapses, which transmit those electrical messages to other cells. Somehow the brain interprets this impossibly noisy code, allowing us to effectively respond to an ever-changing world.
Given the complexity of the neural code, it’s not surprising that some neuroscientists are borrowing tricks from more experienced hackers: cryptographers, the puzzle-obsessed who draw on math, logic, and computer science to make and break secret codes.
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