Artificial intelligence and machine learning must be judiciously used, such as when monitoring internet of things devices, says David De Roure, professor of e-research at the University of Oxford, who offers insights on IoT risk management. "We can use AI and ML to improve the resilience of our systems and improve the responsiveness to the changing circumstances," De Roure says in an interview with Information Security Media Group during IoT India Congress held in Bengaluru. "But every time we add anything to these already complex systems [for IoT devices], we are introducing new complexity, new vulnerabilities, new opportunities both for failure and for susceptibility to attack." De Roure is a professor of e-research at the University of Oxford. From 2009 to 2013 he held the post of National Strategic Director for e-Social Science and was subsequently a Strategic Advisor to the UK Economic and Social Research Council.

Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.

For his next book, Joseph LeDoux knew he had to go deep. He had to go back in time, way back, 3.5 billion years ago. The author of the seminal The Emotional Brain, followed by Synaptic Self and Anxious, sensed a missing element in those books on how brain anatomy and function shape human behavior and emotions. In his new book, The Deep History of Ourselves: The Four-Billion-Year Story of How We Got Our Conscious Brains, LeDoux takes readers back to the emergence of life on Earth to show what our protean brains today owe to the canny survival of Protozoa. "I started asking, 'How far back in evolution does the ability to detect and respond to danger go?'" he said to me in a recent interview at his home in New York City. LeDoux directs the Emotional Brain Institute at New York University. In his research and previous books, he has shown the human brain processes that detect and respond to danger differ from the conscious experiences of fear itself. "I felt I needed to understand more about this process," he said.

And so, you want your teams to reflect the humans that you are going to be serving. So, for us, we want our team to reflect the customer population of our robots because we want to be able to ask the right questions. We want to ask the questions that our customers are asking. We don't want to ask the questions that nerds like me want to know. I have a very specific set of things that I would love our robots to have, which you know, big whoop.

Justin Boutilier uses optimization and machine learning to improve healthcare access, delivery and quality, particularly in low- and middle-income settings. As a second-year PhD student at the University of Toronto, Justin Boutilier spent four weeks in Dhaka, Bangladesh, investigating ways to curb ambulance response times in the bustling capital of a developing country. He quickly got a firsthand look at the scope of the challenge: The roughly 10-mile trip from his hotel to meetings in the city took about three hours. "You could walk faster," he says, "but there's no sidewalk, so it's kind of dangerous." Boutilier, who has joined the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison as an assistant professor, uses optimization and machine learning to improve healthcare access, delivery and quality, particularly in low- and middle-income settings.

What Do You Make of the Results?

The 2019 World Artificial Intelligence Conference (WAIC) is being held in China's economic hub Shanghai from Thursday to Saturday in a bid to boost AI cooperation and innovation globally. With the theme of "Intelligent Connectivity, Infinite Possibilities," this year's event will focus on AI-enabled high-quality development to cope with the common problems in human development and to create a better life for mankind. Two summit forums are scheduled to be held on Thursday, where governors, representatives from international organizations, leading scientists and entrepreneurs discuss the development of the AI industry and the frontier of science to seek high-quality development, application and governance. Over 50 percent of those guest speakers on the forums are from overseas. Twelve themed forums will also be held covering topics such as education, intelligent algorithm, chip and smart hardware, unmanned driving and 5G AI.

After a career as a film composer in L.A., Drew Silverstein moved to New York where he co-founded Amper Music. To combine the highest levels of artistry with groundbreaking artificial intelligence technology to empower anyone to create unique music, instantly. In 2017, Amper raised $4 million in seed funding and is now at the cutting edge of the race to crack AI music. Hot Press caught up with the Amper CEO in Rome, after his recent TEDx Talk, to find out what AI means for our future music consumption, how it will affect our approach to songwriting, how to collaborate with a digital version of yourself, the exploitation of intellectual property by Facebook and Google, the probability of robot composers with feelings, and why he predicts AI to be the greatest creative revolution in the history of music. MARK HOGAN: What is Amper? DREW SILVERSTEIN: Amper is an AI composer, performer and producer that creates unique and professional music in a matter of seconds. The music can be tailored to content or it can be standalone. Our mission is to enable anyone around the world to express themselves creatively through music regardless of their background, expertise or access to resources. Because fundamentally, every person is creative – by the fact that we're people. But just being creative doesn't mean we have the ability to express our creativity. Singing in the shower is easy.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

Fluid Imaging Technologies and the University of Colorado Boulder have recently entered an exclusive research agreement to determine whether the University's artificial intelligence software can identify bloodborne bacteria. The collaboration will establish training set data from microscopy images for the top ten most wanted bacterial strains causing blood infections, and then train a computer to identify the bacteria automatically in tandem with Fluid Imaging Technologies' FlowCam oil immersion flow imaging microscopes. We spoke with Kent Peterson, CEO, Fluid Imaging Technologies, to learn more about the collaboration, the technologies involved, and how they could impact patients. Anna MacDonald (AM): Can you give us a little background to how the collaboration between the University of Colorado and Fluid Imaging Technologies has come about? Kent Peterson (KP): Dr. Ted Randolph had been using the FlowCam instrumentation for years.