Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.
BHP is applying data science to understand how it services machines located across its mines, in the hope of saving $79 million this financial year alone. The miner revealed plans late last year to set up a maintenance centre of excellence (MCoE) based out of Brisbane. The MCoE will standardise maintenance systems and processes for BHP's worldwide operations, replacing the previous model of having 40 different maintenance organisations globally, each with its own way of working. One of the keys to the MCoE model is its reliance on data science techniques, such as machine learning, to understand how maintenance is performed at each site and where improvements can be made. Like other projects since BHP relaunched its technology function at the start of this year, the idea with the MCoE is to create repeatable processes for its business operations across the world.
Nvidia launched a new desktop GPU today that's designed to bring massive amounts of power to people who are working on machine learning applications. The new Titan V card will provide customers with a Nvidia Volta chip that they can plug into a desktop computer. According to a press release, the Titan V promises increased performance over its predecessor, the Pascal-based Titan X, while maintaining the same power requirements. The Titan V sports 110 teraflops of raw computing capability, which is 9X that of its predecessor. It's a chip that's meant for machine learning researchers, developers, and data scientists who want to be able to build and test machine learning systems on desktop computers.
ZDNet's Sandra Vogel posted a formal review of the Huawei Mate 10 Pro, giving it an outstanding 9/10 rating. I've been spending quality time with this business-ready powerhouse and think that the AI found in the camera is worth discussing in a bit more detail. Huawei's partnership with Leica has resulted in some fantastic cameras and performance that is tough to beat. DxOMark awarded the Mate 10 Pro it second highest overall score, 97, and best still image score, 100. Keep in mind, these scores are not scaled to 100.
AVORA, a London-based company that delivers next-generation Business Intelligence (BI) and machine learning as a service, announces that it has raised €1.7 million in funding from institutional and angel investors. New investor Crane Venture Partners joins angel investors Peter Simon, founder of retailer Monsoon, and Steve Garnett, former chairman of Salesforce EMEA. According to analyst firm Gartner, through 2017, a full 60% of big data projects will fail to go beyond piloting and experimentation, and will be abandoned. AVORA was founded by serial entrepreneur Ricky Thomas, who previously established and sold two online companies – DatingUK and PetMeds. After experiencing the data challenges when running businesses firsthand, Thomas developed AVORA, offering a Software as a Service solution that redefines how companies get value from their data.
An artificial intelligence program has become the world's best chess player in just a few hours - and it did it with almost no intervention from humans. AlphaGo Zero, developed by Google subsidiary DeepMind, is a descendant of AlphaGo - the AI program that conquered the human champion of the Chinese board game Go in 2016. After four hours of training, it took on the current world champion chess-playing program, Stockfish 8. Out of 100 games, it won 28 and drew the remaining 72. Even more impressively, it achieved this feat almost completely autonomously. The AI was given a few basic rules, such as how the different chess pieces move, but was programmed with no other strategies or tactics.
On Wednesday, Qualcomm revealed its first concrete details of the Snapdragon 845, the next-generation mobile chip that stands a good chance of being in your next smartphone. The 845 will ship in early 2018, and appear in phones sometime after that. Qualcomm calls the Snapdragon 845 a chip to improve both artificial intelligence and immersion, blending the future of smart devices with the past. At its heart lies the Kryo 385, the semi-custom, upgraded CPU. It's still an eight-core device, with four performance cores running at 2.8GHz and four energy-efficient cores running at 1.8GHz.
As drones and their components get smaller, more efficient, and more capable, we've seen an increasing amount of research towards getting these things flying by themselves in semi-structured environments without relying on external localization. The University of Pennsylvania has done some amazing work in this area, as has DARPA's Fast Lightweight Autonomy program. At NASA's Jet Propulsion Laboratory, they've been working on small drone autonomy for the past few years as part of a Google-funded project. The focus is on high-speed dynamic maneuvering, in the context of flying a drone as fast as possible around an indoor race course using only on-board hardware. For the project's final demo, JPL raced their autonomous drones through an obstacle course against a professional human racing drone pilot.
Google says its AlphaGo Zero artificial intelligence program has triumphed at chess against world-leading specialist software within hours of teaching itself the game from scratch. The firm's DeepMind division says that it played 100 games against Stockfish 8, and won or drew all of them. The research has yet to be peer reviewed. But experts already suggest the achievement will strengthen the firm's position in a competitive sector. "From a scientific point of view, it's the latest in a series of dazzling results that DeepMind has produced," the University of Oxford's Prof Michael Wooldridge told the BBC.
Any sufficiently complicated machine learning system contains an ad-hoc, informally-specified, bug-ridden, slow implementation of half of a programming language.1 As programming languages (PL) people, we have watched with great interest as machine learning (ML) has exploded – and with it, the complexity of ML models and the frameworks people are using to build them. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion, and this brings out many interesting issues in the tools we use to create them – that is, programming languages. While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modelling language (like PyTorch). We'd like to ask – are new ML-tailored languages required, and if so, why?