Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing, and complex games such as Go. These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more.
New research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) examines the problem of how a self-driving car can predict the behavior of other drivers on the road. This prediction requires a degree of social awareness which is difficult for machines, so the researchers took tools from social psychology to help the system classify driving behaviors into either selfish and selfless. The system observed human driving behaviors and was then able to better predict the movements of other cars when it came to merging lanes or making unprotected left turns, with 25 percent greater accuracy than previously. This kind of insight into human behavior is important for safety when autonomous and human drivers are sharing the road. An Uber self-driving car which struck and killed a pedestrian last year, for example, didn't have the ability to recognize jaywalkers.
Artificial Intelligence (AI) is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own. We take for granted how our brains effortlessly calculate the world around us, every second of every day. AI is the concept that a computer can do the same. While AI is the broad science of immolating human learning, machine intelligence (MI) is a specific subset of AI that trains the machine how to learn from data.
The operator of a self-driving Uber that hit and killed a pedestrian in Tempe last year was the primary cause of the accident because she was watching "The Voice" on her phone instead of the road. That's the finding from the National Transportation Safety Board, although the federal agency identified several other contributory causes in its final report submitted on Tuesday. The board also recommended new federal and state requirements for testing autonomous cars on public roads. Beyond the driver, the board found plenty of blame to go around for the nation's first pedestrian fatality involving a self-driving car. Officials called out Uber's lax safety culture, the pedestrian who was high on methamphetamine, and the state of Arizona's lack of safety requirements for the cars.
"Passengers who were Female, paying high Fare and travelling 1st class had high probability of surviving. Passengers who were male, paying low Fare and were in 2nd or 3rd class had less probability of surviving" This is amazing, as it like having an expert analyst with you, who would look at rows and columns and try to understand what the data means.
British Airways has introduced advanced neural networks, known as artificial intelligence, to its airside operation at Heathrow Terminal 5. The new system is designed to help staff ensure every flight departs safely and on time. Currently, when customers disembark an aircraft, British Airways' ground staff manually check and record the details of eighteen different activities that need to be completed before the plane can depart for its next flight - including thorough cleaning of the aircraft interiors, unloading and reloading of catering, luggage and cargo and refuelling. An issue affecting just one of these tasks has the potential to disrupt the entire process and delay the flight's departure. Now, using a network of cameras set up around the aircraft stand by technology start-up Assaia, an alumni of British Airways' parent company IAG's Hangar 51 start-up accelerator programme, artificial intelligence is employed to compare live footage of the complex turnaround process with the proposed schedule.
This week, a Sydney-based electric bicycle company Bolt Bikes officially announced its expansion into the US and UK. Unlike other e-bike companies, Bolt Bikes wants its primary customers to be gig workers instead of pedestrians looking to save a little time. For a $39 a week, subscribers will get their own e-bike, a battery charger, a smartphone mount for their handlebars, a U-Lock, and full maintenance and repair coverage. But most importantly, they'll get a shot at becoming an on-demand delivery person for any number of apps that have become popular in recent years, including Uber Eats, Instacart, Deliveroo, and Door Dash. Since June, Bolt Bikes has been running a pilot program in San Francisco with Postmates, a food delivery app first founded in 2011.
BRITISH Airways has announced its plans to cut delays with the help of artificial intelligence. Passengers travelling through Heathrow Terminal 5, the airline's base in London, will be among the first to notice the difference. There are 18 different activities that need to be completed before a plane can depart before its next flight and most of them start once the previous load of passengers have disembarked. It includes everything from cleaning the cabin to loading and unloading catering and luggage. As Sun Online Travel previously revealed, this turnaround process can be extremely tight - with just 45 minutes for short haul journeys on smaller planes - meaning every second counts.
Time Machine 2019, SparkCognition's annual AI summit, was one for the books. Time Machine is a global AI conference featuring 40 leaders across a wide spectrum of industries to discuss the implications of AI. Our 2019 conference just came to a close, but the ideas that were discussed over the course of its two days are going to reverberate for a long time. But even if you weren't able to come join the conversation with us, we'd hate for you to miss out entirely. So here's a rundown of some of the biggest, most important ideas that emerged over the course of the first day of Time Machine 2019: The infrastructure that powers our society is beginning to reach a breaking point.