As Baidu accelerates its capabilities in self-driving vehicle technology, we dive into the Chinese tech giant's uniquely collaborative approach. Baidu has become the "dark horse" in the autonomous vehicle arms race. In an effort to play catch up to frontrunners in the US and gain an edge on emerging players in China, Baidu has taken a novel approach to developing self-driving software. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. The company's Apollo project, which it launched in April 2017, is an open source software platform that's designed to encourage collaboration across the auto industry to accelerate the development of self-driving cars.

First, and arguably the most popular type of machine learning algorithm, is linear regression. Linear regression algorithms map simple correlations between two variables in a set of data. A set of inputs and their corresponding outputs are examined and quantified to show a relationship, including how a change in one variable affects the other. Linear regressions are plotted via a line on a graph. Linear regression's popularity is due to its simplicity: The algorithm is easily explainable, relatively transparent and requires little to no parameter tuning.

The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.

Making a political donation to a presidential campaign is about to get as easy as -- well, saying it out loud. Starting next month, users of voice-controlled home assistant Amazon Alexa will be able to dictate their donations to a 2020 presidential campaign: "Alexa, I want to make a political contribution," or "Alexa, donate [amount] to [candidate name]." Alexa users can make donations of at least $5 and up to $200 to campaigns, and the feature is currently limited to presidential campaigns. Campaigns can sign up starting Thursday. The latest evolution in campaign technology raises new questions about how such contributions will be screened to make sure they are legal.

Can artificial intelligence become an organic life form? Can it behave, or will it behave in the same way that biological life behaves? I don't think we can classify A.I. as a species, it's a technology. The only way we can make A.I conscious is to incorporate biology into it. We have to be able to integrate bacteria, microorganisms into the artificial intelligence.

Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. The rising technology in Machine Learning in Medicine market is also depicted in this research report. Factors that are boosting the growth of the market, and giving a positive push to thrive in the global market is explained in detail. The report delivers a comprehensive overview of the crucial elements of the market and elements such as drivers, restraints, current trends of the past and present times, supervisory scenario, and technological growth. A thorough analysis of these elements has been accepted for defining the future growth prospects of the global Machine Learning in Medicine market.

From health care to the transportation industry, FOX Business' Maria Bartiromo looks at how artificial intelligence (AI) is shaping the future of society. In February the U.S. government launched an American AI initiative, which aims to stimulate AI development. The government's investments in unclassified R&D for AI technologies is up 40 percent since 2015 and for the first time in history, President Trump's fiscal year 2019 budget requests to designate AI and unmanned autonomous systems a priority. However, some say the problem is that China spends much more on AI investment and financing. In 2017, AI spending hit $39.5 billion, with China accounting for 70 percent of the expenditures. "AI is growing by leaps and bounds.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding artificial intelligence. Which function of the human brain should artificial intelligence replicate? The answer to that question characterizes one of the debates that is as old as the history of AI itself. Since the early efforts to create thinking machines began in the 1950s, research and development in the AI space has fallen into one of two approaches: symbolist and connectionist AI. Symbolist AI, also known as "rule-based AI," is based on manually transforming all the logic and knowledge of the world into computer code.

Mr. Yiannouka is the CEO of the World Innovation Summit for Education (WISE), a global think tank of the Qatar Foundation. WISE is dedicated to enabling the future of education through innovation. Its activities encompass research, capacity-building programs, and advocacy. WISE flagship initiatives include an annual series of research publications, a biennial global summit dubbbed the'Davos of education', the WISE edTech Accelerator, the WISE Innovation Awards, and the WISE Words podcast. Prior to joining WISE in August 2012, Stavros was the Executive Vice-Dean of the Lee Kuan Yew School of Public Policy (LKY School) at the National University of Singapore.