Lifeboat Foundation

The State of the Edge report is based on analysis of the potential growth of edge infrastructure from the bottom up across multiple sectors modeled by Tolaga Research. The forecast evaluates 43 use cases spanning 11 vertical industries.

The one thing these use cases have in common is a growing need to process and analyze data at the point where it is being created and consumed. Historically, IT organizations have deployed applications that process data in batch mode overnight. As organizations embrace digital business transformation initiatives, it’s becoming more apparent that data needs to be processed and analyzed at the edge in near real time.

Of course, there are multiple classes of edge computing platforms, ranging from smartphones and internet of things (IoT) gateways to complete hyperconverged infrastructure (HCI) platforms that are being employed to process data at scale at the edge of a telecommunications network.

Most new achievements in artificial intelligence (AI) require very large neural networks. They consist of hundreds of millions of neurons arranged in several hundred layers, i.e. they have very ‘deep’ network structures. These large, deep neural networks consume a lot of energy in the computer. Those neural networks that are used in image classification (e.g. face and object recognition) are particularly energy-intensive, since they have to send very many numerical values from one neuron layer to the next with great accuracy in each time cycle.

Computer scientist Wolfgang Maass, together with his Ph.D. student Christoph Stöckl, has now found a design method for artificial neural networks that paves the way for energy-efficient high-performance AI hardware (e.g. chips for driver assistance systems, smartphones and other mobile devices). The two researchers from the Institute of Theoretical Computer Science at Graz University of Technology (TU Graz) have optimized artificial neuronal networks in computer simulations for image classification in such a way that the neurons —similar to neurons in the brain—only need to send out signals relatively rarely and those that they do are very simple. The proven classification accuracy of images with this design is nevertheless very close to the current state of the art of current image classification tools.

Today, machine learning permeates everyday life, with millions of users every day unlocking their phones through facial recognition or passing through AI-enabled automated security checks at airports and train stations. These tasks are possible thanks to sensors that collect optical information and feed it to a neural network in a computer.

Scientists in China have presented a new nanoscale AI optical circuit trained to perform unpowered all-optical inference at the speed of light for enhanced authentication solutions. Combining smart optical devices with imaging sensors, the system performs complex functions easily, achieving a neural density equal to 1/400th that of the human brain and a computational power more than 10 orders of magnitude higher than electronic processors.

Imagine empowering the sensors in everyday devices to perform artificial intelligence functions without a computer—as simply as putting glasses on them. The integrated holographic perceptrons developed by the research team at University of Shanghai for Science and Technology led by Professor Min Gu, a foreign member of the Chinese Academy of Engineering, can make that a reality. In the future, its neural density is expected to be 10 times that of human brain.

Ranjan KC

| Phononic crystals as a nanomechanical computing platform.

Without electronics and photonics, there would be no computers, smartphones, sensors, or information and communication technologies. In the coming years, the new field of phononics may further expand these options. That field is concerned with understanding and controlling lattice vibrations (phonons) in solids. In order to realize phononic devices, however, lattice vibrations have to be controlled as precisely as commonly realized in the case of electrons or photons.

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FEATURE (THE CONVERSATION) — The history of humans’ use of technology has always been a history of co-evolution.

Philosophers from Rousseau to Heidegger to Carl Schmitt have argued that technology is never a neutral tool for achieving human ends. Technological innovations – from the most rudimentary to the most sophisticated – reshape people as they use these innovations to control their environment. Artificial intelligence is a new and powerful tool, and it, too, is altering humanity.

Continue reading “Artificial intelligence is killing choice and chance – changing what it means to be human” »

Monitoring your vital signs is becoming easier and easier these days, critical if you want to keep track of your general health and well being, and incredibly useful if you want to see how a life style, or dietary, change is playing out. In this video I look at two new companies that are utilising mobile phones to measure a whole raft of biometric data, simply and easily, and clinically tested to deliver medical-grade accuracy. And these are just first generation versions, who knows where this will take us, and what we will be able to monitor quickly and easily in the next few years.

Medical Diagnosis Software With Just A Smart PhoneIn the near future, your phone or a wearable of some description, will constantly be able to monitor all your health signs continuously ready to alert you to any worrying signs, and what they can do today is just the beginning of where we are heading.

Continue reading “Medical Diagnosis Software With Just A Smart Phone — The Future Is Arriving” »

Today, the most common use cases are much more mundane, including smartphone-based games and apps like Pokemon Go or Apple’s Ruler app, which use the phone’s screen and camera rather than relying on glasses or another set of screens sitting on your face. The few companies who are actively producing AR glasses are mostly focused on work scenarios, like manufacturing and medicine.

Industry watchers and participants think that Apple has a good chance to validated and revolutionize augmented reality like it did with smartphones.