Quite recently, the US Patent and Trademark Office (USPTO) has ruled that Artificial Intelligence (AI) systems can't get the credit of a legal inventor in a patent filing. The ruling has come as a response to two Patent Applications filed corresponding to a flashing light and food container, which were created by an AI-based system known as DABUS. The USPTO has presented a lot of arguments concerning the same. The first and foremost argument corresponds to the Patent Law in the US, which repeatedly refers to the patent inventors or innovators by using humanlike pronouns like'himself' and'herself' and terms like'whoever.' The team that filed the patent applications had argued by saying that the patent law's references to an inventor as an'individual' could very well be applied to machines too.

A United Arab Emirates (UAE) drone strike on a biscuit factory near the Libyan capital Tripoli on November 18 killed eight civilians and injured 27 others, Human Rights Watch (HRW) said. In a report released on Wednesday, the rights group said the UAE appeared to take little or no action to minimise civilian casualties and called on Emirati authorities to conduct a transparent investigation into the incident. "Since the current armed conflict in Tripoli erupted in April 2019, the UAE has been conducting air and drone strikes to support the Libyan Arab Armed forces, previously known as the Libyan National Army [LNA], one of two major parties to the conflict, some of which have resulted in civilian casualties," HRW said. "All causalities in the November incident were civilian factory workers, including seven Libyans and 28 foreign nationals, all of them men." Human Rights Watch visited the site and found remnants of at least four Blue Arrow-7 (BA-7) laser-guided missiles that were launched by a Wing Loong-II drone.

Artificial intelligence has myriad use cases, but it turns out inventing devices isn't one of them -- at least in the eyes of the US Patent and Trademark Office. The agency issued a decision on two patent applications for devices created by an AI system, determining that only humans can legally be credited as inventors. The items in question -- an emergency flashlight and a shape-shifting drink container -- were the brainchildren of a system called DABUS. The Artificial Inventor Project filed the applications last year on behalf of the AI's creator, Stephen Thaler. AIP lawyers argued that, since Thaler didn't have any expertise in either of those types of products and couldn't have come up with them by himself, DABUS should be the credited inventor.

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.

An artificial intelligence system has been refused the right to two patents in the US, after a ruling only "natural persons" could be inventors. The US Patent and Trademark Office rejected two patents where the AI system Dabus was listed as the inventor, in a ruling on Monday. US patent law had previously only specified eligible inventors had to be "individuals". It follows a similar ruling from the UK Intellectual Property Office. And its creator, physicist and AI researcher Stephen Thaler, had argued that because he had not helped it with the inventions, it would be inaccurate to list himself as the inventor.

The US Patent and Trademark Office (USPTO) has ruled that artificial intelligence systems cannot be credited as an inventor in a patent, the agency announced earlier this week. The decision came in response to two patents -- one for a food container and the other for a flashing light -- that were created by an AI system called DABUS. Among the USPTO's arguments is the fact that US patent law repeatedly refers to inventors using humanlike terms such as "whoever" and pronouns like "himself" and "herself." The group behind the applications had argued that the law's references to an inventor as an "individual" could be applied to a machine, but the USPTO said this interpretation was too broad. "Under current law, only natural persons may be named as an inventor in a patent application," the agency concluded. The patents were submitted last year by the Artificial Inventor Project.

Companies and governments delegating or supporting decisions in machine learning algorithms provoke concern and even opposition. This is because high-stakes decisions are being automated and there is evidence that algorithms can replicate or amplify existing biases. The problem is that these issues are not fully resolved even for when decisions are made by people, so there are no general criteria that can be clearly transferred to an algorithm. For example, when it comes to promoting gender fairness in recruitment, should men and women have the same opportunity, and should competences determine who gets the position? Or should you fill a vacancy to maintain parity or a quota, even if it involves ignoring more capable candidates? Issues like these always arise when trying to ensure fairness, or avoid discrimination, in any aspect of the human condition where there are illegitimate differences or when there are vulnerable groups.

"Our greatest assets are our team members, and we are committed to continually improving their lives. Whether investing in leadership initiatives, or improving our facilities, we believe the only way you can create a world-class customer experience is by first creating a world-class team member experience." Preface: To tee up the new item produced by Clayton Homes that follows below, some background is useful. First, some related background, then the new items from Clayton. An independent of Clayton Homes that stopped selling their HUD Code manufactured homes some time ago reminded MHProNews about claims that after Warren Buffett bought their brand, they tried cutting the pay of retail general managers.

Utah is putting its AI surveillance system on ice after learning of its creator's background. The state has suspended (via Salt Lake Tribune) Banjo's contract after learning from a OneZero report that company head Damien was part of the Dixie Knights of the Ku Klux Klan as a teenager and joined the racist group's leader in an anti-Semitic drive-by shooting. While Patton has expressed remorse for his past, according to Utah Attorney General Sean Reyes, officials were concerned enough that they felt it was safer to put an advisory committee and independent audit in place to tackle concerns like privacy and "possible bias." Banjo's deal with Utah lets it combine data from city infrastructure (such as public cameras and 911) with internet content to spot "anomalies," theoretically detecting some crimes as they happen. The firm is supposed to strip all personal data from the system, but the methods and effectiveness aren't clear.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the decision-making process of DNNs has blossomed into an active research field. A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking. This complexity is further exacerbated by the general confusion that exists in defining what it means to be able to explain the actions of a deep learning system and to evaluate a system's "ability to explain". To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field.