As reported in First Post, Uber reveals that the Hyderabad-based team will be responsible for fulfilling promises made in October 2019 when Uber Money was launched and in turn, delivering in India. New features include access to real-time earnings with the Uber Debit account, with drivers being offered debit and credit cards. In addition to this, a digital Uber Wallet will help users track their spending history and manage their money. Uber Money will also tackle new payments methods and build financial compliance tools with smart routing, leveraging Uber's artificial intelligence models for intelligent risk decisions. Naga Kasu, engineering director and Hyderabad site lead, says: "The Uber Hyderabad Tech Centre has the best in class fintech talent specialising in risk, payments, financial reporting and analytics platform engineering. "Besides engineering, we are investing in growing and scaling data science, analytics, and product management organisations to transform Hyderabad into a full spectrum tech site for Uber."

The different tools and technologies that we often use when developing software add complexity to our systems. One of the challenges that we face in web development is that we are often writing the backend and the frontend in different languages and type systems. To build reliable applications we need a way to connect both. We need to make sure our applications are reliable and do not break when the API changes. We can build a bridge that helps us to traverse safely between the backend and the frontend. We can do that with a code generation tool that allows us to use the same types on both sides of the application.

The emergence of artificial intelligence (AI) in tandem with the growing volume and complexity of business data means there will be few in the future workforce to have their work untouched by AI. The global AI software market will be an unstoppable force in the coming years, with a 457% leap in market value from 22.6 billion this year to USD$126 billion in 2025, according to Learnbonds. As a result of this, one in five workers in a nonroutine job will rely on AI for at least part of their role. AI can easily bolster business efficiency and quality, and these draws are proving irresistible to many organisations worldwide looking to streamline operations. By using automation, deep learning, and natural language processing, AI can ease decision making and predict trends.

It's no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches. Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks for computer (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to "classical" machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines. But that higher accuracy comes at a cost.

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation.

A simulation using an artificial intelligence algorithm succeeds in predicting the characteristics of the fusion of two black holes. Nearly five years after the discovery of the first gravitational wave in September 2015, a team from the Center for Theoretical Astrophysics of the California Institute of Technology (CalTech, United States) has just published an article which reveals its details, collisions of black holes. Published in Physical Review Letters of January 11, this work presents the most precise simulation to date to describe the fusion of these compact stars. Machine learning Thus these researchers laid bare the most cataclysmic event that can occur in the Cosmos: the fusion of two black holes, two extremely compact stars, at the origin of the emission of a gravitational wave. Theoretically predicted by Einstein in 1916, it took physicists a century to invent complex and extremely sensitive detectors such as interferometers capable of detecting the tiny vibrations of space-time that are gravitational waves.

Vicarious, a secretive 10-year-old startup backed by Mark Zuckerberg, Elon Musk, and Jeff Bezos, reveals its progress and an initial customer. Google's TensorFlow is ready for quantum, but is AI ready for quantum?

As just about every aspect of computing is being transformed by machine learning and other forms of AI, companies can throw intense algorithms at existing CPUs and GPUs. Or they can embrace Graphcore's Intelligence Processing Unit, a next-generation processor designed for AI from the ground up. Capable of reducing the necessary number crunching for tasks such as algorithmic trading from hours to minutes, the Bristol, England, startup's IPUs are now shipping in Dell servers and as an on-demand Microsoft Azure cloud service. Read more about why Graphcore is one of the Most Innovative Companies of 2020. Ever tempted to click on the exciting discount offered to you in a marketing email?

To accommodate the diversity of infrastructures used for AI solutions, IBM has expanded support of its award-winning software beyond POWER architectures to include Intel platforms. To avoid confusion in the marketplace, IBM PowerAI Vision has been renamed IBM Visual Insights. Contact your IBM representative for the list of selected services available in your country, either as standard or customized offerings for the efficient installation, implementation, or integration of this product. IBM Support is your gateway to technical support tools and resources that are designed to help you save time and simplify support. IBM Support can help you find answers to questions, download fixes, troubleshoot, submit and track problem cases, and build skills. Learn and stay informed about the transformation of IBM Support, including new tools, new processes, and new capabilities, by going to the IBM Support Insider.

Last summer, a 30-car freight train led by three diesel locomotives rumbled down the tracks for 48 miles through the Colorado desert -- with nobody at the controls. But this was no runaway train. In fact, the experiment could be a preview of the rail industry's future. The demonstration at the Transportation Technology Center -- a research and testing facility owned by the Association of American Railroads -- was the debut of driverless train software produced by one of the oldest companies in the industry. Along for the ride were representatives from some of America's largest freight railroads who in recent years have been intrigued by the many ways artificial intelligence (AI) could be applied to one of the nation's oldest industries.