Neuroscientists at DeepMind, a Google-owned AI lab in London, have teamed up with academics at Oxford University and UCL to try and determine how the human brain navigates an underground train network. The group -- whose work was published in the journal Neuron this week -- asked humans to plan a journey in a virtual subway network. Participants were tasked with getting from A to B while MRI scans of their brain were taken. These scans showed which parts of the brain are involved in planning and making decisions. The group, which included Google DeepMind CEO Demis Hassabis, concluded that the brain splits the task of completing a journey into different jobs, with different parts of the brain handling different elements of the task.

Matters of machine intelligence are topics of great interest these days. It reminds me of a statement I read a couple years back from renowned technology writer and author Kevin Kelly: "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Although that prediction proved to be a bit off the mark (or perhaps it's still too early for its time), the idea is certainly compelling. For a better approach to making your company smarter, look to APIs. Nearly every day I hear of a new startup announcing some new intelligence offering.

When people discuss the workplace of the future, they frequently focus on the collaboration technologies and the layout of these new workspaces. We discuss the "intolerance" of millennials and how work tools must change to support the digital native. While the selection of tools is important, this dialogue lacks a discussion of the type of jobs employees will perform and how they'll be trained to do these jobs. We're entering the next generation of the industrial revolution and today's workforce is largely unprepared to fill the next wave of jobs. Many careers in the future will be based on software programing, machine learning and data analysis using tools that barely existed ten year ago.

IBM and a marketing company called The Drum just announced that the AI system known as Watson was able to edit an entire magazine on its own. According to a statement released via The Drum, the magazine edited by Watson contains different features that shows Watson's capabilities. It has different analytical functions, as well as skills necessary to assist modern-day marketers. Also, Watson has been programmed to have the capacity to answer a series of questions about David Olgivy, the "advertising legend," and was able to give some predictions for the winners of this year's Cannes Lions awards. While it is not yet the end for the human editors' careers, this does showcase the potential that artificial intelligence has in an ever-increasing number of fields. IBM Watson program chief David Kenny hopes that one day Watson will be able to ask people questions and develop abductive reasoning skills.

The number of intelligence (A.I.) systems used in infotainment and advanced driver assistance systems (ADAS) systems will jump from 7 million in 2015 to 122 million by 2025, according to a new IHS Technology report. IHS's Automotive Electronics Roadmap Report found the install rate of A.I.-based systems in new vehicles was just 8% in 2015, and the vast majority were focused on speech recognition, according to IHS. However, that number is forecast to rise to 109% in 2025, as there will be multiple A.I. systems of various types installed in many cars. "An artificial-intelligence system continuously learns from experience and by its ability to discern and recognize its surroundings," Luca De Ambroggi, IHS Technology's principal analyst for automotive semiconductors, said in a statement. "It learns, as human beings do, from real sounds, images and other sensory inputs. The system recognizes the car's environment and evaluates the contextual implications for the moving car."

The German credit dataset can be downloaded from UC Irvine, Machine learning community to indicate the predicted outcome if the loan applicant defaulted or not.

Microsoft envisions Cortana doing much more than reminding us to pick up toilet paper on the way home from work. Back in 2014, we Windows Phone fans could barely contain ourselves as we eagerly awaited Cortana's arrival on Windows Phone 8.1. At the time, like many writers, I had a vision of what Cortana would mean for Microsoft and mobile computing. So, well, I wrote about it. Alas, time has moved on, and that initial fervor that fueled the "Cortana conversations" of many Windows phone fans has transitioned through other topics. The Lumia 950 and 950 XL had their time in the limelight. HoloLens enters and re-enters the conversation. Windows 10 updates are a consistent topic, further fueled by Gabe Aul's passing of the torch to Dona Sarkar as the new face for the Insider Program.

They are funded by a new four-year, 1.2 million National Science Foundation grant to computer scientist Sridhar Mahadevan, lead principal investigator at UMass Amherst's College of Information and Computer Sciences. His co-investigators are Mario Parente, an expert in analysis of hyperspectral images at UMass Amherst, and Darby Dyar of Mount Holyoke, a specialist in planetary chemistry and geology who serves on the scientific mission team for the Mars rover. As Mahadevan explains, NASA's Curiosity rover, a car-sized robot, has been exploring a crater on Mars since August 2012 and sending back a steady stream of specialized camera images and data on the chemical composition of rocks and dust for analysis. The data range from one-dimensional spectra of rock samples to three-dimensional hyperspectral images of the Martian surface. He advises Ph.D. students Thomas Boucher, CJ Carey, Steve Giguere, Ian Gemp, Francisco Garcia and Ishan Durugkar in the Autonomous Learning Laboratory, who are exploring machine learning methods to show, for the first time, that new deep learning approaches provide a practical and useful new tool for handling large scientific data sets.

The "why" and "what" questions are fairly straightforward. Why would a company employ machine intelligence? To help mine large data sets, recognize patterns and improve the infrastructure of systems. What is answered through the tools from the likes of Google, Facebook, Microsoft and IBM. Now comes the pertinent questions: how do I get into this to help my business.

In a not-too-distant future, autonomous cars, driven largely by AI systems, will hit the road in large numbers. But getting autonomous vehicles on the road is only half the battle. That's because, even after the cars are out there, system operators will need to frequently update their software models and deploy updates to their fleets. While we can all get away with updating our smartphone apps only once every few months for fun, autonomous cars aren't Angry Birds, and their software will need to be updated regularly in order to keep passengers safe. Across the board, auto manufacturers agree that continuously training and deploying updated software models to their fleets is their biggest challenge.