Nvidia Corp., a maker of graphics chips frozen out of Apple Inc. computers, has posted job listings that indicate a better relationship with the world's most valuable technology company. Current Mac computers feature graphics chips from Advanced Micro Devices Inc. Nvidia, which is the leading manufacturer of high-end graphics chips used in gaming machines, hasn't been an option for multiple generations of computer models from Cupertino, California-based Apple. Nvidia, in a job ad for a software engineer, said a successful applicant will "help produce the next revolutionary Apple products." The role would require "working in partnership with Apple" and writing code that will "define and shape the future"' of graphics-related software on Macs. There are three current job listings on Nvidia's database referencing Apple, with the latest one appearing last week.

History does not always make things easy for geniuses. When John McCarthy (1927-2011) was born in Boston on the eve of the Great Recession to a humble family of European immigrants, little seemed to presage that this child prodigy was to become a worthy successor to Alan Turing. The delicate health of John's little brother led the McCarthy family, who roamed the country in search of work opportunities, to settle in Los Angeles. It was there that John, a teenager already outstanding in mathematics, came into contact with the California Institute of Technology, Caltech, and taught himself college level mathematics after asking for their used textbooks. The future father of artificial intelligence tried to study while also working as a carpenter, fisherman and inventor (he devised a hydraulic orange-squeezer, among other things) to help his family. When he officially entered Caltech to study mathematics, he had already studied so much on his own that he was allowed to skip the first two courses.

Seven out of ten corporate executives say they are making significantly more investments in artificial intelligence (AI) than just two years ago, according to Accenture's recent Technology Vision survey. And more than half say they plan to use machine learning and embedded AI solutions extensively. The race toward a digital future has begun and within the next five years, mastering the impact of this technology on future strategy will be a critical task for every CEO. Computational speed, machine learning and natural user interfaces have all advanced to the point where computers can do jobs that, previously, only humans could do. Intelligent digital labor is set to spark a radical change in labor dynamics, with research from market analyst firm, Gartner, suggesting that by 2030, virtual talent spending will exceed 10 percent of human staff costs.

Bengaluru: Wipro Ltd expects its artificial intelligence platform Holmes to offset the pricing decline in commoditized outsourcing contracts, a development that could help India's third-largest software services firm arrest falling profitability in the current financial year. "We are currently deploying Holmes very aggressively," K.R. Sanjiv, the chief technology officer at Wipro, said in an interview. "Overall profitability is a function of many things, the pricing, how we structure the contracts. But certainly hyper automation should result in a beneficial impact and you can see the first meaningful impact by the end of March 2017." The comments should bring some relief to Wipro investors.

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft. Online transaction processing (OLTP) database applications have powered many enterprise user-cases in recent decades, with numerous implementations in banking, e-commerce, manufacturing and many other domains. Today, I'd like to highlight a new breed of applications that marry the latest OLTP advancements with advanced insights and machine learning. In particular, I'd like to describe how companies can predict a million events per second with the very latest algorithms, using readily available software. Take credit card transactions or loan applications, for instance.

HYDERABAD: Apple quietly acquired a little-known Hyderabad-based startup a few months ago for an undisclosed amount and while little is known about what Tuplejump does, the word is that this is part of the Cupertino-based giant's exploration into artificial intelligence. Known in the trade as an acqui-hire, nearly all of Tuplejump's 16 employees are in the process of becoming Apple staffers. A back-of-the-envelope calculation by an expert put the valuation at about 20 million (Rs 27 crore). "When there is a talent hire, the cost of hiring is usually calculated based on the last few years' salary," the expert said. "So, for a 16-member team, I think it would probably come up to around 4 million a year -- making this deal for something around 20 million (including for possible IP evaluation)."

According to a recent press release, "Cognonto, a new start-up in knowledge-based artificial intelligence (KBAI), announced today the dual release of its Cognonto Platform and KBpedia, a computable knowledge structure to automate much of the effort needed for machine learning. KBpedia leverages six large-scale knowledge bases -- Wikipedia, Wikidata, GeoNames, OpenCyc, DBpedia and UMBEL -- into a single structure expressly designed to support artificial intelligence (AI) within enterprises." Michael Bergman, a co-founder of Cognonto, commented, "Many of the AI advances in recent years, such as question answering on smart phones or systems that beat human contestants in Jeopardy, are built around Web knowledge bases like Wikipedia… But these are one-off systems that only the largest tech firms or research outfits can afford… The idea behind Cognonto is to democratize this process such that any enterprise can afford to train their own machine learners or gain the advantages of knowledge-based artificial intelligence." The release continues, "KBpedia combines the hundreds of thousands of concepts and 20 million entities in its source knowledge bases in a structure that separately captures entities, attributes, relations and topics, the types by which they are categorized, and the connections between them. One innovation of the system, according to Bergman, is the schema, or "knowledge graph," that organizes KBpedia according to the logic of Charles Sanders Peirce, a noted 19th century American mathematician, philosopher and polymath."

Google's giant white Internet-delivering balloons have been floating through the skies for several years now. And because they've been collecting data on how best to pilot them all the while, the company has now decided to let artificial intelligence take control. Wired reports that the engineering team behind Google's Project Loon is going to move away from using control algorithms that are hard-coded, and instead use machine learning to understand how best to stick to a desired flight path. Navigating a balloon through the stratosphere autonomously is a very different task from getting a car to drive through a city by itself. For starters, there's only one way to control the balloons: pump a little extra air in or out in order to have the balloon rise or fall. But an AI system can keep analyzing new data to improve how to react to conditions in a way that a hard-coded algorithm can't, comparing the actual flightpath to the intended one for a given decision.

Wave Computing was founded with the vision of delivering deep learning computers with game-changing computational performance and energy efficiency. Their objective is to enable businesses to analyze complex data in real-time with more accurate results through a fluid discovery and improvement in Deep Neural Network (DNN) development and training with our family of computers. Wave developed a novel Dataflow Processing Unit (DPU) architecture as part of a strategy to natively support a new wave of dataflow model based deep learning frameworks such as Google's TensorFlow and Microsoft's CNTK. Wave's family of deep learning computers achieves its best-in-class DNN training and inference performance through its native support of dataflow model based deep learning frameworks, its CPU-less high bandwidth shared memory architecture, and DPU's 16,000 parallel processing elements power and massive memory bandwidth. This results in a family of computers that delivers more than 10x improvement in compute performance for DNN training and more than 100x improvement in performance for DNN inference.

It's being used to optimize complex chips, balance power and performance inside of data centers, program robots, and to keep expensive electronics updated and operating. What's less obvious, though, is there are no commercially available tools to validate, verify and debug these systems once machines evolve beyond the final specification. The expectation is that devices will continue to work as designed, like a cell phone or a computer that has been updated with over-the-air software patches. But machine learning is different. It involves changing the interaction between the hardware and software and, in some cases, the physical world. In effect, it modifies the rules for how a device operates based upon previous interactions, as well as software updates, setting the stage for much wider and potentially unexpected deviations from that specification. In most instances, these deviations will go unnoticed.