If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In the emergent war to have the best artificial intelligence capability, academia might have the most casualties. According to the National Science Foundation, 57 percent of new computer-science doctoral graduates in the United States take industry jobs, meaning they leave academia for the private sector. This is compared to 38 percent a decade ago, according to The Wall Street Journal. Given that academia is the primary breeding ground for skills in emerging fields like AI, what would a constant academic exodus of talent in the field mean for the future development of its talent pool? One of the biggest concerns is that there will be fewer graduates with a thorough education in AI. "The number of graduating master's and Ph.D.-level computer scientists may decrease, which is the opposite to what the current market is demanding," said Peter Morgan, chief AI officer at Ivy Data Science, an AI-as-a-service platform and training company based in New York City.
If you ask, many people will say we are in a new era of higher education, one where machine learning and big data analytics are driving rapid change. From the influx of adaptive learning technologies to the automated student support services and predictive analytics models driving new interventions, there are fewer spaces of college and university life that are not being touched by these technological innovations. These technological opportunities could offer a lot to higher education. Indeed, if we ignore the opportunities that machine learning and big data analytics might provide to complement our human capacities, we will do a disservice to those we claim to serve -- our students. But if we treat them as an opportunity to downsize the work force or largely replace human social interactions with automated ones, we are going to lose a lot more than we gain.
NEW YORK – For a dozen students from Futaba Future High School in Fukushima Prefecture, a recent visit to the United Nations was a chance to share their plans to improve the lives of others by drawing from their catastrophic earthquake and tsunami experiences as a source of strength. Despite overcoming enormous hurdles in the aftermath of the March 11, 2011, disaster that took more than 19,000 lives, the surviving students have moved forward with aspirations of choosing future paths to benefit the global community. "Thanks to all my experiences like getting bullied, joining the drama club and studying at my high school, I think I could grow well," Satsuki Sekine told U.N. diplomats, staff and youth representatives who gathered to hear their presentation on the current situation in Fukushima early this month as part of a scheduled visit while in New York. The 17-year-old explained how drama can be used to portray the challenges of discrimination and conflict "not as an abstract concept but with specific and visual examples." Recounting how the tsunami rendered her home unlivable, she explained how her life in Tomioka as a normal 9-year-old was turned upside down.
The Madrid ASDM summer school is in its thirteenth edition this year, with hundreds of students from all over the world having attended so far. It comprises 12 intensive (15 lecture hours) week-long courses, and a student may attend from one up to six courses. The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series. Each course has theoretical and practical classes, the latter done with R or python. While the summer school is mainly attended by people from academia - PhD students and researchers-, people from the industry also assist.
CLEVELAND, Ohio - Computers are grading your child's state tests. After Ohio started using American Institutes for Research in 2015 to provide and score state tests, Artificial Intelligence (AI) programs have increasingly taken over grading. Computers are now scoring the entire test for about 75 percent of Ohio students, State Superintendent Paolo DeMaria and state testing official Brian Roget told the state school board recently. The other 25 percent are scored by people to help verify the computer's work. Ohio and AIR are not alone.
Design has consequences Carnegie Mellon University design students are exploring ways to enhance interactions with new technologies and the power of artificial intelligence. Assistant Professor Dan Lockton teaches the course, "Environments Studio IV: Designing Environments for Social Systems" in CMU's School of Design and leads the school's new Imaginaries Lab. "We want the designers of tomorrow to think about the overlap between the human world and AI. Many of our students are going to go work for companies like Facebook or Google, and they're going to be making decisions that might seem very small in the moment -- what text do we put on a button, how easy do we make it for someone to do this thing or that -- but those decisions are going to impact people's lives. We want them thinking through how their design has consequences."
I spend a lot of time in the air, travelling around the globe spreading the word about On-Demand Education Marketplace, or ODEM.IO. One of the questions I'm asked most often on my business trips is "How will blockchain technology help ODEM to improve education?" My answer: Blockchain gives us the opportunity to go big. Blockchain, automated smart contracts and artificial intelligence enable us to provide solutions to global problems in education such as accessibility, cost and the removing the inefficiencies caused by intermediaries. For students the result is improved access to a learning that better prepares them for the changing demands of the job market.
Concepts in the book are laid out clearly, often with diagrams, but the book moves quickly. The book expects you to keep up or you will fall behind. That being said, each section has an overview of the concepts to be covered and ends with worked examples and quiz questions, the answers to which are available on the book's website. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.
In the decade since the Great Recession, governments have used fiscal policy to prop up flagging domestic demand. This response has been considered appropriate because the shock was seen as temporary. But this attention to demand-side weaknesses may have distracted governments from attending to supply-side gaps that have been widening with the acceleration of technological change and artificial intelligence. Policymakers should start paying more attention to what's called structural fiscal policies, that is, changes in both public spending and tax collection to aid the expansion of the productive potential of economies. As laborsaving technologies flood the market, delays in doing this could mean that workers pay a stiff price, while consumers do not realize many of the benefits.
Data science is transforming many industries, from health care to banking to heavy manufacturing, and women are leading the charge. That was the crux of the Cambridge Women in Data Science Conference, held March 5 as part of a global event launched by Stanford University in 2015 to educate, inspire, and connect women in tech. The local conference, now in its second year, was hosted by the Institute of Applied Computational Science (IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences; the MIT Institute for Data, Systems, and Society; and Microsoft. Distinguished speakers from academia and industry presented technical talks to more than 240 female technologists, researchers, and students, highlighting research in such areas as deep learning applications in oncology, data science tools for pollution monitoring, and the challenges of preventing bias in algorithms. In addition, local winners of an international datathon/kaggle challenge, held in conjunction with Stanford's global conference were announced, and students presented posters and took advantage of networking and recruiting opportunities.