Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
The models are updated using a CNN, which ensures robustness to noise, scaling and minor variations of the targets' appearance. As with many other related approaches, an online implementation offloads most of the processing to an external server leaving the embedded device from the vehicle to carry out only minor and frequently-needed tasks. Since quick reactions of the system are crucial for proper and safe vehicle operation, performance and a rapid response of the underlying software is essential, which is why the online approach is popular in this field. Also in the context of ensuring robustness and stability, some authors apply fusion techniques to information extracted from CNN layers. It has been previously mentioned that important correlations can be drawn from deep and shallow layers which can be exploited together for identifying robust features in the data.
Apple's HomePod is not scheduled for release until December, but the Cupertino giant may have wanted to out more details about its Amazon Echo competitor. Tim Cook's company has released the device's firmware, and some developers have managed to get their hands on it to get new information about Apple's very own smart speaker. On Friday, Apple reportedly pushed out the HomePod's firmware, and this led developers like Steve Troughton-Smith to learn more about the new device. In his quest to unearth tidbits about Apple's smart speaker, Troughton-Smith found out that the HomePod is going to run the full iOS stack. This is an interesting detail as it suggests that the HomePod would pretty much be like an iPhone sans the screen, MacRumors has learned.
The same artificial intelligence that may soon drive your new car is being adapted to help drive interventional radiology care for patients. Researchers at the University of California, Los Angeles (UCLA), have used advanced artificial intelligence, also called machine learning, to create a "chatbot" or Virtual Interventional Radiologist (VIR). This device communicates automatically with a patient's physicians and can quickly offer evidence-based answers to frequently asked questions. The scientists will present their research today at the Society of Interventional Radiology's 2017 annual scientific meeting in Washington, D.C. This breakthrough will allow clinicians to give patients real-time information on interventional radiology procedures as well as planning the next step of their treatment.