Walt Disney World in Florida appears poised to launch the highest-profile commercial deployment of driverless passenger vehicles to date, testing a fleet of driverless shuttles that could cart passengers through parking lots and around its theme parks. According to sources with direct knowledge of Disney's plans, the company is in late-stage negotiation with at least two manufacturers of autonomous shuttles – Local Motors, based in Phoenix, and Navya, based in Paris. It's unclear whether contracts would go to both or just one of the companies. The sources, who asked not be identified to avoid offending Disney, said the company plans a pilot program later this year to transport employees in the electric-drive robot vehicles. If that goes well, they said, the shuttles would begin transporting park visitors sometime next year.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. This is Walt, Audi's new collaborative robot. As part of a Flemish research project lead by prof.

I woke up one morning and realised that my whole world had changed. The dawn of the new digital era was upon us as we shifted overnight into a new trajectory. It meant that old rules didn't apply anymore and a new course of action had to be considered and applied. The future used to be more of the same, but now we can expect something different every day. And then it hit me: we are not equipped for this change. While online advertising has become smarter, intuitive and more personal, many brands are lagging behind, quite oblivious to this visible modification.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using dont cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.

A retired police detective has claimed that Madeleine McCann could be found using facial recognition technology on Facebook. Madeleine vanished from the family's holiday apartment in Praia da Luz in Portugal on May 3, 2007, when she was three years old – with her mother Kate saying the 10th anniversary is a'horrible marker of time, stolen time'. Former Scotland Yard detective chief inspector Mick Neville has revealed that the site's cutting-edge software could help find Maddie, who would now be 14 years old. Mr Neville, a forensics expert, believes the state of the art technology could be used to trace Maddie because of a distinctive blemish in her right eye. He told The Sun: 'If she was still alive -- and there is no proof she is not -- then by using a combined tactic of technology and people with advanced facial recognition skills you could potentially find where Madeleine is today.'

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. The "last mile problem" has long been a thorn in the side of logistics providers, transportation companies, and retailers alike. Compared to the main legs of bulk shipping, train, truck, or aircraft transport, the final leg (or last mile) from logistics hubs to individual homes and offices has traditionally incurred the highest cost and complexity. Last mile challenges have only grown as the proliferation of online shopping strains capacity.

How to make machines learn on their own similar to humans? This is the pivotal question for the development of artificial intelligence. To develop intelligent machines and systems (artificial intelligence), we need to understand how human intelligence and learning work. For this, we will explore the ideas behind reinforcement learning. In the process, we will also explore answers to these seemingly unrelated questions.

Artificial intelligence models may be the new tool to help screen and evaluate efforts in tuberculosis-prevalent areas that often are plagued by limited access to radiologists. In TB-prone areas, there is a lack of trained radiologists qualified to screen and diagnose TB, which can be done using chest imaging techniques. However, the researchers used deep learning, a type of artificial intelligence that allows computers to complete tasks based on existing relationships of data. They modeled a deep convolutional neural network (DCNN) after brain structure to employ multiple hidden layers and patterns to classify images. "There is a tremendous interest in artificial intelligence, both inside and outside the field of medicine," Dr. Paras Lakhani, study co-author and assistant professor of Radiology at Thomas Jefferson University Hospital (TJUH) in Philadelphia, said in a statement.

"Thou canst not touch the freedom of my mind," wrote the playwright John Milton in 1634. But, nearly 400 years later, technological advances in machines that can read our thoughts mean the privacy of our brain is under threat. Now two biomedical ethicists are calling for the creation of new human rights laws to ensure people are protected, including "the right to cognitive liberty" and "the right to mental integrity". Scientists have already developed devices capable of telling whether people are politically right-wing or left-wing. In one experiment, researchers were able to read people's minds to tell with 70 per cent accuracy whether they planned to add or subtract two numbers.

Tuesday is World Malaria Day. The global health community and a coalition of public-private initiatives has successfully begun taming the scourge, with a 21% decrease in its global incidence between 2010 and 2015; still, there were 212 million malaria cases worldwide and the mortality rate from the disease was 29% in 2015, according to the latest World Health Organization (WHO) figures. One key tactic for fighting infectious diseases like malaria is to pinpoint exactly where they're spreading in order to stop them in their tracks. This way, preventive measures like mosquito control and the deployment of treatment resources can be better targeted. Google, the Bill & Melinda Gates Foundation, and the Clinton Health Access Initiative (CHAI) have banded together with academic and public health partners with this very goal in mind--and are harnessing machine learning through the Google Earth Engine to accomplish it. The organizations are taking part in a Malaria Elimination Initiative effort called project DiSARM which will be piloted in Swaziland and Zimbabwe and uses the Google Earth Engine to map malaria.