Information Technology Hardware


Machine learning in smartphones

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This technology will make your device more energy efficient, enable and improve virtual and augmented reality experiences, provide for smarter camera functionalities, improve device security, and of course, allow for better audio connections. Mobile processors like Qualcomm's Snapdragon 835 leverage machine learning in an effort to extend and expand the boundaries of mobile performance. Yes, artificial intelligence and machine learning can aid and improve all sorts of functions and processes in minutes and specific ways, but as it concerns you, the user, your phone will simply do everything that you need it to – but faster, better, and with greater efficiency. Many devices already feature some form of machine learning (those with the Snapdragon 835 mobile processor, for example, like ODG's R-8 and R-9 smart glasses).


Robotics In The Years Ahead – The India Story - CXOtoday.com

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With recent developments in AI, we are seeing the advent of intelligent robots that can perform tasks similar to that of a human being. The acceptance, however, will be a gradual process before robots address real-life issues, alongside with human beings. Though India is not yet self-sufficient to develop or manufacture complex and sophisticated robotic system, however, due to the availability of 3D printers and opens source development platforms like ROS, it has brought-in significant changes to the ability of Indian researchers to come-up with robotic solution for specific problems in the day to day life. It is absolutely true that technology like robotics will revolutionise the human employment.


Microsoft made its AI work on a $10 Raspberry Pi

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The idea came about from Microsoft Labs teams in Redmond and Bangalore, India. By doing that, they were able to make an image detection system run about 20 times faster on a Raspberry Pi 3 without any loss of accuracy. "There is just no way to take a deep neural network, have it stay as accurate as it is today, and consume 10,000 times less resources. To get some new ideas and help, they've made some of their early training tools and algorithms available to Raspberry Pi hobbyists and other researchers on Github.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

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A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Machine Learning Reveals Systematic Sexism in Astronomy

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Now, a quantitative study published on Friday in Nature Astronomy demonstrates that gender bias in astronomical research extends even to journal citations, which are an indicator of academic prestige and are linked with better access to grant money, speaking engagements, and professional advancement. Led by Neven Caplar, a PhD student at ETH Zürich's Institute of Astronomy, the new research found that papers with male lead authors were cited 10 percent more frequently than papers led by women, even after controlling for non-gender-specific disparities such as seniority, team size, publication date, field, and academic institution. The team reached this conclusion after using machine-learning to analyze a dataset of over 200,000 papers published between 1950 and 2015 in five influential journals: Astronomy & Astrophysics, The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society, Nature, and Science. In cases where first authors used their initials--a tactic women researchers disproportionately use to avoid gender bias--Caplar's team took extra measures to identify exceptions in publishing records that exposed authors' full names.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Apple 'Neural Engine' chip could power AI on iPhones

Engadget

But to keep its devices competitive, Apple is building a secondary mobile processor dedicated to powering AI. The tech titan's devices currently split AI tasks between two chips -- the main processor and a GPU -- but this new one, allegedly known internally as the Apple Neural Engine, has its own module dedicated to AI requests. That puts Apple further behind Qualcomm's latest Snapdragon mobile chips, which already have a dedicated AI module, and Google's Tensor Processing Units available in its Cloud Platform to do AI heavy lifting. Unlike the company's differential privacy methods protecting data sent to Apple's servers, the Neural Engine chip would let devices sift through data on their own, which would be faster and easier on the battery, just like the M7 processors did for motion back in 2013.


Here's The Unofficial Silicon Valley Explainer On Artificial Intelligence

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Last year, Frank Chen, a partner at the A-list venture capital firm Andreessen Horowitz (a16z), published a primer on artificial intelligence. In fact, if the mobile cloud was computing's previous major era, the next will be the era of AI, Jen-Hsun Huang, the CEO of Nvidia, one of the world's largest makers of the kinds of graphics processors that power the computers behind today's AI applications, told me last year. "It is the most important computing development in the last 20 years, and [every major technology company is] going to have to race to make sure that AI's a core competency." Chen's primer video went "unexpectedly viral," he told Fast Company yesterday, becoming one of a16z's most viewed pieces of content ever.


Artificial intelligence has rising impact on financial markets

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Many scientists and futurists agree that the effects of artificial intelligence and automation on society are difficult to predict. While many predicted that the tip of the spear for such technology would play out in areas such as medicine or general computer system markets, the AI revolution is already underway in the financial markets. Nearly all market-making is presently dominated by machines that employ AI techniques, including advanced pattern recognition. The markets evolution from the primordial ooze of computers, networks and massive storage systems to a complex, intelligent and somewhat singular market entity will impact society well beyond finance.


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Mashable

Those microphones are backed by Intel's Real Audio technology, a natural language processing system that can respond to commands and questions, and follow context. The Oakley Radar Pace system responded with the beats per minute measurement. It even includes touch sensitive controls, which you can use to access music and talk to Intel Real Audio. For all the built-in technology and sensors, the Radar Pace lacks a heart rate monitor, which means that, if you want to track that key workout metric, you'll need to wear and connect to a third party device.