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Category: robotics/AI

Horizon Robotics, led by Yu Kai, Baidu’s former deep learning head, is developing AI chips and software to mimic how the human brain solves abstract tasks, such as voice and image recognition. The company believes that this will provide more consistent and reliable services than cloud based systems.

The goal is to enable fast and intelligent responses to user commands, with out an internet connection, to control appliances, cars, and other objects. Health applications are a logical next step, although not yet discussed.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center.

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The role of technology within our personal and professional lives continues evolving at an exceptionally fast pace. From utility-based mobile apps and wearable devices, to the emergence of augmented and virtual reality, the digital revolution is expanding to cover every aspect of the human experience.

In an era of entrepreneurship, founders rely heavily on advancements in technology to develop cutting edge products, platforms and experiences that meet the growing demands of a global consumer base. As content remains essential to building a brand or launching a business, it’s also critical that companies have the capability to swiftly adapt in changing markets. Being able to successfully scale a business, amidst the inevitable pivots and unexpected turns, requires having access to the tools and solution-based software needed to create, modify and fix things on-demand.

For companies dependent on manpower to manage these responsibilities, efficiency becomes contingent upon talent and training, guided by sharp instincts and relentlessly working around the clock to assure tasks are not only completed effectively, but to further offset the likelihood of human error. For software developers, who have an extremely detailed and meticulous role, being such an invaluable piece to the puzzle can prove to be very risky, time-consuming and equally as expensive; especially as testing, predicting and automating becomes increasingly paramount. As companies aim to cut costs without sacrificing quality, while understanding the core function of technology is to provide streamline solutions to complex problems, what arises is the notorious battle of man versus machine, and also where a company like Dev9 steps in.

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A new technique has been developed to implant high-performance magnetic memory chip on a flexible plastic surface without compromising performance.

It looks like a small piece of transparent film with tiny engravings on it, and is flexible enough to be bent into a tube. Yet, this piece of “smart” plastic demonstrates excellent performance in terms of data storage and processing capabilities. This novel invention, developed by researchers from the National University of Singapore (NUS), hails a breakthrough in the flexible electronics revolution, and brings researchers a step closer towards making flexible, wearable electronics a reality in the near future.

The technological advancement is achieved in collaboration with researchers from Yonsei University, Ghent University and Singapore’s Institute of Materials Research and Engineering. The research team has successfully embedded a powerful magnetic memory chip on a flexible plastic material, and this malleable memory chip will be a critical component for the design and development of flexible and lightweight devices. Such devices have great potential in applications such as automotive, healthcare electronics, industrial motor control and robotics, industrial power and energy management, as well as military and avionics systems.

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Like this feature on QC.

If you have trouble wrapping your mind around quantum physics, don’t worry — it’s even hard for supercomputers. The solution, according to researchers from Google, Harvard, Lawrence Berkeley National Laboratories and others? Why, use a quantum computer, of course. The team accurately predicted chemical reaction rates using a supercooled quantum circuit, a result that could lead to improved solar cells, batteries, flexible electronics and much more.

Chemical reactions are inherently quantum themselves — the team actually used a quote from Richard Feynman saying “nature isn’t classical, dammit.” The problem is that “molecular systems form highly entangled quantum superposition states, which require many classical computing resources in order to represent sufficiently high precision,” according to the Google Research blog. Computing the lowest energy state for propane, a relatively simple molecule, takes around ten days, for instance. That figure is required in order to get the reaction rate.

That’s where the “Xmon” supercooled qubit quantum computing circuit (shown above) comes in. The device, known as a “variational quantum eigensolver (VQE)” is the quantum equivalent of a classic neural network. The difference is that you train a classical neural circuit (like Google’s DeepMind AI) to model classical data, and train the VQE to model quantum data. “The quantum advantage of VQE is that quantum bits can efficiently represent the molecular wave function, whereas exponentially many classical bits would be required.”

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Although BMI is nothing new; I never get tired of highlighting it.

Now the group has come up with a way for one person to control multiple robots.

The system works using one controller who watches the drones, while his thoughts are read using a computer.

The controller wears a skull cap fitted with 128 electrodes wired to a computer. The device records electrical brain activity. If the controller moves a hand or thinks of something, certain areas light up.

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Open the hood of just about any electronic gadget and you probably will find printed circuit boards (PCBs)—most often in a leaf-green color—studded with processing, memory, data-relaying, graphics, and other types of chips and components, all interconnected with a labyrinth of finely embossed wiring. By challenging the technology community to integrate the collective functions hosted by an entire PCB onto a device approaching the size of a single chip, DARPA’s newest program is making a bid to usher in a fresh dimension of technology miniaturization.

“We are trying to push the massive amount of integration you typically get on a printed circuit board down into an even more compact format,” said Dr. Daniel Green, manager of the new program, whose acronym, “CHIPS,” is itself a typographic feat of miniaturization; the program’s full name is the Common Heterogeneous Integration and Intellectual Property (IP) Reuse Strategies Program. “It’s not just a fun acronym,” Green said. “The program is all about devising a physical library of component chips, or chiplets, that we can assemble in a modular fashion.”

A primary driver of CHIPS is to develop a novel, industry-friendly architectural strategy for designing and building new generations of microsystems in which the time and energy it takes to move signals—that is, data—between chips is reduced by factors of tens or even hundreds. “This is increasingly important for the data-intensive processing that we have to do as the data sets we are dealing with get bigger and bigger,” Green said. Although the program does not specify applications, the new architectural strategy at the program’s heart could open new routes to computational efficiencies required for such feats as identifying objects and actions in real-time video feeds, real-time language translation, and coordinating motion on-the-fly among swarms of fast-moving unmanned aerial vehicles (UAVs).

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