If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
ANA Holdings Inc., the operator of All Nippon Airways Co., said Wednesday it has started testing a semi-autonomous bus that will transport passengers and staff working at Tokyo's Haneda Airport. The company will conduct the test with the electric bus capable of carrying 57 passengers on a 1.9-kilometer route through the end of this month, aiming to start trial operation later in the year. The vehicle, with level-3 automation, allows drivers to turn their attention away from driving and engage in different tasks. "As the Tokyo Olympics are approaching, we hope more passengers from around the world will see our latest technology," ANA Senior Executive Vice President Shinzo Shimizu said in a ceremony at the airport. In 2018, the number of passengers who arrived at and departed from the airport increased 2.1 percent to 85 million, according to Japan Airport Terminal Co. which manages the Haneda Airport facilities.
Advances in biological and medical technologies drive continuous generation of large amounts of biomedical Big Data. This will double in less than 3 years if the current rate of growth is sustained! Given the exponential progress in sequencing technology the increase will only get steeper, entailing an intensified demand for experts in NGS data analysis. Big Data requires applying new solution to leverage its potential. Machine Learning (ML) is the answer to the increased complexity of research problems in science, industry and in everyday life.
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Innovating machine learning with near-term quantum computing" Maria Schuld - University of KwaZulu-Natal & Xanadu Abstract: Algorithms that run on quantum computers - so-called quantum circuits - underlie different laws of information processing than conventional computations. By optimizing the physical parameters of quantum circuits we can turn these algorithms into trainable models which learn to generalize from data. This talk highlights different aspects of such "variational quantum machine learning algorithms", including their role in the development of near-term quantum technologies, their interpretation as a cross-breed of neural networks and support vector machines, strategies of automatic differentiation, and how to integrate quantum circuits with machine learning frameworks such as PyTorch and Tensorflow using open-source software.
More than 100 sessions dedicated to PyData (artificial intelligence, machine learning, ethics...) and Python topics (programming, DevOps, Web, Django...). Sprints are an informal part of the conference, where all are welcome to exchange ideas, hack on exciting projects, and create lasting connections.
As modern supply chains transform into highly-connected digital networks, you now have the power to respond to disruptions before they occur and make better business decisions in real-time. Learn how JDA and Blue Yonder are putting cutting-edge AI and machine learning technology to work for retailers, right now.
The use of flexible actuators in robotic systems can realize certain intelligent functions and migrate academic research to engineering applications. Herein, the flexible actuators that rely on different external stimuli are addressed, and their materials, designs, and approaches suitable for soft robotic applications are highlighted. Their application advancement and future perspective toward various soft robotics are also discussed.
If we go back in history, we will see that Linear Programming (LP) was the first to appear. It will be fair to say that LP fostered the economic development of a great number of countries in the 20th century. Nevertheless, as plenty of real-world problems are nonlinear, there was a need for new kind of solvers. Via Quadratic Programming (QP), the methods of Nonlinear Programming (NLP) were developed. But even they sometimes do not provide us with the best solution.
Fully self-driving cars are still a thing of the future. But in today's laboratories, the technology ranges from commonly used cruise control systems to so much automation that humans don't need to get into a car at all. In Taiwan, a startup is developing a driver's cockpit that's comfortable and packed with artificial intelligence features that transfers control of the vehicle to the computer whenever the system senses that the human driver is sick, tired, distracted or just sloppy. The 3-year-old Taipei-based Mindtronic AI developed this cockpit, called DMX, last year with luxuries like easy-to-use entertainment for the driver. But what if the driver gets mesmerized by a soccer match?
New technologies are poised to challenge assumptions that AI and robotics will be used to perform only low-level and highly repetitive tasks. Over the past decade, U.S. tech firms have made significant advancements in artificial intelligence and robotics, making it far easier and more efficient to automate tasks and functions across industries. Artificial intelligence (AI) affects all types of risks and lines of insurance, and the workers' compensation market has a particularly large stake in the developments. Although the U.S. has experienced technological change and disruption during prior periods of industrial revolution, the pace and scope of the fourth industrial Revolution positions it to have a far greater impact on the U.S. and global economies. The recent advancements in AI and robotics are some of the most significant computer science advancements of our generations.