TAIPEI (Reuters) - Nvidia Corp chief executive Jensen Huang said on Thursday artificial intelligence would enable fully automated cars within 4 years, but sought to tamp down expectations for a surge in demand for its chips from cryptocurrency miners.

The idea of smart cities – infrastructure interlinked by software – isn't new, but it's undeniably cool. Who wouldn't want to live somewhere where programs use data and evidence, not intuition, to actively improve their day-to-day lives? Now imagine that an entire smart city actually exists, but it's even more advanced than you could possibly imagine, where infrastructural systems are altered on the fly by an artificial intelligence (AI). This may sound futuristic, but one such place can already be found in China. As reported back in October 2016, the government of the city of Hangzhou – home to over 9 million people – collaborated with Alibaba and Foxconn to build the "City Brain" project.

Robots will be 100 times more intelligent than the average human in 30 years, the CEO of tech giant SoftBank has claimed. Billionaire tech mogul Masayoshi Son, 60, said that by 2047 artificial intelligence (AI) will have reached an IQ of 10,000. By comparison, the average human IQ is 100, while anything over 140 is a'genius' score. Mensa, the'high IQ society', only accepts members with a score above 130. Speaking at the Future Investment Initiative in Riyadh, Saudi Arabia, on Wednesday, Mr Son said: 'This is the first time ... the tool becomes smarter than ourselves.'

Ma believes that companies must be prepared for decades of pain to come to terms with the advance of robots. This sentiment was reinforced by the president of the World Bank (https://www.weforum. So are all jobs at risk? But the factors impacting jobs will be many (see chart). Countries will have to navigate carefully, to find ways to ensure that job formation efforts do not flag.

Octavia, a humanoid robot designed to fight fires on Navy ships, has mastered an impressive range of facial expressions. When she's turned off, she looks like a human-size doll. She has a smooth white face with a snub nose. Her plastic eyebrows sit evenly on her forehead like two little capsized canoes. When she's on, however, her eyelids fly open and she begins to display emotion.

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.

Amazon Web Services is the first cloud to launch new compute instances that allow developers to build applications that tap into Nvidia's new generation of Volta GPUs, which are designed to provide high-performance acceleration for applications like AI computation. Companies all over are turning to machine learning to help propel their businesses, but building new models often requires a great deal of computation. The Volta is supposed to be a good deal faster at that than past generations of Nvidia's silicon, and making it available through Amazon's cloud means that companies will be able to get started using them right away. Customers will be able to run instances with up to 8 V100 GPUs, which will be made available initially from AWS's Northern Virginia, Oregon, Ireland, and Tokyo datacenters. Nvidia launched a new GPU Cloud offering alongside AWS, which is designed to provide companies with the most optimized environment for running deep learning applications on top of the company's hardware in a public cloud.

Superstore The staff finds a body in the store on Halloween, prompting a variety of reactions from workers and customers. Supernatural Loretta Devine returns in her guest role as a psychic from Kansas, who turns to Sam and Dean (Jared Padalecki, Jensen Ackles) for help after a wraith (guest star Jon Cor) murders one of her friends and may be targeting her granddaughter (guest star Clark Backo), who shares her gifts. Grey's Anatomy A flashback to war-torn 2007 Iraq fills in some important blanks about events leading up to the kidnapping of Megan (Abigail Spencer), Owen's (Kevin McKidd) younger sister, who had been missing for 10 years. Gotham Gordon and Bullock (Ben McKenzie, Donal Logue) pursue a serial killer who has been murdering cops. The Good Place Michael (Ted Danson) needs Janet's (D'Arcy Carden) help to determine the cause of a new glitch before it gets out of control.

Conjugate gradient methods are a class of important methods for solving linear equations and nonlinear optimization. In our work, we propose a new stochastic conjugate gradient algorithm with variance reduction (CGVR) and prove its linear convergence with the Fletcher and Revves method for strongly convex and smooth functions. We experimentally demonstrate that the CGVR algorithm converges faster than its counterparts for six large-scale optimization problems that may be convex, non-convex or non-smooth, and its AUC (Area Under Curve) performance with $L2$-regularized $L2$-loss is comparable to that of LIBLINEAR but with significant improvement in computational efficiency.

In this study, we propose a novel deep neural network and its supervised learning method that uses a feedforward supervisory signal. The method is inspired by the human visual system and performs human-like association-based learning without any backward error propagation. The feedforward supervisory signal that produces the correct result is preceded by the target signal and associates its confirmed label with the classification result of the target signal. It effectively uses a large amount of information from the feedforward signal, and forms a continuous and rich learning representation. The method is validated using visual recognition tasks on the MNIST handwritten dataset.