"Father of the Video Game" Ralph Baer escaped Jewish persecution in Nazi Germany as a teen and served in the U.S. Army in WWII. After coming of age in tough times, he felt driven to bring "more fun and whimsy" into the world. Ralph Baer's childhood was stolen by the Nazis. The German-born Jew gained a semblance of revenge overseas, imagining a new way for children of all ages to play. Ralph Baer invented video games.

Researchers at USC's Viterbi School of Engineering think artificial intelligence has a future place in Hollywood's ratings process. A new AI tool is intended to rate a movie's content at the script stage, allowing screenwriters and producers to edit or adjust the screenplay to achieve the desired rating from the Motion Picture Association's ratings board, making desired changes before the movie is shot. The AI project focused on the presence of violence, drug abuse and sexual content, areas that traditionally play a role in a film's rating. The tool, researchers say, has the potential to provide instant feedback to storytellers and decision-makers. Researchers told TheWrap they are currently testing the technology with various partners to make the AI tool available soon to the general public.

While, the system, which Ellison dubbed as "self-driving" and "the world's first autonomous database," may be unique by some measures, it is also part of a long-standing trend that is well under way. Automation of database cluster deployment on cloud has become increasingly common, and wider automation can be anticipated, according to Tony Baer, an analyst at Ovum. "You can see how cloud databases are doing automation -- with database sharding as a major example," Baer said. Meanwhile, query performance and other database activities are also being affected by advanced machine learning technology, he said. Baer noted that "Oracle has all kinds of database activity logs. That is big data that acts as a corpus for machine learning that can figure out what is a normal pattern, and highlight queries that are going to cause trouble."

After revolutionizing tax and accounting over the course of decades, technology finally looks poised to reshape the third major service of the traditional accounting practice: the audit. Machine learning, data analytics, ever-more-powerful and mobile computers, and new tools like blockchain will do more than just change the way auditors do their job -- increasingly, they'll change what that job is. To get a glimpse of what the audit (and auditor) of the future will look like, Accounting Today convened a virtual roundtable of experts in the field. Sharing their thoughts on the future of auditing here are: Mark Baer, managing partner of the audit services group at Top 10 Firm Crowe Horwath; Frank Casal, vice chair of audit at Big Four firm KPMG; Cindy Fornelli, the executive director of the Center for Audit Quality; Joel Shamon, the national audit leader at Top Five Firm RSM US; and Jimmy Thompson, an audit partner at Texas-based MaloneBailey. Which trends -- whether technological, regulatory, economic or otherwise -- should auditors be paying the most attention to over the next five years? Casal: Audit professionals' work is fundamentally about "trust."

Streaming analytics is primed to be the big breakout use case for big data in 2017, but the big disruptor in the big data landscape will be machine learning, according to Ovum. Ovum's new 2017 Trends to Watch: Big Data report says that big data remains the fastest-growing segment of the information management software market, and is forecast to grow from $1.7 billion in 2016 to $9.4 billion by 2020, comprising 10% of the overall market for information management tooling. The most disruptive trend, however – and the one driving many of the above – is going to be machine learning, Ovum says. Under the covers, machine learning is already becoming ubiquitous as it is embedded in many services that consumers take for granted. Increasingly, machine learning is becoming embedded in enterprise software and tooling for integrating and preparing data.

Tony Baer: It's broad in that, in many cases, businesses and consumers are already using services that have machine learning embedded in them -- they just don't realize it. But in terms of how many companies have data scientists on board, ones that are writing or using machine learning algorithms and doing their own internal development, that will still be limited. That's even though there are libraries available for machine learning, so you don't have to just write them from scratch anymore. There are also emerging collaboration tools that are designed to connect the data scientist to the data engineer or the business. You're seeing an upswing of tooling, but largely, the appeal of that is going to be limited to those organizations that have very deep resources -- the same types of organizations, really, who were the pioneers with Hadoop.

Take one major trend spanning the business and technology worlds, add countless vendors and consultants hoping to cash in, and what do you get? In the world of big data, the surrounding hype has spawned a brand-new lingo. Read on for a glossary of sorts highlighting some of the main data types you should understand. The shining star in this constellation of terms is "fast data," which is popping up with increasing frequency. It refers to "data whose utility is going to decline over time," said Tony Baer, a principal analyst at Ovum who says he coined the term back in 2012.