On May 18th, 2012, attorneys for Oracle and Google were battling over nine lines of code in a hearing before Judge William H. Alsup of the northern district of California. The first jury trial in Oracle v. Google, the fight over whether Google had hijacked code from Oracle for its Android system, was wrapping up. The argument centered on a function called rangeCheck. Of all the lines of code that Oracle had tested -- 15 million in total -- these were the only ones that were "literally" copied. Every keystroke, a perfect duplicate. It was in Oracle's interest to play up the significance of rangeCheck as much as possible, and David Boies, Oracle's lawyer, began to argue that Google had copied rangeCheck so that it could take Android to market more quickly. Judge Alsup was not buying it. "I couldn't have told you the first thing about Java before this trial," said the judge. "But, I have done and still do a lot of programming myself in other languages. I have written blocks of code like ...

Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson's adage applies well to AI adoption: The future is already here, it's just not evenly distributed. The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities.

I would recommend that you start with Introduction to Statistical Learning with R (usually shortened as ISLR). A lot of people have adapted the examples to Python if you google a bit and it's an excellent book that hides just enough complexity to not be overwhelming. Plus, once you have a good understanding of all of it, you can either graduate to the more extensive version (Elements of Statistical Learning, usually shortened as ESL) for a more rigorous treatment of the same thing, or choose to go for something different like Bishop's Pattern Recognition and Machine Learning. ISLR is free as a pdf and has a corresponding MOOC. ESL doesn't, but is also free on the author's website.

The programme has been created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). Also taken into consideration when creating the faculty was the School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. In the faculty, learning is based on practice and projects. National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.

I was rehearsing a speech for an AI conference recently when I happened to mention Amazon Alexa. At which point Alexa woke up and announced: "Playing Selena Gomez." I had to yell "Alexa, stop!" a few times before she even heard me. But Alexa was oblivious to my annoyance. We're now surrounded by hyper-connected smart devices that are autonomous, conversational, and relational, but they're completely devoid of any ability to tell how annoyed or happy or depressed we are.

Human AI Collaboration: A Dynamic Frontier Partnerships Between Human and Artificial Intelligence November 1, 2017 8:30a-5:30p Stanford University, Mckenzie Room (3rd FL Jen-Hsun Huang Engineering Center) Paid Registration Required If you are a mediaX member or are faculty, staff or student of Stanford, please email Addy Dawes for a special registration code. In a few decades, we've gone from machines that can execute a plan to machines that can plan. We've gone from computers as servants to computers as collaborators and team members. Even teams of highly competent people struggle to clarify goals, understand each other in conversations, define roles and responsibilities, and adapt when necessary. Determining what we want from collaboration is sometimes the hardest task.

A basic understanding of how it is affecting our culture is critical for business, students, sales representatives and professionals. It is a topic that inspires St. Andrews alumnus Martin Brossman to gather big-picture insights which he will share in his presentation at noon on Friday, October 20 in LA104 on the Laurinburg campus. "There has never been any other time in life when so many aspects of our world are focused on advancing artificial intelligence (AI) and machine learning as today," Martin Brossman said, "I believe students and professionals need a basic understanding of how Machine Learning and AI are progressing today because its influence on our life is growing rapidly. As our world gets more automated and AI gains greater dominance in our society, working on enhancing our best human qualities will give us a competitive advantage." About the Friday Science Series "Friday Science at St. Andrews seminar series consists of a seminar most Friday's of each semester. Speakers are from a diverse mix of folks including faculty, alumni, and speakers from outside the university. He provides customized coaching and training for individuals and groups, integrating digital marketing, social networking and reputation management. In Oct. 2009 he received St. Andrews' Ethel N. Fortner Writer and Community Award, St. Andrews University's highest literary award, for his first book, "Finding Our Fire - Enhancing men's connection to heart, passion, and strength." His books are available on Amazon. About St. Andrews University St. Andrews is a branch of Webber International University. The University's mission is to offer students an array of business, liberal arts and sciences, and pre-professional programs of study that create a life transforming educational opportunity which is practical in its application, global in its scope, and multi-disciplinary in its general education core. Students will acquire depth of knowledge and expertise in their chosen field of study, balanced by breadth of knowledge across various disciplines. Special emphasis is placed on enhancing oral and written communication, and critical thinking skills. The University awards degrees at the bachelor and master levels at locations in Florida and North Carolina, as well as at the associate level in Florida. Traditional classroom, online, and hybrid learning environments are available. Opportunities exist for students to draw on the courses and programs of study at both locations through online courses and/or periods of residence at either campus. Webber's programs in Florida focus on the worldwide business environment, and emphasize development of skills in administration and strategic planning, applied modern business practices, and entrepreneurship. The St. Andrews branch campus in North Carolina offers an array of traditional liberal arts and sciences and pre-professional programs of study."

A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework. We introduce new theory that gives appropriate scalings of graph parameters that provably lead to a well-defined limiting posterior as the size of the unlabeled data set grows. Furthermore, we show that these consistency results have profound algorithmic implications. When consistency holds, carefully designed graph-based Markov chain Monte Carlo algorithms are proved to have a uniform spectral gap, independent of the number of unlabeled inputs. Several numerical experiments corroborate both the statistical consistency and the algorithmic scalability established by the theory.

You've probably heard that a robot is going to take your job. It's an oft-repeated refrain, heralded in article headlines and speeches from luminaries such as Elon Musk and Stephen Hawking. Some experts predict that anywhere from 38 to 57 percent of jobs could be automated in the next few decades, depending on who you ask, and the jobs aren't limited to any one industry. Automation threatens to eliminate or limit jobs such as waitstaff, truck drivers, factory workers, accountants, cashiers, and retail employees, according to a recent report from PBS. But to other experts, these apocalyptic predictions are overblown.

In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.