FRANKFURT (Reuters) - Moscow-based Internet firm Yandex launched a $160 smart speaker on Tuesday to work with its digital assistant'Alice', becoming the latest challenger to take on the leading voice-activated home helpers from Silicon Valley.

PARIS - A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against machine in the quest for better, faster diagnostics, researchers said Tuesday. A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The machine -- a deep learning convolutional neural network or CNN -- was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at'expert' level with more than five years of experience, 19 percent had between two and five years' experience, and 29 percent were beginners with less than two years under their belt. 'Most dermatologists were outperformed by the CNN,' the research team wrote in a paper published in the journal Annals of Oncology.

Accountancy, law, insurance… these professions usually conjure images of spreadsheets, glass and steel offices, jargon-filled sentences, square eyes and grey suits. Perhaps this is part of the reason why we willingly pay high fees for such services: passing the burden to highly intelligent individuals who will perform those laborious tasks for us. But all this might be about to change. Machine learning and artificial intelligence solutions are already better at performing many tasks than the professionals: beating human lawyers in reviewing contracts and predicting the outcomes of legal cases[1]. But it is not about replacing lawyers, accountants and insurance managers with machines.

Australian researchers have praised a computer that has been hailed for being better than an international team of specialists at detecting skin cancer. Scientists from Germany, the United States and France developed an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The computer was found to offer more accuracy and fast diagnostics than 58 dermatologists from 17 countries, when shown photos of malignant melanomas and benign moles. Scientists from Germany, the U.S. and France developed an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images On average, flesh and blood dermatologists accurately detected 86.6 percent of skin cancers from the images, compared to 95 percent for the machine, known as a convolutional neural network or CNN. Australian experts Victoria Mar, from Melbourne's Monash University, and Peter Soyer from the University of Queensland said it was a major breakthrough in detecting skin cancers.

PARIS – A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against machine in the quest for better, faster diagnostics, researchers said Tuesday. A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images. The machine -- a deep learning convolutional neural network or CNN -- was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles. Just over half the dermatologists were at "expert" level with more than five years of experience, 19 percent had between two and five years' experience, and 29 percent were beginners with less than two years under their belt. "Most dermatologists were outperformed by the CNN," the research team wrote in a paper published in the journal Annals of Oncology.

The report comes from McKinsey and it is part of an evolving research project on the influence of new technology on the economy, business, employment, and society. The research assesses changing skills and forms of employment, as measure against core workplace skills across key European countries: France, Germany, Italy, Spain, and the United Kingdom, plus the U.S. The aim is to trend shifts over time and detect new patterns of working and types of employment. The rise of automation and the economic and employment effects divide opinion. According to PwC analysis, artificial intelligence, robotics and other forms of smart automation can deliver great economic benefits, perhaps contributing up to $15 trillion to global GDP by 2030. Alternative arguments indicate that deskilling of work is likely, a loss of jobs and a rise in unemployment. Indeed the Governor of the Bank of England, Mark Carney has warned of robots taking jobs leading to rise in the idea of Marxism and'labor process theory'.

Artificial intelligence is one of the those wonky topics that tech geeks salivate over but spend little time dissecting. Its usually referenced as a simple programming tool called deep learning, which trains robots in a given task by introducing voluminous amounts of data, or as a scary, existential threat to mankind. In our second episode of Season 3, "Who's Afraid of AI?" we explore this technology that's affecting nearly every aspect of the auto industry and beyond. Host Shiraz Ahmed interviews Maya Pindeus, the 27-year-old CEO of Humanizing Autonomy, an AI startup focused on human-machine interactions with autonomous cars. Pindeus met a Daimler executive at Ars Electronica, a conference that focuses on the nexus of arts and technology, in Austria, and later began collobarating on a project.

Lt. Col. Stanislav Petrov settled into the commander's chair in a secret bunker outside Moscow. His job that night was simple: Monitor the computers that were sifting through satellite data, watching the United States for any sign of a missile launch. It was just after midnight, Sept. 26, 1983. A single word flashed on the screen in front of him. The fear that computers, by mistake or malice, might lead humanity to the brink of nuclear annihilation has haunted imaginations since the earliest days of the Cold War.

Artificial intelligence is better than doctors at spotting skin cancer, a study has shown. A Google algorithm devised to recognise unusual moles not only picked up more cancers but also ruled out more benign lesions. Experts are increasingly optimistic that within a few years machine learning will automate many diagnoses, helping doctors to become more efficient and reducing mistakes. Last week Theresa May said AI that harnessed genetic information and medical records to spot disease early was crucial to Britain's future prosperity. The latest study, led by Professor Holger Haenssle, of the University of Heidelberg, is one of the first in which AI convincingly comes out on top against specialists in spotting cancer.

The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. However, the current justification is intuitive rather than analytical. This work proposes one minor modification to the normal ensembling methodology, which we prove allows the ensemble to perform Bayesian inference, hence converging to the corresponding Gaussian Process as both the total number of NNs, and the size of each, tend to infinity. This working paper provides early-stage results in a reinforcement learning setting, analysing the practicality of the technique for an ensemble of small, finite number. Using the uncertainty estimates they produce to govern the exploration-exploitation process results in steadier, more stable learning.