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Trump says Biden has 'obligation' to take a cognitive test, presidency requires 'stamina' and 'mental health'

FOX News

President Trump joins Fox News medical contributor Dr. Marc Siegel for an exclusive interview on'Tucker Carlson Tonight.' In an interview Wednesday with Fox News medical contributor Marc Siegel, President Trump boasted about his cognitive test performance and said presumptive Democratic presidential candidate Joe Biden should take the test. "In a way he has an obligation to," Trump said, adding that the presidency requires "stamina" and "mental health." Trump said he took the test to prove to the media that he was fit to serve in the presidency after reports supposedly questioning his cognitive ability. Trump has used the argument that Biden is too old to run for president as a cornerstone strategy in his presidential campaign against the former vice president.

FTC's Tips on Using Artificial Intelligence and Algorithms


Artificial intelligence (AI) technology that uses algorithms to assist in decision-making offers tremendous opportunity to make predictions and evaluate "big data." The Federal Trade Commission (FTC), on April 8, 2020, provided reminders in its Tips and Advice blog post, Using Artificial Intelligence and Algorithms. This is not the first time the FTC has focused on data analytics. In 2016, it issued a "Big Data" Report. AI technology may appear objective and unbiased, but the FTC warns of the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities.

Chatbot for attorneys KLoBot AI


This is a Legally binding agreement between you (either an individual or a single entity) and KLoBot, Inc. ("KLoBot" or "KLOBOT") for the "KLoBot" Software and associated media and printed materials, and many include online or electronic documentation for KLoBot Software Product or KLoBot Software (collectively "KLoBot"). By installing, copying or otherwise using KLoBot, you are agreeing to be bound by the terms and conditions of this agreement, including KLoBot license and disclaimer of KLoBot software warranty below. Please read this document carefully before using KLoBot. If you do not agree with the terms and conditions of this agreement you should not install or use KLoBot. In consideration for your payment of any applicable license fee for KLoBot, KLoBot hereby grants to you a personal, non-transferable (except as expressly provided in Section 4 below) and non-exclusive right to use and execute KLoBot on a single Microsoft Azure Tenant, without right to sublicense KLoBot.

NexOptic Introduces ALIIS INN


NexOptic Technology (TSXV:NXO) (OTCQB:NXOPF) (FRANKFURT:E3O1) today introduced All Light Intelligent Imaging Solutions (ALIIS). NexOptic Technology Corp. ("NexOptic" or the "Company") (TSXV:NXO) (OTCQB:NXOPF) (FRANKFURT:E3O1) today introduced All Light Intelligent Imaging Solutions (ALIIS). This new artificial intelligence technology replaces Advanced Low Light Imaging Solution (ALLIS) thanks to significant upgrades and added functionality. The new All Light solution suite is the result of significant re-engineering of NexOptic's proprietary machine learning algorithms to encompass virtually all light environments and enable super high-resolution functionality. ALIIS pushes the limits of traditional imaging in all lighting conditions, adding substantial value to all camera users.

Modeling Contrary-to-Duty with CP-nets Artificial Intelligence

Modelling deontic notions through preferences [12] has the advantage of linking deontic notions to the manifold research on preferences, in multiple disciplines, such as philosophy, mathematics, economics and politics. In recent years, preferences have also been addressed within AI [15,8,18] and applications can be found in multi-agent systems [19] and recommender systems [17]. We shall model deontic notions through ceteris-paribus preferences, namely, conditional preferences for a state of affairs over another state of affairs, all the rest being equal. In particular, we shall focus on the ceteris-paribus preference for a proposition over its complement. The idea of ceteris-paribus preferences was originally introduced by the philosopher and logician Georg von Wright [22].

Utilising AI in financial compliance -- Financier Worldwide


Given the increasing compliance demands on organisations from the public and regulators alike, companies cannot afford to neglect their compliance obligations. The role of compliance is to prevent, detect, respond to and remediate risk and financial institutions (FIs) must use all of the tools at their disposal to achieve it. Implementing an effective compliance framework requires the whole firm to be on board, from the C-suite down. The compliance function is in a state of flux, however – virtually unrecognisable from a decade ago. The catalyst for much of the change in the financial services industry was the global financial crisis.

How AI can address the global sanctions challenge


Today's global sanctions regimes have arguably never been more challenging for organisations to ensure they remain compliant and have the required screening processes and procedures in place. Over the past decade, trade and economic sanctions have become an ever more popular tool of foreign policy in an increasingly uncertain geo-political climate. Aside from country-specific sanctions, such as those against Iran, Russia, North Korea, etc, more targeted regulations focus upon particular businesses or individuals. As a result, national and international AML, screening and anti-fraud obligations have increased in both scope and complexity. Failure to comply with sanctions and money laundering obligations, can result in severe financial and reputational costs.

Machine Understandable Policies and GDPR Compliance Checking Artificial Intelligence

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...

The Windfall Clause: Distributing the Benefits of AI for the Common Good Artificial Intelligence

As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity. This in turn has spurred debate on the social responsibilities of large technology companies to serve the interests of society at large. In response, ethical principles and codes of conduct have been proposed to meet the escalating demand for this responsibility to be taken seriously. As yet, however, few institutional innovations have been suggested to translate this responsibility into legal commitments which apply to companies positioned to reap large financial gains from the development and use of AI. This paper offers one potentially attractive tool for addressing such issues: the Windfall Clause, which is an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits. By this we mean an early commitment that profits that a firm could not earn without achieving fundamental, economically transformative breakthroughs in AI capabilities will be donated to benefit humanity broadly, with particular attention towards mitigating any downsides from deployment of windfall-generating AI.

Towards a computer-interpretable actionable formal model to encode data governance rules Artificial Intelligence

Towards a computer-interpretable actionable formal model to encode data governance rules Rui Zhao School of Informatics University of Edinburgh Edinburgh, UK Malcolm Atkinson School of Informatics University of Edinburgh Edinburgh, UK Abstract --With the needs of science and business, data sharing and reuse has become an intensive activity for various areas. In many cases, governance imposes rules concerning data use, but there is no existing computational technique to help data-users comply with such rules. We argue that intelligent systems can be used to improve the situation, by recording provenance records during processing, encoding the rules and performing reasoning. We present our initial work, designing formal models for data rules and flow rules and the reasoning system, as the first step towards helping data providers and data users sustain productive relationships. I NTRODUCTION Data ethics and privacy are of rising importance, especially with the establishment of GDPR [1]. Similar issues also apply in research when data from various sources are used as inputs to analyses and simulations. Researchers are aware that there are governance rules applied to the data, but they can easily lose track of the rules when the number of sources becomes large. The large volume of rules brings problem from three aspects: 1) to fully read and understand the rules; 2) to consider the consequence of combining data and their associate rules; 3) to assign rules to output so that results can be used compliantly. One response is to make data open and freely accessible (e.g. This sounds nice but it still leaves rules, for example to properly acknowledge sources and to protect personal and commercially sensitive data, even within collaborating communities [4]. Moreover, this doesn't solve (or even decrease) the prevalent polarization: data are either completely public (with one or a few well-known commonly agreed governance rules) or completely under control with heterogeneous (yet potentially similar) governance rules written in different languages, similar to the situation for copyright licenses.