Blockchain technology and artificial intelligence, two cutting-edge technologies, have the potential to change the face of healthcare as we know it by improving the quality and reducing costs through improved efficiencies. Most of us are at least somewhat familiar with artificial intelligence primarily through virtual assistants such as Siri and Alexa. Artificial intelligence automates repetitive learning and discovery through data after initially being set up by a human being. As many people also know, you have to be fairly specific when asking Siri and Alexa any questions -- the question must be posed in the right way -- to get the answer you are looking for. As an example, our interactions with Alexa, Siri, Google Search and Google Photos are based on deep learning.
Kumar, T. K. Satish (Information Sciences Institute, University of Southern California) | Xu, Hong (Information Sciences Institute, University of Southern California) | Tang, Zheng (Information Sciences Institute, University of Southern California) | Kumar, Anoop (Information Sciences Institute, University of Southern California) | Rogers, Craig Milo (Information Sciences Institute, University of Southern California) | Knoblock, Craig A. (Information Sciences Institute, University of Southern California)
A simple temporal network (STN) can often be used to represent the flexibility in the execution of a plan. Nodes represent the execution times of actions and directed edges represent constraints between them. An STN with resources (STNR) is an STN in which each node is associated with production or consumption levels of resources. The upper (lower) resource envelope of an STNR is the maximum (minimum) accumulated resource levels at every time instant over all possible executions. In this paper, we discuss the usefulness of resource envelopes in the context of execution monitoring. We show that they can be used in a tractable framework for forward projection of world states during plan execution. This allows an execution monitor to recognize depletion of resources early and to generate alerts with large look-aheads. It also supports retasking by enabling “what-if reasoning” during plan execution.
Over the last two years, academic researchers have identified various methods that they can transmit hidden commands that are undetectable by the human ear to Apple's Siri, Amazon's Alexa, and Google's Assistant. According to a new report from The New York Times, scientific researchers have been able "to secretly activate the artificial intelligence systems on smartphones and smart speakers, making them dial phone numbers or open websites." This could, perhaps, allow cybercriminals to unlock smart-home doors, control a Tesla car via the App, access users' online bank accounts, load malicious browser-based cryptocurrency mining websites, and or access all sort of personal information. In 2017, Statista projected around 223 million people in the U.S. would be using a smartphone device, which accounts for roughly 84 percent of all mobile users. Of these 223 million smartphones users, around 108 million Americans are using the Android Operating System, and some 90 million are using Apple's iOS (operating system).
While this reality has become more tangible in recent years through consumer technology, such as Amazon's Alexa or Apple's Siri, the applications of AI software are already widespread, ranging from credit card fraud detection at VISA to payload scheduling operations at NASA to insider trading surveillance on the NASDAQ. Broadly defined as the imitation of human cognition by a machine, recent interest in AI has been driven by advances in machine learning, in which computer algorithms learn from data without human direction.1 Most sophisticated processes that involve some form of prediction generated from a large data set use this type of AI, including image recognition, web-search, speech-to-text language processing, and e-commerce product recommendations.2 AI is increasingly incorporated into devices that consumers keep with them at all times, such as smartphones, and powers consumer technologies on the horizon, such as self-driving cars. And there is anticipation that these advances will continue to accelerate: a recent survey of leading AI researchers predicted that, within the next 10 years, AI will outperform humans in transcribing speech, translating languages, and driving a truck.3
The terms "machine learning" and "artificial intelligence" (AI) conjure up feelings that are equal parts fear and fascination. Until recently, the prospect of a piece of software making human-like decisions resided safely in the far-fetched expectations of 1960s-era computer scientists or the plot lines of science fiction novels. Today, however, after decades of unmet expectations, we finally have AI systems that are beginning to influence our lives in tangible ways. Voice recognition systems like Amazon's Echo and Apple's Siri, and once-unimaginable fantasies like self-driving cars, are on the market for consumers, with more exciting life-like systems to come. We have also seen a few early signs of robotic autonomy that makes us feel uneasy, like the Russian robot that learned how to escape the lab!