Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data among different clients. We conduct a case study to show that heterogeneity in resource and data has a significant impact on training time and model accuracy in conventional FL systems. To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity. To further tame the heterogeneity caused by non-IID (Independent and Identical Distribution) data and resources, TiFL employs an adaptive tier selection approach to update the tiering on-the-fly based on the observed training performance and accuracy overtime. We prototype TiFL in a FL testbed following Google's FL architecture and evaluate it using popular benchmarks and the state-of-the-art FL benchmark LEAF. Experimental evaluation shows that TiFL outperforms the conventional FL in various heterogeneous conditions. With the proposed adaptive tier selection policy, we demonstrate that TiFL achieves much faster training performance while keeping the same (and in some cases - better) test accuracy across the board.

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.

Ea ch process description is shaped like a formalized business policy consisting of the following set of features: - the file(s) to be processed; - the software that carries out the processing; - the purpose of the processing; - the entities that can access the results of the processing; - the details of where the results are stored and for how long; - the obligations that are fulfilled while (or before) carrying out the processing; - the legal basis of the processing. It is not hard to see that the first five elements in the above list match SPECIAL's usage policy language (UPL) introduced in Section 3. As far as the above elements are concerned, the only difference between UPL expressions and a business policy is the granularity of attribute values. Fo r example, the involved data (specified in the first element of the above list) are not expressed as a general, content-oriented category, but rather as a concrete set of data sourc es or data items. Such objects can be modeled as instances or subclasses of the general data categories illustrated in Section 3, thereby creating a link between digital artifacts and usage policies. Similar considerations hold for the other a t-tributes: - processing is not necessarily described in the abstract terms adopted by the processing vocabulary introduced in Section 3; in a business policy, this can be specified by naming concrete software procedures; - the purpose of data processing may be directly related to the data controller's mission and products; - recipients may consist of a concrete list of legal and/or physical persons, as opposed to general categories such as Ours or ThirdParty; - storage may be specified by a list of specific data repositories, at the level of files and hosts. With this level of granularity, specific authorizations can be derived from the business policy, for example: The indicated software procedure can read the indicated data sources. The results can be written in the specified repositories. The specified recipients can read the repositories...

Casino executives, industry analysts and lawyers attended a conference at the UNLV Boyd School of Law to consult on how biometrics, AI and machine learning could shape the future of Las Vegas casinos, writes the Nevada Independent. While there are many opportunities for the gaming industry, most machine learning and facial recognition-enabled product ideas addressed customer service and customer recognition. These include slot machines that leverage facial biometrics to recognize important or banned players, and reduce fraud attempts, or facial recognition-equipped tables to help pit managers identify and track known players. "What we're seeing is this introduction of technology into the gaming industry in ways we've never seen before, and because of it, it started to raise issues -- or questions -- as to how this works and what the ramifications could be for things like patron privacy, anonymity and data protection," said Anthony Cabot, Distinguished Fellow in Gaming Law at the UNLV Boyd School of Law and event organizer. While speakers focused on presentations about competing laws and technology problems, there was not enough discussion on how to solve these problems, according to the report, yet Cabot hopes the gaming industry and regulators will join forces to deliver solutions.

Cohort of 14 U.S. and international startups to relocate to Bentonville for 12 weeks PRESS RELEASE – The first-ever Arkansas-based artificial intelligence and machine learning accelerator will launch later this month, with the goal of helping a cohort of startups within these fields connect to regional enterprise partners. The Fuel Accelerator, in its second iteration, will provide regular, hands-on education and workshops to a cohort of 14 companies from across the United States, Europe and Asia. These 14 companies will make their way to Northwest Arkansas, at the foot of the Ozark Mountains, for a 12-week, enterprise-ready accelerator that will provide them with access to other startup founders, industry experts, institutions of higher education, and public policy officials. Fuel launched in late 2018 with eight startups participating in a supply chain-focused, 16-week program. The program helped its first cohort nurture relationships with key Fortune 500 companies through feedback sessions, training, pilots and demos.

This blog post is adapted from our June 10 response to the National Institute of Standards and Technology's (NIST) request for information (RFI) 2019-08818: Developing a Federal AI Standards Engagement Plan. This RFI was released in response to an Executive Order directing NIST to create a plan for the development of a set of standards for the acceptable use of AI technologies. Given the wide adoption of AI technologies and the lag in commensurate laws and regulations, this post aims to help NIST by highlighting the current state, plans, challenges, and opportunities in ethics and AI. In 2016 the European Union (EU) created the General Data Protection Regulation (GDPR) that would expand protections around EU citizens' personal data beginning in 2018. Meanwhile, China has extensively integrated AI technologies into their government and social structure via the China Social Credit System.

The value of building data-driven businesses with AI at their core is well known today, and business executives are rushing to implement the technology into their operations and gain a competitive advantage, but it's not as simple as creating a data lake and crafting AI models. A large number of AI companies attempting to implement more AI models or build AI-first businesses have experienced challenges. A December 2018 PwC survey found that only 4% of businesses have successfully implemented AI. That's why today the World Economic Forum released the AI toolkit for Boards of Directors. The AI toolkit for Boards of Directors is being released ahead of the annual WEF meeting in Davos, Switzerland where the toolkit will be formally debuted next week.

São Paulo, January 2020 - Virtual assistants are increasingly present in people's routine, whether to help, answer questions and facilitate daily life. What they all have in common are women's names and the standard female voice, such as Lu, Siri, Alexa, Nat, Bia, etc. According to a study entitled "I'd Blush If I Could" published by UNESCO in May 2019, virtual assistants via Artificial Intelligence suffer from high levels of gender prejudice, although they usually answer with tolerant, subservient and passive phrases. Based on this context, the "Hey Update My Voice" movement was launched in partnership with UNESCO with the objective of drawing attention to cyber education and respect for virtual assistants, and ask companies to update their assistants' responses. If even virtual assistants are harassed, can you imagine how many women are victims of this kind of violence?

Artificial Intelligence (AI) is advancing at an unprecedented rate and with a dynamism that is really incomparable to any other field in tech. This has made much fear about AI that it can not only take human jobs but also be used for causing disharmony in people's lives while impacting their privacy, among other things. He urged authorities to create a framework in order to approach AI. He said, in a conversation with Klaus Schwab, "AI is one of the most profound things we are working on as humanity; it's more profound than fire or electricity or any of the other bigger things we have worked on. It has tremendous positive sides to it, but it has real negative consequences."

In the decision, the UKIPO Hearing Officer, Huw Jones, citing sections 7 and 13 of the Act (The Patents Act 1977) and Rule 10 of the Rules (The Patents Rules 2007), Officer Jones said "the Office accepts that DABUS created the inventions" in the patent applications but that as it was a machine and not a natural person, it could not be regarded as an inventor. Moreover, as DABUS has no rights to the inventions, the Officer stated it is unclear how the applicant derived the rights to the inventions from DABUS: "There appears to be no law that allows for the transfer of ownership of the invention from the inventor to the owner in this case, as the inventor itself cannot hold property." Id. at p. 6. Officer Jones further noted that while he agreed inventors other than natural persons were not contemplated when the EPC was drafted, "it is settled law that an inventor cannot be a corporate body." Accordingly, since the "applicant acknowledges DABUS is an AI machine and not a human, so cannot be taken to be a'person' as required by the Act." However, the Hearing Officer also added that the case raised an important question: given that an AI machine cannot hold property rights, in what way can it be encouraged to disseminate information about an invention?