Machine Learning And The Changing Face Of Today's Data Centers

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Machine learning and Artificial intelligence have taken over data centers by storm. As racks begin to fill with ASICs, FPGAs, GPUs, and supercomputers, the face of the hyper-scale server farm seems to change. These technologies are known to provide exceptional computing power to train machine learning systems. Machine learning is a process that involves tremendous amounts of data-crunching, which is a herculean task in itself. The ultimate goal of this tiring process is to create applications that are smart and also to improve services that are already in everyday use.


You Can Hack That: Host a Hackathon to Ideate, Innovate and Motivate GovLoop

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Hackathons are a hot commodity these days. These creative competitions are yielding big ideas on almost everything. Need to test a new product? Looking to train staff in new capabilities? You can hack all of those.


Future search engines will help you find information you don't even know you need University of Helsinki

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The research surrounding methods of information retrieval is an entire field of science whose specialists aim to provide us with even better search results – a necessity as the amount of data constantly keeps growing. To succeed in their quest, researchers are focusing on the interaction between humans and computers, connecting methods of machine learning to this interaction. One of these researchers is Dorota Głowacka, who assumed an assistant professorship in machine learning and data science at the Helsinki Centre for Data Science HiDATA at the beginning of 2019. Głowacka is studying what people search for and how they interact with search engines, with a particular focus on exploratory search. This is a search method that helps find matters relevant to the person looking for information, even if they are not entirely certain about what they are looking for to begin with.


HVAC Giant Trane Acquires EcoFactor's Home Energy Analytics Technology

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EcoFactor is one of several startups with a cloud computing platform to manage and analyze data from smart thermostats and other home energy devices. But it also specializes in using that data to monitor and predict performance problems and impending failures of the air conditioners keeping houses cool. That kind of technology could have a lot of value to the companies that make heating, air conditioning and ventilation equipment -- enough to make it worth owning. On Tuesday, HVAC giant Trane announced it has acquired EcoFactor's energy analytics software for an undisclosed sum. Trane, a brand of Ingersoll Rand, plans to integrate EcoFactor's "unique artificial intelligence (AI) capabilities for energy efficiency and HVAC fault detection" into its existing Nexia home automation line.


Spintronic memory cells for neural networks

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In recent years, researchers have proposed a wide variety of hardware implementations for feed-forward artificial neural networks. These implementations include three key components: a dot-product engine that can compute convolution and fully-connected layer operations, memory elements to store intermediate inter and intra-layer results, and other components that can compute non-linear activation functions. Dot-product engines, which are essentially high-efficiency accelerators, have so far been successfully implemented in hardware in many different ways. In a study published last year, researchers at the University of Notre Dame in Indiana used dot-product circuits to design a cellular neural network (CeNN)-based accelerator for convolutional neural networks (CNNs). The same team, in collaboration with other researchers at the University of Minnesota, has now developed a CeNN cell based on spintronic (i.e., spin electronic) elements with high energy efficiency.


UVM Study: AI Can Detect Depression in a Child's Speech

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A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics. Around one in five children suffer from anxiety and depression, collectively known as "internalizing disorders." But because children under the age of eight can't reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment. "We need quick, objective tests to catch kids when they are suffering," says Ellen McGinnis, a clinical psychologist at the University of Vermont Medical Center's Vermont Center for Children, Youth and Families and lead author of the study.


Data virtualization use cases cover more integration tasks

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Gartner predicts that 60% of organizations will deploy data virtualization software as part of their data integration tool set by 2020. That's a big jump from the adoption rate of about 35% the consulting and market research company cited in a November 2018 report on the data virtualization market. But the technology "is rapidly gaining momentum," a group of four Gartner analysts wrote in the report. The analysts said data virtualization use cases are on the rise partly because IT teams are struggling to physically integrate a growing number of data silos, as relational database management system (DBMS) environments are augmented by big data systems and other new data sources. They also pointed to increased technology maturity that has removed deployment barriers for data virtualization users.


AI In Health Care: The Top Ways AI Is Affecting The Health Care Industry

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Artificial intelligence (AI) is having a major impact on the health care industry. In fact, AI in health care is redefining the medical care field and all its functions. It is playing a big role in health care data. When health care data uses AI, it provides new and improved analytics. AI analytics are of use in the detection, diagnosis and treatment of many diseases.



Three Rules When Using AI to Add Value to Your IoT Smart Cities Machine Learning Analytikus United States

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A survey with 83 Gartner Research Circle members indicates that, among 35% of the respondents, "identifying use cases for AI" was the top three challenges in exploring and adopting AI. It's impossible to recommend a single use case that is applicable for every city, because different cities have different priorities for their smart city projects. Among all the IoT use cases in smart cities, which keep evolving and expanding, ensure you give priority to those use cases of higher value. How can the value of use cases be defined in a smart city context then? There are some general principles to follow based on two key parameters: value that the project would bring to the citizens and value that the project would deliver for the governments.