Category: Machine Learning

UTSA researchers want to teach computers to learn like humans

A new study by Paul Rad, assistant director of the UTSA Open Cloud Institute, and Nicole Beebe, Melvin Lachman Distinguished Professor in Entrepreneurship and director of the UTSA Cyber Center for Security and Analytics, describes a new cloud-based learning platform for artificial intelligence (A.I.) that teaches machines to learn like humans.

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Artificial agent designs quantum experiments

On the way to an intelligent laboratory, physicists from Innsbruck and Vienna present an artificial agent that autonomously designs quantum experiments. In initial experiments, the system has independently (re)discovered experimental techniques that are nowadays standard in modern quantum labs. This shows how machines could play a more creative role in research in the future.

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Innovative System Images Photosynthesis to Provide Picture of Plant Health

Crop imager could enable agricultural machinery that automatically responds to stressed plants. Researchers have developed a new imaging system that is designed to monitor the health of crops in the field or greenhouse. The new technology could one day save farmers significant money and time by enabling intelligent agricultural equipment that automatically provides plants with water or nutrients at the first signs of distress. With further development, the system has the potential to be used aboard unmanned aerial vehicles to remotely monitor crops.

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Voice impersonators can fool speaker recognition systems

Skilful voice impersonators are able to fool state-of-the-art speaker recognition systems, as these systems generally aren’t efficient yet in recognising voice modifications, according to new research from the University of Eastern Finland. The vulnerability of speaker recognition systems poses significant security concerns.

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Machine Learning Detects Marketing and Sale of Opioids on Twitter

Using advanced machine learning, a cross disciplinary team of University of California San Diego researchers developed technology that mined Twitter to identify entities illegally selling prescription opioids online. Between June and November 2015, some 619,937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone and hydrocodone were collected.

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