They say'beauty is in the eye of the beholder', but in a new event the beholders are robots. The Beauty.AI beauty contest used five algorithms to evaluate youthfulness, face symmetry, skin and other parameters, and then compare them to models and actors in a database. Now, the systems have announced the winners from more than 6,000 user-submitted selfies of individuals who live all over the world and range in ages of 18 to 69. The Beauty.AI beauty contest put together of robot judges to determine the winners. More than 6,000 people from around the world submitted head shots to be analyzed by the algorithms.

Scientists and engineers around the world are quietly working on the next generation of machines which could usher in a new age of computing. Even the fastest supercomputers today are still bound by the system of 1's and 0's which enabled the very first machines to make calculations. But experts believe that drawing on the strange properties of the quantum world can enable computers to break free from these binary shackles, creating the most powerful problem-solving machines on the planet. Now, according to New Scientist, researchers at Google may announce a breakthrough as soon as next year, potentially reaching what the company terms'quantum supremacy' before anyone expected. Despite the great promise, one of the major stumbling blocks for the development of quantum computers has been demonstrating they can beat classical computers – achieving what computer scientists call quantum supremacy.

While Sony is currently one of the leading producers of smartphones, cameras and home entertainment systems, the company may soon be heading into the realm of robotics and AI. On Thursday, Kazuo Hirai, CEO of the Tokyo-based company, took to the stage at the IFA electronics show in Berlin to discuss the firm's newest products. He said that Sony was keen to explore new areas of technology, and that artificial intelligence and robotics were part of that. Kazuo Hirai, CEO of Sony said that the company was keen to explore new areas of technology, and that artificial intelligence and robotics were part of that. Sony's plans to get back into consumer robotics include efforts to produce a machine capable of'forming an emotional bond' with users.

Determining optimal well placements and controls are two important tasks in oil field development. These problems are computationally expensive, nonconvex, and contain multiple optima. The practical solution of these problems require efficient and robust algorithms. In this paper, the multilevel coordinate search (MCS) algorithm is applied for well placement and control optimization problems. MCS is a derivative-free algorithm that combines global and local search. Both synthetic and real oil fields are considered. The performance of MCS is compared to generalized pattern search (GPS), particle swarm optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES) algorithms. Results show that the MCS algorithm is strongly competitive, and outperforms for the joint optimization problem and with a limited computational budget. The effect of parameter settings for MCS are compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.

Google's DeepMind's Health initiative will soon work to explore how artificial intelligence could save hours and hours of precious time in the treatment of oral, head and neck cancers, the company announced on Tuesday. Whatever it is Zuckerberg is calling his personal home AI system. Yesterday he updated an audience in Rome about his personal project this year. He had Facebook (FB) engineers work on a computerized domestic assistant as his personal challenge. The AI controls his AC, lighting, opens his security gate, and can even make him toast.

Uber announced Thursday that it will launch a pilot program with self-driving cabs in the Pittsburgh area. NHTSA's former administrator is celebrating the announcement, but says regulatory challenges exist before self-driving cars are common. David Drummond, one of Alphabet's most senior executives, announced that he was stepping down from Uber's board just as the ride-hailing company announced its intention to dive into self-driving cars - a longtime R&D project of Alphabet's Google. SAN FRANCISCO - If you need a sure sign that the race to develop self-driving cars is no longer just an interesting science project, David Drummond's departure from Uber's board is it. Drummond, an early employee of Alphabet who oversees corporate development and its investment arm for the parent of Google, stepped down Monday after two years of board service Uber. Prior to both companies confirming the departure, tech news site The Information reported that Drummond had been kept out of recent board meetings.

Nvidia has been a leader in producing the technology behind high-quality graphics for years, but the company is now betting on a different future. With the rapid advances in self-driving vehicles, warehouse robots, diagnostic assistants, and speech and facial recognition, there's plenty of reasons for companies to be excited about deep-learning-based artificial intelligence (AI). Nvidia is one such technology firm that is extremely bullish in this space. In a recent conversation with CXOtoday, Vishal Dhupar, Managing Director-South Asia, Nvidia said that the company is moving ahead with its AI-focused hardware, software, and solutions for the enterprise. As such data scientists in both industry and academia have been using graphics processing units (GPUs) for machine learning to make groundbreaking innovations across a variety of applications including image classification, video analytics, speech recognition and natural language processing or NLP. In particular, Deep Learning, the use of sophisticated, multi-level "deep" neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data, is an area where Nvidia is significantly investing in recent quarters.

It is already changing the face of warface, with AI adversaries and electronic spies taking centre stage. However, America has fallen catastrophically behind in the hi-tech battlefield, a new report has claimed. The Defense Science Board's report into autonomy concluded'there are both substantial operational benefits and potential perils associated with its use,' and called for immediate action. The Defense Science Board's report into autonomy concluded the DoD must accelerate its exploitation of autonomy to remain ahead of enemies. 'This study concluded that DoD must accelerate its exploitation of autonomy--both to realize the potential military value and to remain ahead of adversaries who also will exploit its operational benefits.'

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has developed an artificial intelligence (AI) method, aimed at training computers to interpret pathology images. The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep multi-layer convolutional network. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 per cent of the time. One of the researchers, Aditya Khosla, said, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "In our approach, we started with hundreds of training slides for which a pathologist has labeled regions of cancer and regions of normal cells," said Dayong Wang.

RAGE AI significantly extends the frontier of deep learning and machine intelligence technology from "natural language processing" to "natural language understanding." RAGE AI incorporates deep linguistic parsing and proprietary innovations to understand meaning in context, which makes its solutions completely transparent, auditable and flexible. The platform facilitates unsupervised to supervised learning and contains several innovations to support automated knowledge acquisition including pragmatic knowledge. RAGE AI is not a black box and does not rely on statistical patterns present in training data. Introduced in 2011, RAGE AI is an integral part of the broader RAGE Enterprise platform, a provider of all process orchestration and automation capabilities.