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Republicans argue that the lower taxes for corporations and wealthy individuals promised in the tax bill currently before Congress will result in new investment in businesses and more jobs. But in the age of artificial intelligence and automation, trickle-down economics won't create employment. What corporations and the US economy at large need most in this emerging era is not more free cash, but a new approach to machine-assisted human productivity and purpose. Olaf J. Groth (@olafgrothsf) is a professor of global strategy, innovation, and digital futures at Hult International Business School, as well as CEO of Cambrian.ai. With Mark Nitzberg he is the co-author of Solomon's Code: Humanity in a World of Thinking Machines, due in 2018.

The concept of AI has been a subject of fascination for almost as long as computers have existed. However, we've only recently begun to see what a future with AI might actually look like and -- despite the grim picture that is often painted in sci-fi stories -- the outlook is pretty exciting. Luckily for us, the AI industry is currently less focused on the task of replacing the human race with an army of brutal machines and more occupied with solving big data problems. While this might stave off the cyborg apocalypse for a few years, it does create a more immediate issue for the global community. As the demand for skilled professionals in AI continues to expand, employers are struggling to keep up.

In this article, we will show you what AI is and how to learn more about it. Train yourself and become an AI programmer!

Hoy traemos a este espacio este panel de Future of Life Institute sobre la Inteligencia artificial de 8 reconocidos pensadores gurús tecnológicos actuales: Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, and Jaan Tallinn discuss with Max Tegmark (moderator) what likely outcomes might be if we succeed in building human-level AGI, and also what we would like to happen. The Beneficial AI 2017 Conference: In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients and followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial. You can find an audio balanced version of this panel here: https://www.youtube.com/watch?v OFBwz...

The artificial intelligence (AI) in agriculture market was valued at USD 432.2 Million in 2016 and is expected to be valued at USD 2,628.5 Million by 2025, at a CAGR of 22.5% during the forecast period. The base year considered for this study is 2016, and the market forecast is provided for the period between 2017 and 2025. The size of the AI in agriculture market has been estimated using both top-down and bottom-up approaches. These approaches have been used to estimate and validate the size of the AI in agriculture market and various other dependent submarkets. The key players in the market have been identified through secondary research, and their regional market shares have been determined through primary and secondary research.

Millions of patients live in rural or developing areas where disease treatment isn't the problem: it's overcoming the 1:1,000 doctor to patient ratio and screening for preventable diseases. High school student Kavya Kopparapu shares the future of artificial intelligence in this field-- as a substitute for doctors in these areas to deliver much-needed medical diagnoses. Kavya Kopparapu is the Founder and CEO of GirlsComputingLeague and current junior at Thomas Jefferson High School for Science and Technology. She is dedicated to sharing her passion for computer science with others, especially young girls, as the field has given her a world of opportunity, and has been recognized by organizations such as the White House and the National Center for Women in Information Technology (NCWIT). Her journey with computer science began in elementary school, when she was introduced to the Scratch programming language and developed robots using the Mindstorms programming language.

The Jupyter Notebook (http://jupyter.org/) is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and explanatory text. Jupyter Notebook is a popular tool for scientists and students performing data analysis and visualization. It is also useful for instructors wishing to build interactive tutorials for students, since Jupyter Notebooks can be converted to slide shows with live code execution. Initially, Jupyter Notebook included built-in support for the Julia, Python, and R programming languages, but it now includes an interface for supporting other languages as well. The NCAR Command Language (NCL) is an interpreted language designed specifically for scientific data analysis and visualization.

This is a contributed article by Irina Raicu, the director of the Internet Ethics program at the Markkula Center for Applied Ethics. "Sometimes a type of glory lights up the mind of a man," writes John Steinbeck in his novel "East of Eden," which is set in a California valley -- Salinas, though, not Silicon. "It happens to nearly everyone. You can feel it growing or preparing like a fuse burning toward dynamite. Okay, but what does that have to do with artificial intelligence? I don't know how it will be in the years to come. There are monstrous changes taking place in the world, forces shaping a future whose face we do not know. Some of these forces seem evil to us, perhaps not in themselves but because their tendency is to eliminate other things we hold good. That line finds an echo in our times. Various ethicists are writing, these days, about the concerns that AI might eliminate some things "we hold good" -- and not just meaning "jobs." They write, for example, about the threat of "moral de-skilling" in the age of algorithmic decision-making. About what might be lost or diminished by the advent of robot caretakers. About what role humans will play, in general, in an age of machine learning and neural networks making so many of the decisions that shape human lives. "It is true," Steinbeck writes, A group can build automobiles quicker and better than one man, and bread from a huge factory is cheaper and more uniform. When our food and clothing and housing all are born in the complication of mass production, mass method is bound to get into our thinking and to eliminate all other thinking. We are in the process of shifting from the kind of mass production that Steinbeck talked about to a kind of mass production that requires much less human involvement. If "mass method" was bound to get into our thinking back then, how is it shaping our thinking now? Is this what the current focus on data collection and analysis of patterns is about? "In our time," adds Steinbeck, This in my time is the danger. There is great tension in the world, tension toward a breaking point, and men are unhappy and confused. In our own time, AI is spreading into all the various spheres of our lives, and there is tension and great concern about its impact. We are confused by dueling claims that AI will eliminate jobs or create new ones; that it will eliminate bias or perpetuate it and make it harder to identify; that it will lead us to longer, happier lives -- or to extinction. "At such a time," writes Steinbeck's narrator, "it seems natural and good to me to ask myself these questions.

In this Learning Plan, instructor Ted Hills identifies the goals of Data Modeling and prepares you for new concepts using Concept and Object Modeling Notations (COMN). Within the six-course program, you will learn how to represent real-world models based on COMN, model objects within a computer's memory or storage, and dive into modeling requirements and their expressions in data. By the end of this program, you will understand the differences between SQL and NoSQL and between ACID and BASE, and how to model each in a data-centric organization, whether using a document database or data warehouse. Enroll in one of these individual courses or purchase the entire Learning Plan. We offer several bulk licensing options for corporate and group use.