Columbia University is offering free online course on Machine Learning. It is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In this course applicants will master the essentials of machine learning and algorithms to help improve learning from data without human intervention. The course will start on January 16, 2017. Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields.

Today, we're one month away from election day, and the race for the presidency is closing in on the home stretch. Newsrooms around the country are buzzing with activity: interviews, fact checking, reporting and, of course, combing through huge quantities of videos and recordings of both candidates, hunting for that juicy soundbite that might change public opinion and the course of the election. Now you can search through it just like you would with text. You get taken to the exact time when those terms were mentioned during his speech. Click the red timeline markers to jump through the video to hear each audio clip.

Researchers have trained an artificial intelligence algorithm to understand and predict visual humor, representing a major development towards creating "common sense" machines. The machine-learning algorithm, created by scientists at Virginia Tech, is capable of both recognising and creating humorous scenes by analysing certain aspects of an image considered to be funny. According to the researchers, humor is a major barrier to the advancement of AI and could hold the key to unlocking emotional intelligence. "Humor is an integral part of human lives," the paper's abstract states. "Despite being tremendously impactful, it is perhaps surprising that we do not yet have a detailed understanding of humor."

SEATTLE--(BUSINESS WIRE)--(NASDAQ: AMZN)--Today, Amazon announced the Alexa Prize, an annual university competition dedicated to accelerating the field of conversational artificial intelligence (AI). The goal of the inaugural competition is to build a "socialbot" on Alexa that will converse with people about popular topics and news events. The team with the highest-performing socialbot will win a 500,000 prize. Additionally, a prize of 1 million will be awarded to the winning team's university if their socialbot achieves the grand challenge of conversing coherently and engagingly with humans for 20 minutes. Teams of university students can submit applications now and the contest will conclude at AWS re:invent in November 2017, where the winners will be announced.

Self-driving cars might fill the roads a lot sooner if carmakers can put aside their rivalries and share the data that would teach computers how to drive safely. MobileEye, an Israeli company that supplies advanced computer hardware and software to many carmakers to enable cars to spot objects on the road, is now developing ways to train cars to drive themselves. The effort involves feeding computers huge quantities of driving behavior into a vast, highly realistic simulation, so that they can learn how to drive for themselves. And MobileEye aims to have different customers contribute the data that their vehicles collect. "If you want to leverage many, many cars, you need to leverage as many carmakers as possible," says Amnon Shashua, cofounder and CTO of MobileEye.

Much has been written about DevOps and its ability to speed up time-to-value and innovation. Machine learning is no different. New approaches and algorithms--for example, deep learning--are coming out all the time, and data scientists are trying them out through code and relying less on GUI-based interfaces. After the new approach has been tested out in a sandbox environment with limited scope, it's time to move toward development, QA, and finally, production. Each one of these environments can be automated with DevOps through tools like Jenkins, Puppet, Chef, Ansible, and Docker.

Dr. Alessandro Negro is the Chief Scientist at GraphAware. He has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. Before joining the team, Alessandro has gained over 10 years of experience in software development and spoke at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento. Your email address will not be published.

Now that we have training and test datasets sampled, initially preprocessed and available in the data grid, we can close Web Notebook and start experimenting with different techniques and algorithms by submitting Spark applications. For our first baseline approach let's take a single feature device_conn_type and logistic regression algorithm: We will explain a little bit more what happens here. At first, we load the training dataset from the data grid, which we prepared and saved earlier with Web Notebook. Then we use StringIndexer and OneHotEncoder to map a column of categories to a column of binary vectors. For example, with 4 categories of device_conn_type, an input value of the second category would map to an output vector of [0.0, 1.0, 0.0, 0.0, 0.0].

Microsoft is jumping on the artificial intelligence bandwagon with the formation of a new research group that will seek to make the technology more accessible via its Azure cloud while helping to deliver new capabilities across applications, services and infrastructure. The infrastructure portion of the effort focuses on combining the processing engines like GPUs and FPGAs designed to improve network connectivity as ways to boost AI performance running on Microsoft's Azure Cloud. Microsoft (NASDAQ: MSFT) said last week Harry Shum, a 20-year company veteran who worked on the Bing search and Cortana intelligence personal assistant projects, would head the AI initiative. More than 5,000 computer scientists and engineers work for Microsoft's AI and Research Group. Microsoft's AI initiative seeks to "democratize" AI technology through a focus on agents, applications, services and infrastructure.

The go to place for quick and easy translations – Google Translate – just received a huge upgrade with Deep Learning algorithms boosting its translation capabilities and reducing errors by 60%. Google's experiments with neural machine translation pays off in a big manner. Like most translation services, Google Translate too relied on breaking down sentences into smaller phrases or groups of words and then translated these phrases which were later joined together to produce the output. With Neural Machine Translation, Google Translate can translate entire sentences without breaking them in phrases. This new approach has been said to reduce errors by at least 60 percent compared to the previous phrase based approach.