Scientists have built an AI tool that finds drone pilots flying dangerously close to airports or protected airspace. The system aims to reduce the risks drones pose to aircraft. Not only can they collide with planes, but they can also interfere with radio signals, causing a pilot to lose control of the aircraft. These risks have already caused chaos at a number of airports. Most notoriously, London's Gatwick airport was forced to shut down in December 2018 after drones were spotted near the runway.
Tesla CEO Elon Musk says the car manufacturer will have the "basic functionality" to deliver level 5 autonomous driving this year. Bloomberg and Reuters report that Musk said Tesla was "very close" to bringing out fully autonomous driving capabilities. He made the claim in a prerecorded video shown at the World AI Conference in Shanghai. "I'm extremely confident that level 5 or essentially complete autonomy will happen and I think will happen very quickly," said Musk. "I remain confident that we will have the basic functionality for level 5 autonomy complete this year." SEE: An IT pro's guide to robotic process automation (free PDF) (TechRepublic) In April last year, Musk said Tesla would probably have fully autonomous driving by the end of the year and that he would be "shocked" if it didn't by the end of 2020 at the latest, at which point "having a human intervene will decrease safety". From the beginning of this month, Tesla sells its Autopilot Full Self-Driving (FSD) for $8,000, up from $7,000.
In the middle of June, I'd discussed why it is a smart move for investors to get in on artificial intelligence (AI). The development of AI has the potential to dramatically reshape our economy and society for decades to come. Today, I want to discuss why Canadians should seek exposure to this space. Moreover, I want to look at two stocks that are betting big on AI to propel their growth going forward. AI development is occurring in a broad array of sectors.
The beginning of modern AI started when the term "Artificial Intelligence" was coined in 1956, at a conference at Dartmouth College, in Hanover. Government funding and interest increased but expectations exceeded the reality which led to AI Winter from 1974 to 1980. British government started funding again to compete with the Japanese but couldn't stop the 2nd AI winter which occurred from 1987 to 1993. In recent years, there's been intense hype about AI but we aren't even near living up to the public expectation. Self-driving cars and health care are currently the most hyped areas of artificial intelligence, the most challenging and are taking much longer than the optimistic timetables.
What comes to mind when you think of Artificial Intelligence (AI)? Maybe you think of robots taking over the world, like in the movies, or self-driving cars. Merriam-Webster defines artificial intelligence as 1) a branch of computer science dealing with the simulation of intelligent behavior in computers and 2) the capability of a machine to imitate intelligent human behavior. Did you know that AI is gradually changing the way consumers shop at various stages of the buyer's journey? In subtle ways, artificial intelligence affects the way a potential buyer searches for product or brand information during the awareness stage.
Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge. Developments in edge computing mean that edge AI is becoming more important.
Machine learning (ML) and artificial intelligence (AI) are frequently imagined to be the gateways to a futuristic world in which robots interact with us like people and computers can become smarter than humans in every way. But of course, machine learning is already being employed in millions of applications around the world--and it's already starting to shape how we live and work, often in ways that go unseen. And while these technologies have been likened to destructive bots or blamed for artificial panic-induction, they are helping in vast ways from software to biotech. Some of the "sexier" applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software can not only self-improve through millions of simulations, it can also adapt on the fly if faced with new circumstances while driving. But ML is possibly even more important in fields like software testing, which are universally employed and used for millions of other technologies. So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?
DeepMap announced it has been named to the Forbes AI 50, a list of the top private companies using artificial intelligence to transform industries. DeepMap develops scalable, high-integrity mapping solutions for autonomous driving. "We are honored to be included on the Forbes AI 50 list and recognized as a technology innovator," said Mark Wheeler, Co-Founder and CTO, DeepMap. "High-definition, centimeter-level precision maps help define the world in terms that a self-driving vehicle can understand. Our technology provides a critical piece of the puzzle for safe autonomy, including Level 2, a category of human-driven vehicles that is a step up from today's advanced driver-assistance systems."