Power Is Limiting Machine Learning Deployments

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The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many benefits, much bigger gains will come from pushing machine learning to the edge. To get there, power must be addressed. "You read about how datacenters may consume 5% of the energy today," says Ron Lowman, product marketing manager for Artificial Intelligence at Synopsys.



BCI:SCIENCE AND PRACTICE.SAMARA 2017

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BCI: Science&Practice is the only annual international conference in Russia with the focus on direct brain-machine interaction. Since October 2015 it is annually organized by Samara State Medical University and IT Universe Ltd in Samara, where a wide range of healthcare technologies, including brain-computer interfaces, virtual reality and other modern IT applications are developed . The conference is supported by Department of Information Technologies of Samara Region and Neuronet Industrial Union. The organizing and program committees members are leading scientists, representatives of state and non-commercial organizations, innovative companies. Attendance is free of charge.


SpiceNews

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SoFi, directed with the help of a Super Nintendo controller paired with acoustic signals, has been engineered to help researchers explore marine life more freely in depth, and help us get closer to the expansive ecosystem that blooms beyond what our naked eyes can perceive. SoFi is essentially a soft robotic fish structure that consists of a controller, Raspberry Pi, and HiFi Berry, sealed inside a water proof silicone membrane that has been cast moulded. The membrane is also filled with a mineral oil that is non conductive, and allows for equalization underwater. The Raspberry Pi receives input from controller, after which ultrasound signals are amplified for SoFi through the HiFi Berry. These amplified ultrasound signals, which are interpreted by a modem embedded within SoFi's head, controls everything from directing tail movement, pitch and depth, to the on-board camera.


Gartner Survey Reveals Leading Organizations Expect to Double the Number of AI Projects In Place Within the Next Year

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Organizations that are working with artificial intelligence (AI) or machine learning (ML) have, on average, four AI/ML projects in place, according to a recent survey by Gartner, Inc. Of all respondents, 59% said that they have AI deployed today. The Gartner "AI and ML Development Strategies" study was conducted via an online survey in December 2018 with 106 Gartner Research Circle Members – a Gartner-managed panel composed of IT and IT/business professionals. Participants were required to be knowledgeable about the business and technology aspects of ML or AI either currently deployed or in planning at their organizations. "We see a substantial acceleration in AI adoption this year," said Jim Hare, research vice president at Gartner.


Would you trust an algorithm to diagnose an illness?

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Teaching AI systems to learn language from letters, not words

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A new approach to natural language processing (NLP) that teaches neural networks linguistic fundamentals by training them using unsegmented textual input on the interaction between individual letters rather than whole words. Most recurrent neural networks (RNNs) that form the basis of NLP systems are trained on vocabularies of known words. To train RNNs in a way that more closely resembles how humans learn the fundamentals of language, we removed the word boundaries from training data sets and trained the networks at the character (instead of word) level. A multilingual study of this unsupervised character-level language modeling task used data sets of millions of words in English, German, and Italian. It showed that these "near tabula rasa" RNNs develop an impressive spectrum of linguistic knowledge, including segmenting groups of characters into words, distinguishing nouns from verbs, and even inducing simple forms of word meaning.


Brainworks uses smartphone camera and AI to detect your heart rate, breathing

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Brainworks is combining artificial intelligence and simple smartphone camera images to automatically detect someone's heart rate or breathing rate. And that's just one of the avenues the Emeryville, California-based company is pursuing in its work to use intelligent ambient biometric sensors to improve health care. Brainworks CEO Phillip Alvelda spoke at our VB Transform AI event on Thursday, and he demoed the technology in real time, both onstage and in an interview. He said health care should be more preventative than reactive. With that in mind, the company's technology is contactless and always-on, though it only works if users have opted in for the constant monitoring, which -- unlike most medical equipment -- is physically non-invasive.


Intel's Neuromorphic System Hits 8 Million Neurons, 100 Million Coming by 2020

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At the DARPA Electronics Resurgence Initiative Summit today in Detroit, Intel plans to unveil an 8-million-neuron neuromorphic system comprising 64 Loihi research chips--codenamed Pohoiki Beach. Loihi chips are built with an architecture that more closely matches the way the brain works than do chips designed to do deep learning or other forms of AI. For the set of problems that such "spiking neural networks" are particularly good at, Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient. The new 64-Loihi system represents the equivalent of 8-million neurons, but that's just a step to a 768-chip, 100-million-neuron system that the company plans for the end of 2019. Intel and its research partners are just beginning to test what massive neural systems like Pohoiki Beach can do, but so far the evidence points to even greater performance and efficiency, says Mike Davies, director of neuromorphic research at Intel.


Seven ways your UAV-mapping business will benefit from an AI object detection platform - sUAS News - The Business of Drones

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Along with the hardware and software sectors, the drone services market is the largest segment in the commercial drone industry with the strongest expansion. According to the market research report "Global Drone Service Market Analysis & Trends – Industry Forecast to 2025", the drone services market is estimated at USD 4.4 billion in 2019 and is projected to reach USD 63.6 billion by 2025, at a CAGR of 55.9% from 2019 to 2025. This is a huge opportunity for drone service providers. The key for capturing a share of this growing market is to offer turnkey business solutions beyond data capture, such as mapping, surveying and specialized geospatial analytics. With more and more business relying on location data to optimize their day-to-day operations and planning or gain first-hand market insights.