Many applications in Bayesian statistics are extremely computationally intensive. However, they are also often inherently parallel, making them prime targets for modern massively parallel central processing unit (CPU) architectures. While the use of multi-core and distributed computing is widely applied in the Bayesian community, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs. Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs). Since the effective utilisation of GPGPUs typically requires specialised programming languages, such technologies are not ideal for the wider Bayesian community. In this work, we practically demonstrate, using standard programming libraries, the utility of the SIMD approach for several topical Bayesian applications. In particular, we consider sampling of the prior predictive distribution for approximate Bayesian computation (ABC), and the computation of Bayesian $p$-values for testing prior weak informativeness. Through minor code alterations, we show that SIMD operations can improve the floating point arithmetic performance resulting in up to $6\times$ improvement in the overall serial algorithm performance. Furthermore $4$-way parallel versions can lead to almost $19\times$ improvement over a na\"{i}ve serial implementation. We illustrate the potential of SIMD operations for accelerating Bayesian computations and provide the reader with essential implementation techniques required to exploit modern massively parallel processing environments using standard software development tools.

Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates. Differently from existing methods, we make use of the most recent first moment of gradients to compute the individual learning rates per iteration. The motivation behind it is that the dynamic variation of the first moment of gradients may provide useful information to obtain the learning rates. We refer to the new method as the rapidly adapting moment estimation (RAME). The theoretical convergence of deterministic RAME is studied by using an analysis similar to the one used in [1] for Adam. Experimental results for training a number of DNNs show promising performance of RAME w.r.t. the convergence speed and generalization performance compared to the stochastic heavy-ball (SHB) method, Adam, and RMSprop.

Like early mammals scuttering between the legs of tyrannosaurs, a lot of little companies are trying to weave around--and maybe even outlast--the big boys of self-driving technology. One such example is Perrone Robotics, a small Virginia company that has developed a self-driving package that it says can be quickly adapted to any vehicle. This Swiss Army knife of an AI can give smarts to an existing car, shuttle bus, or truck--even the gargantuan trucks used in mining. Tiny shuttles and behemoth trucks sell in small numbers, and equipping them to drive themselves is beneath the dignity of major players, like Alphabet's Waymo and General Motors' Cruise Automation. "What we're doing, certainly Waymo and GM Cruise could do, but they are focused on their own agenda. This is our niche, and we are going where we can add real value," says David Hofert, the chief marketing officer at Perrone Robotics.

Think of a university campus: it has its own roads, shops, residential areas, banks and transport links. It may be visited by tens of thousands of people each day. It is, in effect, a tiny city. Across the globe, these mini metropolises are increasingly opting for a smart city approach. This is a tech-driven model that's used in places such as Barcelona, where street lamps react intelligently to surroundings to save energy; Seattle, where smart traffic lights respond to the conditions on the road; and even Milton Keynes, which has a real-time "data hub" sharing information about the town's energy and water consumption, transport, weather and pollution.

In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors. In the United States, where the researchers had economic data from the U.S. Census, they also found that people across socioeconomic lines put similar efforts into interior decoration. "We were interested in seeing how other cultures decorated," said Clio Andris, assistant professor of geography, Penn State and an Institute for CyberScience associate. "We see maps of the world and wonder, 'What's it like living there,' but we don't really know what it's like to be in people's living rooms and in their houses. This was like people around the world inviting us into their homes."

