What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun--but not "he" or "she." The algorithm went on to pick the funniest captions for thousands of the New Yorker's cartoons, and in most cases, it matched the intuition of its editors. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz. Google released the word2vec tool, and Facebook followed by publishing their speed optimized deep learning modules.

Lung cancer is one of the most common types of cancer, with nearly 225,000 new cases of the disease expected in the U.S. in 2016. Using a data set of high-resolution scans of lungs provided by the National Cancer Institute, participants will develop artificial intelligence algorithms to accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that prevents low-dose CT scans from being widely used for lung cancer detection. Competition results have the potential to advance our understanding of how all types of cancer develop and spread in the body. They'll also free radiologists to spend more time with patients.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

Machine learning has come a long way since Hollywood painted it as shiny robots fueled by artificial intelligence. In the Hollywood version, robots usually end up replacing humans. But today, we're actually using machine learning to supplement many of the things that humans do best. They feel foreign, scientific, and hard to understand. And for many professionals, the phrase still sounds like highly technical jargon.

How can I become a data scientist? A quick search yields a plethora of possible resources that could help -- MOOCs, blogs, Quora answers to this exact question, books, Master's programs, bootcamps, self-directed curricula, articles, forums and podcasts. Their quality is highly variable; some are excellent resources and programs, some are click-bait laundry lists. Since this is a relatively new role and there's no universal agreement on what a data scientist does, it's difficult for a beginner to know where to start, and it's easy to get overwhelmed. Many of these resources follow a common pattern: 1) Here are the skills you need and 2) Here is where you learn each of these.

The Knoxville R Users Group is presenting a workshop on text analysis using R by Bob Muenchen. The workshop is free and open to the public. A description of the workshop follows. When analyzing text using R, it's hard to know where to begin. There are 37 packages available and there is quite a lot of overlap in what they can do.

The "virtuous circle" comprising RPA, machine learning and analytics was the central theme of this month's BotVisions webinar series. Joining me were Kelly Coupe, Principal Product Manager, and Abhijit Kakhandiki, VP of Products, to share their insights into how business users are integrating the execution capabilities of RPA with the cognitive capabilities of machine learning to take the business benefits of automation to the next level. While extremely good at executing specifically defined tasks, RPA tools are limited in the sense that they cannot adjust to new conditions or learn from experience. Machine learning, meanwhile, applies Artificial Intelligence (AI) capabilities to lend business context to the tasks executed by RPA systems, enabling the latter to make better decisions and be more productive. For example, RPA systems can effectively perform many tasks associated with loan origination or account management.

Ben Rossi writes in Information Age about the emergence of educational technology, and how colleges and universities could become more effective with Integrated Learning Systems by using them for more than regurgitating old styles of instruction on new equipment. He writes that ed tech should be more than just an innovative way of educational delivery, but part of the education itself by allowing students and teachers to create their own questions, answers and theories on a variety of elements on a given subject. Technology has the capacity for education to replace the currency of grades and test scores with imagination and creation in action, a necessity for an industry which spent more than $6 billion on teaching technology in 2015. Several colleges and universities are working to reform higher education into spaces for innovation and commercial development. The University of Connecticut, Arizona State University, and Princeton University are among a handful of schools encouraging students to find entrepreneurial niches and to take learning and career passions beyond the classroom.

What makes one intervention work in a school when another seemingly similar one falls flat? Increasingly detailed computer models of student behavior and learning may help researchers avoid such setbacks by better pinpointing interventions before taking them to schools. "In education research, I get a great idea, apply for funding, … then I spend a few months in schools taking time from students and teachers, and often find out it doesn't work," said Richard L. Lamb, an assistant professor of science education and educational measurement at Washington State University in Pullman. "That's great that we have that data," he said, "but it's not the most efficient way to do [research and development]." Instead, Mr. Lamb and colleagues are working to pair education technology and neuroscience to mimic how students learn in a classroom and provide an additional means of testing and honing interventions.