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Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence

#artificialintelligence

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...


Inside AI: Technology Landscape of Artificial Intelligence

@machinelearnbot

AI Clouds: Lego blocking cloud based services with developer kits, large general purpose AI companies are enabling developers to deploy algorithms via SDKs within their cloud hosted platforms. From Microsoft Azure AI platform all the way to Amazon's AWS AI Offerings, these organizations provide pre-trained models, GPUs and storage that are necessary for more effective continuous deployment, testing and quality assurance (QA). AI Languages: Beyond software applications to onboard users onto AI platforms, companies are standardizing new languages to familiarize developers to continually build using their libraries. Uber's AI Labs, for example, released their own probabilistic python offshoot programming language, Pyro. Wit.ai is another language for developers to build cross device applications.


Towards a Framework for Certification of Reliable Autonomous Systems

arXiv.org Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.


Uber is Planning Self-Flying Drone Taxis

U.S. News

Uber head of product Jeff Holden told Recode that, unlike helicoptors, "VTOL aircraft could have multiple rotors, could have fixed wings and perhaps eventually would use batteries and be more silent." The Telegraph notes that Amazon's delivery drones are employing a similar technology.