History exists so that man could learn from his mistakes, It's been around five years have passed since Microsoft's $7 billion ill-fated acquisition of Nokia's smartphone business, and now after learning too many lessons, both the tech champions are coming together yet again. Microsoft has announced a strategic collaboration with Nokia to accelerate transformation and innovation across industries with Cloud, Artificial Intelligence (AI) and Internet of Things (IoT). "Bringing together Microsoft's expertise in intelligent cloud solutions and Nokia's strength in building the business and mission-critical networks will unlock new connectivity and automation scenarios," Microsoft Azure Executive Vice President Jason Zander said in a statement. "We're excited about the opportunities this will create for our joint customers across industries." The new partnership combines Microsoft's expertise in Cloud Computing and Artificial Intelligence with Nokia's 5G private wireless and mission-critical networking prowess.
Trash talk has been part of sport and human competition for as long as people have been competitive, but now robots are getting in on the game. Researchers from Carnegie Mellon University, in Pittsburgh, Pennsylvania, programmed a robot called Pepper to use mild insults such as'you are a terrible player' and'your playing has become confused'. It would then use these insults while challenging a human to a game called'Guards and Treasures' that is designed to test rationality. Even though the robot used very mild language, the human player's performance got worse while they were being insulted, according to lead author Aaron M. Roth. The team say tests like this could help work out how humans will respond in future if a robot assistant disagrees with a command, such as over whether to buy healthy or unhealthy food.
AI is predicted to add up to $15.7 trillion by 2030, but three main aspects prevent successful adoption within companies. Delivering a keynote speech at the recent Big Data LDN conference, IBM's general manager of Data and Watson AI, Rob Thomas, discussed three main factors that often befall plans to implement AI. Describing AI's potential as "the greatest opportunity we'll ever see in our lifetimes", Thomas suggested the following: The potential that AI has is by no means lost on business leaders. According to a survey by MIT, 85% see AI as an opportunity to gain value from company data. However, decision-makers often struggle to get the best out of AI due to uncertainty surrounding how it would fit into business practices.
Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing, and complex games such as Go. These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more.
New research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) examines the problem of how a self-driving car can predict the behavior of other drivers on the road. This prediction requires a degree of social awareness which is difficult for machines, so the researchers took tools from social psychology to help the system classify driving behaviors into either selfish and selfless. The system observed human driving behaviors and was then able to better predict the movements of other cars when it came to merging lanes or making unprotected left turns, with 25 percent greater accuracy than previously. This kind of insight into human behavior is important for safety when autonomous and human drivers are sharing the road. An Uber self-driving car which struck and killed a pedestrian last year, for example, didn't have the ability to recognize jaywalkers.
Data-driven AV is nothing new. For the past five years, AV endpoints have been migrating to the network with the promise of capturing room metrics and system analytics. The cloud, 5G's lightning-fast connectivity, IoT sensors, and artificial intelligence (AI) are converging to capture more valuable data in more locations. Some AV professionals believe this confluence will usher in a new era of design, one in which systems are built with learned user behaviors in mind. AI is already making an impact in higher education.
The need for convergence of people, process and technology in modern business has ignited the evolution of newer engineering methodologies. Artificial Intelligence (AI) is no exception. It demands even greater interaction of human and non-human resources in the production processes. AI solutions are built on the basis of an algorithm, data and the continuous learning process. Constantly growing data has enriched the quality of the knowledge and increased computing power has extended machine learning into deep learning; together, our collective ability to quickly evolve an AI solution has improved.
As far as the ongoing debate over who is going to be most impacted by the AI trend -- blue-collar or white-collar workers -- the answer, as it turns out, is white collar, says new research from the Brookings Institution. "Better-paid, white-collar or office occupations may be most exposed to AI," Brookings said in summarizing the major findings of a report set to be published today (Nov.
Artificial Intelligence (AI) is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own. We take for granted how our brains effortlessly calculate the world around us, every second of every day. AI is the concept that a computer can do the same. While AI is the broad science of immolating human learning, machine intelligence (MI) is a specific subset of AI that trains the machine how to learn from data.
HPC and AI in a phone booth: ScaleMatrix and Nvidia announced today at the SC19 conference in Denver a joint offering that puts up to 13 petaflops of Nvidia DGX-1 compute power in an air conditioned, water-cooled ScaleMatrix Dynamic Density Control (DDC) "clean room" cabinet. Built for modular deployments and designed for high-demand AI workloads, ScaleMatrix said its ruggedized cabinet can be erected "anywhere power and a roof exist," and it includes biometric security and fire suppression. At the high end of the product line is a composable SKU comprised of the Nvidia DGX-1 system, a single rack running at 42kW, containing 13 DGX-1 units and delivering 13 Pflops of throughput. Other configurations come with a DGX POD deployment, four DGX-2s, run at 43kW and deliver 8 Pflops of compute, the companies said. The units will be sold with storage and networking following DGX POD reference architecture designs, such as NetApp's ONTAP AI solution.