Artificial intelligence (AI) is becoming very real--and at an exponentially faster rate. Moreover, those organizations that leverage AI in sync with those Hard Trends and Soft Trends that are shaping the future stand to make the most of its extraordinary potential. On one level, artificial intelligence is poised to help anticipate and address such critical issues as cybersecurity, civil unrest and even outright acts of terrorism. For example, using technology such as automated smart detection, officials at the recent Olympics in Rio were successful in maintaining security in a wide array of venues and locations. Closer to home, the Central Intelligence Agency's deputy director for digital innovation Andrew Hallman recently addressed the issue of anticipatory intelligence at an event hosted by the government and technology website NextGov.

An important goal for us is to give you as accurate an ETA as possible. When you request a car and we tell you it's going to be 14 minutes or 12 minutes before it shows up, we want to make sure that that estimate is as precise as it can be. We gather information from millions of trips, because we know exactly how long it took for the car to come to you for each trip. We basically use data to build models that estimate the time it will typically take for the car to reach you at any given time of the day, any given time of the week. That is better than any attempt to compute the route and say, "It's going to take the car seven minutes to get to you."

"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed" Arthur Smauel. We can think of machine learning as approach to automate tasks like predictions or modelling. For example, consider an email spam filter system, instead of having programmers manually looking at the emails and coming up with spam rules. We can use a machine learning algorithm and feed it input data (emails) and it will automatically discover rules that are powerful enough to distinguish spam emails. Machine learning is used in many application nowadays like spam detection in emails or movie recommendation systems that tells you movies that you might like based on your viewing history.

Data Scientists across Skyscanner have started meeting every fortnight to discuss research papers that tackle similar problems to those that we face within Skyscanner. The 2nd paper we read was: "Mining Large Streams of User Data for Personalized Recommendations" (hi Xavier!). Just like the last post, we're we're also writing up a brief, non-technical overview the problems/opportunities we discussed. Netflix famously announced a 1M prize in 2006, calling on researchers across the world to improve their movie recommender system by 10%. To create this competition, they had to make a critical decision: how could Netflix measure a 10% improvement in their system?

Robots aren't taking our jobs, but artificial intelligence is making it easier than ever to do them. "Amy" saves entrepreneur Gillian Morris about 43 productive hours a year. Morris, the founder of Hitlist, a travel app that alerts users to cheap flights, has been using Amy, a virtual assistant from x.ai for about two years, to schedule meetings. To ask for Amy's help, Morris sends an email to the person or people she wants to meet with and copies Amy. From there, Amy takes Morris out of the email chain and handles the back and forth about dates and times.

Tim Leake is SVP and Creative, Marketing & Innovation for RPA, a Santa Monica-based agency all about People First. So, it's somewhat fitting that Leake would have an opinion on the emergence of Artificial Intelligence in the creative community. I put a cheeky little post up on social media last week, reading: "Artificial Intelligence will eliminate a lot of jobs in the coming years. But if you're worried, remember AI sucks at being creative." Nearly immediately, the Internet told me I was wrong.

Book has clear details of what to look at from spark application and configuration point of view to fine tune spark application execution in production environment. In this latest technology world, this books adds a lot of value to resources working in various shops gearing up their applications towards spark framework.

This is my first discussion on the forum so please forgive me any faux pas but I have something really exciting to share! At work I had some free time and Azure credits so I decided to give their ML lab a whirl. I took a very broad and difficult question, although one that should have had a finite answer, and used some SCADA data to train a model that would be able to predict unplanned downtime in a well far enough ahead as to be able to send out an engineer to apply preventative maintenance. Common idea, I felt, and enough data to work something out. I won't bore you with the details (unless you ask, of course!) but something astonishing and totally unexpected came out of the analysis.

LAS VEGAS--On a raised floor in a ballroom at the Paris Hotel, seven competitors stood silently. These combatants had fought since 9:00am, and nearly 4 million in prize money loomed over all the proceedings. Now some 10 hours later, their final rounds were being accompanied by all the play-by-play and color commentary you'd expect from an episode of American Ninja Warrior. Yet, no one in the competition showed signs of nerves. To observers, this all likely came across as odd--especially because the competitors weren't hackers, they were identical racks of high-performance computing and network gear.

At Lyft, engineers who build machine learning products are part of the Data Science group. Lyft's Data Science Team builds mathematical models underpinning the platform's core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We are hiring motivated experts in each of these fields.