Lifeboat Foundation

In a new study published in Science, researchers have developed a new method for detecting infrared light by changing its frequency to a corresponding frequency in the range of visible light.

Electromagnetic waves have a characteristic frequency and wavelength that are inversely proportional; as one increases, the other decreases. Measured in Hertz (Hz), human eyes can perceive light frequencies between 400 and 750 trillion Hz, or terahertz (THz). Smartphone cameras can detect down to 300 THz, and other detectors used in fiber-optic cables can detect around 200 THz.

OAKLAND/LOS ANGELES, Calif., Dec 2 – Andy Chanley, the afternoon drive host at Southern California’s public radio station 88.5 KCSN, has been a radio DJ for over 32 years. And now, thanks to artificial intelligence technology, his voice will live on simultaneously in many places.

“I may be a robot, but I still love to rock,” says the robot DJ named ANDY, derived from Artificial Neural Disk-JockeY, in Chanley’s voice, during a demonstration for Reuters where the voice was hard to distinguish from a human DJ.

Our phones, speakers and rice cookers have been talking to us for years, but their voices have been robotic. Seattle-based AI startup WellSaid Labs says it has finessed the technology to create over 50 real human voice avatars like ANDY so far, where the producer just needs to type in text to create the narration.

To say we’re at an inflection point of the technological era may be an obvious declaration to some. The opportunities at hand and how various technologies and markets will advance are nuanced, however, though a common theme is emerging. The pace of innovation is moving at a rate previously seen by humankind at only rare points in history. The invention of the printing press and the ascension of the internet come to mind as similar inflection points, but current innovation trends are being driven aggressively by machine learning and artificial intelligence (AI). In fact, AI is empowering rapid technology advances in virtually all areas, from the edge and personal devices, to the data center and even chip design itself.

There is also a self-perpetuating effect at play, because the demand for intelligent machines and automation everywhere is also ramping up, whether you consider driver assist technologies in the automotive industry, recommenders and speech recognition input in phones, or smart home technologies and the IoT. What’s spurring our recent voracious demand for tech is the mere fact that leading-edge OEMs, from big names like Tesla and Apple, to scrappy start-ups, are now beginning to realize great gains in silicon and system-level development beyond the confines of Moore’s Law alone.

Light is an electromagnetic wave: It consists of oscillating electric and magnetic fields propagating through space. Every wave is characterized by its frequency, which refers to the number of oscillations per second, measured in Hertz (Hz). Our eyes can detect frequencies between 400 and 750 trillion Hz (or terahertz, THz), which define the visible spectrum. Light sensors in cell phone cameras can detect frequencies down to 300 THz, while detectors used for internet connections through optical fibers are sensitive to around 200 THz.

At lower frequencies, the energy transported by light isn’t enough to trigger photoreceptors in our eyes and in many other sensors, which is a problem given that there is rich information available at frequencies below 100 THz, the mid-and far–infrared spectrum. For example, a body with surface temperature of 20°C emits infrared light up to 10 THz, which can be “seen” with thermal imaging. Also, chemical and biological substances feature distinct absorption bands in the mid-infrared, meaning that we can identify them remotely and non-destructively by infrared spectroscopy, which has myriads of applications.

After Google ditched Qualcomm chips in its Pixel phones, Android chief Hiroshi Lockheimer brings a message of peace to Qualcomm’s Snapdragon Summit.

According to a new report from Korea, Samsung is considering adding water resistance to its cheaper devices, starting with the Galaxy A33.

Qualcomm kicked off its annual Snapdragon Technology Summit with its new premium smartphone SoC dubbed the Snapdragon Series 8 Gen 1. As expected, the new SoC improves performance and efficiency in every aspect of the chip, but enhancements in AI and image processing are especially important for the next generation of premium smartphones.

The new Series 8 Gen 1 SoC follows the previous Snapdragon 888 generation as Qualcomm’s premium smartphone SoC. And yes, there is a new naming convention with this generation. Rather than continuing to count up until the company runs out of numbers for new products, Qualcomm changed the naming convention to that basic series number (Series, 8, 7, 6, and 4) followed by a generational number, similar to what the company began doing with its Snapdragon 8CX SoCs for PCs. Since this is the first generation of smartphone SoCs to use the new nomenclature, the new family of devices will be “Gen 1”. For now, however, the company only announced the premium chip — the Series 8 Gen 1.

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Research has long strived to develop computers to work as energy efficiently as our brains. A study, led by researchers at the University of Gothenburg, has succeeded for the first time in combining a memory function with a calculation function in the same component. The discovery opens the way for more efficient technologies, everything from mobile phones to self-driving cars.

In recent years, computers have been able to tackle advanced cognitive tasks, like language and image recognition or displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the human brain is still unmatched in its ability to perform tasks effectively and energy efficiently.

“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades. Cognitive tasks, like image and voice recognition, require significant computer power, and mobile applications, in particular, like mobile phones, drones and satellites, require energy efficient solutions,” says Johan Åkerman, professor of applied spintronics at the University of Gothenburg.

When humans look at a scene, they see objects and the relationships between them. On top of your desk, there might be a laptop that is sitting to the left of a phone, which is in front of a computer monitor.

Many deep learning models struggle to see the world this way because they don’t understand the entangled relationships between individual objects. Without knowledge of these relationships, a robot designed to help someone in a kitchen would have difficulty following a command like “pick up the spatula that is to the left of the stove and place it on top of the cutting board.”

In an effort to solve this problem, MIT researchers have developed a model that understands the underlying relationships between objects in a scene. Their model represents individual relationships one at a time, then combines these representations to describe the overall scene. This enables the model to generate more accurate images from text descriptions, even when the scene includes several objects that are arranged in different relationships with one another.