Splunk started its life as a log analysis system and has since grown into a general solution for analyzing and acting on machine-generated data. With Splunk Enterprise 6.5, the company's enterprise-level offerings now feature machine learning, an ingredient that's all but obligatory for any big data product. But Splunk's approach is less opaque than most, and it encourages enterprise devs to build with it instead of merely deploying it. Splunk has two offerings for machine learning: a prepackaged set of functionalities for common use cases, and a developer toolkit for building custom machine learning models that can be leveraged against data harvested with Splunk. Enterprises getting their feet wet with either Splunk, machine learning, or a combination of the two can start with the Splunk IT Service Intelligence, Splunk User Behavior Analytics, and Splunk Enterprise Security bundled solution sets.

Operational intelligence software developer Splunk is expanding the machine-learning capabilities of its products, debuting new releases of its flagship Splunk Enterprise platform and several applications that leverage machine data for business intelligence, security and other tasks. "Machine data is absolutely key to digital transformation," said President and CEO Doug Merritt in a keynote speech Tuesday that kicked off the .conf2016 "Machine learning enables organizations to get deeper insights from their machine data and ultimately increases the opportunity our customers can gain from digital transformation." He went on to say that the "machine data fabric" is the most effective way for businesses to "collect, store, analyze, interpret and share" data throughout an enterprise. Splunk's software is used to collect and analyze operational data, including machine data generated by IT systems and networks, security systems and Internet of Things devices, to generate actionable insights.

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per hour. Later in the keynote (starting at 25:00), John Salch, VP of Technology and Platforms at PROS describes using R to determine prices for airline tickets, hotel rooms, and laptops. PROS has been using R for a while in development, but found running R within SQL Server 2016 to be 100 times (not 100%, 100x!) faster for price optimization.

NVIDIA gives automakers, tier-1 suppliers, automotive research institutions, and start-ups the power and flexibility to develop and deploy breakthrough artificial intelligence (AI) systems for self-driving vehicles. NVIDIA's unified AI computing architecture enables training deep neural networks in the data center on the NVIDIA DGX-1, and then seamlessly runs them on NVIDIA DRIVE PX 2 inside the vehicle. This end-to-end approach leverages NVIDIA DriveWorks software and allows cars to receive over-the-air updates to add new features and capabilities throughout the life of a vehicle.

Over the last decade, the "data revolution" has touched every aspect of our work and personal lives. Today's business challenges have never been more complex, and the critical insights that can address these challenges are often buried in an avalanche of data. In today's marketplace, the business that wins, is the business that "thinks." The viability of a company in the marketplace now depends on its ability to use data and analytics to fuel a thinking business. Companies in industries as diverse as healthcare, retail, banking and manufacturing are already using cognitive technologies to reshape business and do things faster and more efficiently than ever before.

I started my career in 1997 with the advent of modern eDiscovery. In fact, it was not even called eDiscovery when I developed my first applications for processing data in the context of eDiscovery. I founded Advocate Solutions, Inc around that same time and we developed Discovery Cracker - one of the first eDiscovery processing applications. Producing documents was a different game back then as the price for processing was incredibly high. I Joined kCura, known for its legal database application Relativity, in 2010 and saw firsthand how fast the amount of data involved eDiscovery was rising.

Initially based in Peru, Laboratoria has expanded into Mexico and Chile.

Sophia, a young animator is offered what should be her dream job. But when she discovers the truth of the modern "creative" process, she must make a hard choice about her passion for film. Set in an unsettling tomorrow, REAL ARTISTS, is the new sci-fi short film from award-winning director/screenwriter Cameo Wood (DUKHA IN SUMMER) and based on the short story by Hugo/Nebula/World Fantasy winning author Ken Liu (THE GRACE OF KINGS) and stars renowned actress Tamlyn Tomita (FOUR ROOMS, JOY LUCK CLUB, THE DAY AFTER TOMORROW) and marks the debut of Tiffany Hines (BONES) in a sci-fi indie role. Help us bring this story to life. We need 1000 followers on Seed&Spark in order to be eligible for distribution on Netflix, Hulu, iTunes, and Amazon - If we get 1000 followers, we also get a grant of filmmaking products and services worth 8,500.

AI is the technology that will have the single biggest impact on cities over the next decade. At Urban.Us, we already meet teams using very large datasets to train algorithms to drive cars, water yards efficiently, guide drones to survey construction sites and route first responders to the people who need them most, and others who use bots to provide legal guidance to people with parking fines. These startups are benefiting from an explosion of data generated by human activities and sensors. Ironically, while the flood of data is difficult for people to understand, it's great for teaching machines. Thanks to cheaper storage and processing to train new algorithms, we've seen a surge in AI deep-learning techniques.

When the writer Rebecca Forster first heard how Google was using her work, it felt like she was trapped in a science fiction novel. "Is this any different than someone using one of my books to start a fire? I have no idea," she says. "I have no idea what their objective is. Certainly it is not to bring me readers."