This is something that's been on my mind for a while, and it's been hard to shake - even in beautiful New Zealand. Thought I'd use some of the New Year's energy to write it up. It's a bit long - despite my efforts to get it down to a manageable size, but let's start somewhere. There's a scene in the movie "I, Robot", inspired by Asimov's series of the same name, where the titular robot tells the main character that he cannot create a work of art. He does this while creating a rather striking sketch that most humans would be happy to have been the creator of. This movie stands out in my memory as unique because it briefly touches on what the purpose of existence might be to an artificial mind, unlike most I've seen. Intelligences in popular culture are often portrayed as villains, and even the ones on the side of humanity seem far too concerned with the same things we are - domination, power, control, even glimpses of happiness - that I'm given to wonder if it's been given any serious thought. It's something that's been stuck in my mind for a while, but I have to admit I haven't made any significant progress. That said I'm hoping I can repeat some of the questions I've had and wonder about what the answers might be without making myself any more lost than I already am. To start, let's consider what I think is the popular perception of AI. Before we do so, I'd like to borrow some terminology to define what I'm talking about as an Artificial General Intelligence (AGI), also called a strong AI. We'll explore the meaning of this term later, but for now let's consider it to be a general mind that can perform any intellectual task a human being can. This definition if you'll notice does not require consciousness or sentience, each of which is a concept just as complicated if not more. For now, let's forget about what an Artificial SuperIntelligence would be - which in my mind is an advanced AGI - and use the term'intelligence' and'artificial intelligence' to refer to an AGI.

The hardware segment is projected to witness the highest growth rate during the forecast period. Algorithm Segment Review Based on algorithm, it is classified into deep learning, querying method, natural language processing, and context aware processing. The deep learning segment is projected to grow at the highest CAGR during the forecast period, owing to increase in use of signal reduction, data mining, and image recognition, which are integral components of most AI protocols. Global AI in healthcare Market: Key Geographic Segment Based on region, the AI in healthcare market is divided into North America, Europe, Asia-Pacific, and LAMEA. North America accounted for the largest market share in the AI in healthcare market in 2016, and is expected to retain its dominance throughout the forecast period.

India could emerge as the third-largest market in the Asia-Pacific (APAC) region for IBM's artificial intelligence (AI)-powered workforce automation solution, launched in November last year. The Armonk-based software services giant expects large-sized and mid-sized enterprises from sectors such as banking, insurance and manufacturing to be among the first adopters of the solution. The solution, dubbed the Talent and Transformation suite of services, is one among several that have come out of IBM's global AI platform, Watson. "India is one of the largest markets for the solution in terms of opportunity after Australia and Singapore (in the APAC region)," Lula Mohanty, general manager for APAC at IBM Global Business Services, told TechCircle. "Only five per cent of chief executive officers (CEOs) think that they have embarked on a transformation journey, especially when it comes to human resources core functions and only 24% of CHROs (chief human resources officers) think that they have a lot of work to do in terms of improving their core functions. This is a positive change in terms of rising awareness in the country," she added.

Demand forecasting, for all of its importance in business, has had a mixed run in retail. Even in fairly predictable categories in general merchandise, it's far too easy for retailers to start the current year's plan by loading in all the assumptions made from the year before, rather than starting clean with a new demand forecast. In fact, according to RSR Research's benchmark, even though 68% of better-performing retailers ("Retail Winners") and 53% of all other retailers believe that starting with a demand forecast as the basis for the next year's plan is very valuable, only 49% of Winners and 29% of their peers actually do so today. Part of the reason why is because forecast error in retail is high, as high as 32% according to some estimates. And, the more sporadic or non-repeatable the demand is, the more forecast error occurs – thus, grocery retailers operating a replenishment strategy have a far easier time using a forecast than a fashion retailer introducing a high-fashion item that responds to a new trend. Additionally, not all products face the same demand profiles.

First responders will be able to use drones equipped with Augmented Reality technology to better deal with emergency situations. Drones have been getting a really bad rep of late, specially in the United Kingdom, after rogue operators managed to shut down operations at both Gatwick and Heathrow airports, effectively ruining Christmas for thousands of travellers and prompting widespread clamour for greater regulation against them. Yet like all technology, it's not the tech itself, but what you do with it that counts, and which makes it a force for evil – or for the greater good. The other side of all the fear and annoyance that drones can cause in the wrong hands are the life-saving applications that companies like Edgybees are working on. Edgybees was initially founded as AR video game enhancement software, then pivoted to specialize in rescue drone technology that collects geospatial data and overlays information onto video feeds to bring emergency responders accurate and real-time information.