Artificial intelligence software that can predict when a heart is going to fail by analysing scans of it beating has been developed by British scientists. The technology could help doctors to identify patients at greater risk of death sooner and provide more intensive treatment straight away. The machine-learning software, developed by researchers at the MRC London Institute of Medical Sciences (LMS) and Imperial College London, analyses moving images of a patient's heart captured during an MRI scan. It uses advanced image processing to build a digital model which replicates the way more than 30,000 points in the heart contract during each beat. The team fed the system historic data from hundreds of patients.

Tesla won't have to recall any vehicles despite a crash that left a man dead when his car hit a truck. Officials have finally brought a close to a six-month-old investigation that followed the high-profile crash, exonerating Tesla. The controversy and worry emerged after a man's vehicle ran into a truck while using Tesla's "Autopilot" software, which allows to car to operate semi-autonomously. The US National Highway Traffic Safety Administration said that it hadn't found evidence of a defect that would require it to ask for a recall of the cars. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

The "virtuous circle" comprising RPA, machine learning and analytics was the central theme of this month's BotVisions webinar series. Joining me were Kelly Coupe, Principal Product Manager, and Abhijit Kakhandiki, VP of Products, to share their insights into how business users are integrating the execution capabilities of RPA with the cognitive capabilities of machine learning to take the business benefits of automation to the next level. While extremely good at executing specifically defined tasks, RPA tools are limited in the sense that they cannot adjust to new conditions or learn from experience. Machine learning, meanwhile, applies Artificial Intelligence (AI) capabilities to lend business context to the tasks executed by RPA systems, enabling the latter to make better decisions and be more productive. For example, RPA systems can effectively perform many tasks associated with loan origination or account management.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory.

Ben Rossi writes in Information Age about the emergence of educational technology, and how colleges and universities could become more effective with Integrated Learning Systems by using them for more than regurgitating old styles of instruction on new equipment. He writes that ed tech should be more than just an innovative way of educational delivery, but part of the education itself by allowing students and teachers to create their own questions, answers and theories on a variety of elements on a given subject. Technology has the capacity for education to replace the currency of grades and test scores with imagination and creation in action, a necessity for an industry which spent more than $6 billion on teaching technology in 2015. Several colleges and universities are working to reform higher education into spaces for innovation and commercial development. The University of Connecticut, Arizona State University, and Princeton University are among a handful of schools encouraging students to find entrepreneurial niches and to take learning and career passions beyond the classroom.

Every year the US government spends $5 billion on cancer research. And yet more than 8 million people still die every year from the disease--despite the frequent refrain that a cure is just around the corner. Scientists today are exploring more promising new technologies than ever before: whole-genome sequencing, liquid biopsies, mRNA vaccines, AI-powered imaging analysis. But that doesn't necessarily mean they're more promising. No number of flashy new disruptors can fix cancer research's real problem: much of its data can't be trusted, because it was never validated.

A few months ago, Autonomy founder and CEO Mike Lynch sold his company to HP for £7.1 billion. Back in 2000, when he had just become Britain's first software billionaire, Lynch gave an interview in which he talked about perception and explained how he built his company. It was based, he said, on the ideas of a little-known 18th-century clergyman called Thomas Bayes. That was my introduction to Thomas Bayes, whose ideas have been used to solve many intractable problems, a number of which Sharon Bertsch McGrayne studies in depth in The Theory That Would Not Die. In the last ten years, Bayes has become famous, and few working in the field of probability theory, computer intelligence or mathematics can have failed to have come into contact with his rule.

Editor's Note: An update to this article has been posted here on 7/14/04. As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. And they are doing so without relying on the jumble of rule-based techniques common in earlier knowledge representation efforts. These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information. They appear most effective when the semantic distinctions that humans take for granted are crucial to the application's purpose.

John McCarthy, the inventor of programming language Lisp and a pioneer in "artificial intelligence" technology, died Monday night. Mashable reports that McCarthy was also one of the first people to propose "selling computing power through a utility business model," in 1961. Tributes to McCarthy poured in Tuesday, some from posters on Usenet, where McCarthy was an active presence, or from technology writers like Steven Levy, who wrote on Twitter: "Broke news to Siri that John McCarthy... died. She took it well but we humans will miss him." McCarthy's Web site also has a wide following.

ROB MILNE earned international respect for his innovative work in adapting artificial intelligence (AI) as a practical aid to industry and in bridging the gap between the research laboratory and the factory floor. The company he founded at Livingston near Edinburgh, at the heart of the "Silicon Glen", reversed the initials AI to create Intelligent Applications. The projects and software it produced won many awards for innovation and excellence. Milne's devotion to computer technology was matched by a love of mountains and the physical achievement of reaching the highest summits.