Ecopia is creating the first HD Map of Waterloo Region. Today, drivers use maps for way-finding and to generally orientate themselves with their surroundings, but as the task of driving shifts from the in-car driver to in-vehicle automation, the role of digital maps shifts significantly. These next generation maps for machines come in the form of a highly accurate and realistic representation of the road, generally referred to as high-definition (HD) maps. The base layers of the Waterloo Region HDMap, created by Ecopia's Global Feature Extraction services, offers a highly accurate and highly attributed representation of the road, including attributes such as lane model, traffic signs, road furniture and lane geometry, as autonomous vehicles need very different maps from those that are currently used in today's navigation systems. HDMaps of Waterloo Region will be available to SMEs and academia on a platform hosted and developed by Ecopia.
Try zeroing in on an orange. While the human brain may correctly identify the images, a machine might mistake them for a missile or jaguar, says Chaz Firestone, assistant professor in the Department of Psychological and Brain Sciences. Those mistakes might seem comical at face value, but could prove deadly if a self-driving car doesn't recognize a person in its path, for example. Or when we begin relying more on automated radiology to screen for anomalies like tumors or tissue damage. "Most of the time, research in our field [of artificial intelligence] is about getting computers to think like people," says Firestone.
Artificial intelligence (AI) is a megatrend that the industry will continue to talk about for the next 20 years. So said Brian Burke, chief of research at Gartner, who presented the top 10 strategic technology trends for 2019 on day three of the Gartner Symposium/ITxpo in Cape Town this morning. Burke said technology is continuing to advance. It is these advancements that underpin Gartner's "Continuous Next" operating philosophy. "AI is going to underlie pretty much everything that we do in technology."
Except they only came to a few departments, mainly the traditionally IT ones. According to Luke Marsden, CEO and founder of Dotscience, enterprise-grade artificial intelligence (AI) and machine learning is being held back, in part, because the mathematicians and statisticians behind it are stuck in the days of Waterfall -- still emailing code back and forth to each other. Dotscience, a collaboration tool for end-to-end machine learning data and model management, recently ran a survey of enterprises. The resulting State of Development and Operations of AI Applications 2019 included results like how 63.2% of businesses reported they are spending between $500,000 and $10 million on their AI efforts. But 60.6% of respondents continued to experience operational challenges.
"There is but one truly serious question in philosophy, and that is suicide," wrote Albert Camus in The Myth of Sisyphus. This is equally true for a human navigating an absurd existence and an artificial intelligence navigating a morally insoluble situation. As AI-powered vehicles take the road, questions about their behavior are inevitable -- and the escalation to matters of life or death equally so. This curiosity often takes the form of asking whom the car should steer for should it have no choice but to hit one of a variety of innocent bystanders. There are a number of reasons this question is a silly one, yet at the same time a deeply important one.
Machine learning & Artificial Intelligence Top Training center in Bangalore ML & AI Coaching Center in Bangalore Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. The field of Artificial Intelligence (ai systems) encompasses computer science, natural language processing, math, psychology, neuroscience, data science, machine learning and many other disciplines.
Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other.
Recently, students on the Peiyangyuan Campus of Tianjin University (TJU) caught their first glimpse of a little blue and white self-driving car moving leisurely around the campus. If students blocked its way, it would automatically slow down and brake. Actually, the driverless vehicle is being used for express delivery and was recently put into use in mid-October. Despite its budding appearance, it is the latest product from Alibaba's Cainiao E.T. Logistics Laboratory, which holds the leading edge in international autopilot capability. The Cainiao unmanned vehicle is moving on the Peiyangyuan Campus of Tianjin University.
We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in'Applications for deep learning in ecology'.
Korean telecommunications company KT announced plans to invest 300 billion won ($257 million) over the next four years to become an artificial intelligence (AI) company, Korean press reported. The Korean telco also said it aims to hire nearly 1,000 specialists in the AI field with the aim of creating new value propositions in line with the deployment of 5G networks in the country. KT rolled out its AI-based service, called Giga Genie, in January of 2017. This AI service was initially offered in the form of a television set-top box. The company has been recently expanding the application of the AI-based service to speakers, apartments, hotels and cars.