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Category: mobile phones – Page 134

Using Artificial Intelligence to Generate 3D Holograms in Real-Time on a Smartphone

A new method called tensor holography could enable the creation of holograms for virtual reality, 3D printing, medical imaging, and more — and it can run on a smartphone.

Despite years of hype, virtual reality headsets have yet to topple TV or computer screens as the go-to devices for video viewing. One reason: VR can make users feel sick. Nausea and eye strain can result because VR creates an illusion of 3D viewing although the user is in fact staring at a fixed-distance 2D display. The solution for better 3D visualization could lie in a 60-year-old technology remade for the digital world: holograms.

Holograms deliver an exceptional representation of 3D world around us. Plus, they’re beautiful. (Go ahead — check out the holographic dove on your Visa card.) Holograms offer a shifting perspective based on the viewer’s position, and they allow the eye to adjust focal depth to alternately focus on foreground and background.

The Apple Car Is Coming Sooner Than You Think

Tesla remains the king.

Ranjan KC

The Apple Car. Quite possibly the most hotly anticipated rumour of this decade. And last decade. Years in the making, and still years from its first appearance, what do we know about the Apple Car?

The late Apple co-founder Steve Jobs was said to be thinking about the company’s involvement in the automotive industry way back in 2008, the era of the iPhone 3G. Fast forward a few years and the Project Titan name begins to get thrown around, an Apple project destined to bring autonomous transport to life. More than 1000 employees were transferred onto this project in its early days.

Apple seemingly put all its eggs into this basket though, because in 2016 rumours had it that Project Titan was getting axed. After major staffing changes and leadership issues, the Project remains in operation today with John Giannandrea at the wheel — Apple’s artificial intelligence and machine learning chief.

Continue reading “The Apple Car Is Coming Sooner Than You Think” | >

State of the Edge report projects edge computing will reach $800B by 2028

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.

New approach found for energy-efficient AI applications

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 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 for image classification in such a way that the —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.

Nanoprinted high-neuron-density optical linear perceptrons perform near-infrared inference on a CMOS chip

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 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 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.

New skills of graphene: Tunable lattice vibrations

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.

Artificial intelligence is killing choice and chance – changing what it means to be human

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.

Writing – and later, the printing press – made it possible to carefully record history and easily disseminate knowledge, but it eliminated centuries-old traditions of oral storytelling. Ubiquitous digital and phone cameras have changed how people experience and perceive events. Widely available GPS systems have meant that drivers rarely get lost, but a reliance on them has also atrophied their native capacity to orient themselves.