Many methods have been developed to represent knowledge graph data, which implicitly exploit low-rank latent structure in the data to encode known information and enable unknown facts to be inferred. To predict whether a relationship holds between entities, their embeddings are typically compared in the latent space following a relation-specific mapping. Whilst link prediction has steadily improved, the latent structure, and hence why such models capture semantic information, remains unexplained. We build on recent theoretical interpretation of word embeddings as a basis to consider an explicit structure for representations of relations between entities. For identifiable relation types, we are able to predict properties and justify the relative performance of leading knowledge graph representation methods, including their often overlooked ability to make independent predictions.

By 2022, nearly a third of consumers will rely on artificial intelligence to decide what they eat, what they wear or where they live. To keep up with consumer demands, enterprises are adopting AI at a rapid pace, with industries from finance to healthcare embracing the transformational nature of this technology. Yet as virtual assistants become smarter and robots sound more like humans, IT leaders will become responsible for drawing ethical boundaries around the use of this technology. While many frameworks exist for creating an ethical AI strategy, these principles are not set in stone. Rather, formulating an ethical strategy requires a more individualistic, question-based approach.

New policies need to be created for artificial intelligence (AI) in order to govern its use while allowing for innovation, according to the US Chamber's Technology Engagement Center and Center for Global Regulatory Cooperation. "The advent of artificial intelligence will revolutionize businesses of all sizes and industries and has the potential to bring significant opportunities and challenges to the way Americans live and work, said Tim Day, senior vice president, Chamber Technology Engagement Center, in a press release. The principles, "serve as a comprehensive guide to address the policy issues pertaining to AI for federal, state, and local policymakers." The chamber also endorsed the Organization for Economic Co-operation and Development's recommendations for AI. "As leaders in the development and use of AI, the U.S. business community has a strong interest in supporting a global AI ecosystem," said Sean Heather, senior vice president of International Regulatory Affairs, US Chamber of Commerce.

New policies need to be created for artificial intelligence (AI) in order to govern its use while allowing for innovation, according to the US Chamber's Technology Engagement Center and Center for Global Regulatory Cooperation. "The advent of artificial intelligence will revolutionize businesses of all sizes and industries and has the potential to bring significant opportunities and challenges to the way Americans live and work, said Tim Day, senior vice president, Chamber Technology Engagement Center, in a press release. The principles, "serve as a comprehensive guide to address the policy issues pertaining to AI for federal, state, and local policymakers." The chamber also endorsed the Organization for Economic Co-operation and Development's recommendations for AI. "As leaders in the development and use of AI, the U.S. business community has a strong interest in supporting a global AI ecosystem," said Sean Heather, senior vice president of International Regulatory Affairs, US Chamber of Commerce.

In recent times, regulators have begun to explore the use of technology to help them perform their regulatory and supervisory functions. Known as RegTech (a contraction of the terms "regulatory" and "technology") and also SupTech (a contraction of the terms'supervision' and'technology"), innovation in this area includes the use of natural language processing (NLP) – a form of artificial intelligence – to facilitate and enhance the review of documents by regulators to assess compliance with disclosure requirements. There is a broad range of documents to which such technology might be applied, including corporate accounts, corporate announcements, company prospectuses and financial product disclosure documents. Developments in RegTech have accompanied developments in FinTech (for a discussion about FinTech and smart contracts, see China Business Law Journal volume 7 issue 8: FinTech and smart contracts). This column explores the potential that NLP offers in the area of corporate disclosure, and the legal and regulatory implications that arise as a result. These implications include the following: (1) whether technology will change the way in which the language of corporate disclosure and disclosure standards are interpreted by regulators; (2) whether regulators will be able to maintain transparency in relation to how technology is used to monitor and review corporate disclosure; and (3) how to maintain an appropriate degree of human involvement and guarantee trust in the process.

Ethics is, without a doubt, one of the most important topics to emerge in machine learning and artificial intelligence over the last year. While the reasons for this are complex, it nevertheless underlines that the area has reached technological maturity. After all, if artificial intelligence systems weren't having a real, demonstrable impact on wider society, why would anyone be worried about its ethical implications? It's easy to dismiss the debate around machine learning and artificial intelligence as abstract and irrelevant to engineers' and developers' immediate practical concerns. Ethics needs to be seen as an important practical consideration for anyone using and building machine learning systems.

Come join us for a little preview of the upcoming AI Humans Conference where we will feature a full day of Social Good uses cases across such topics as Privacy, Personal Safety, Public Policy, Inclusion, Employment, Education, Legal Justice, Conflict and others. Hear form Joan Wang, of Omelas, who will share her professional journey as an up and coming AI Leader. She will also present on Bias in AI,. This talk is part of our Women in AI Leadership series that features successful woman in the AI field. Our featured speakers are living proof of diversity in AI and have found ways to move forward in the space..

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight global mesh network technology platform for both mobile and fixed solutions, today announced that it commenced R&D activities for machine learning and analog computing, specifically targeting the area of robotics. GBT plans to further research analog computing to drive more improvements in computational efficiency. One of the main aims of this new research and development effort is to provide machine learning algorithms with better resilience to noise and uncertainty, while avoiding a trade-off between accuracy and numerical precision. Analog computing is an innovative emerging field which GBT intends to invest in, in order to enable its AI technology with analog features. GBT will base this new R&D effort on software to be integrated with its existing platforms and hardware.

Despite its role in early 20th-century fiction, AI has been part of the professional conversation for barely 70 years. AI was first studied at a Dartmouth College conference in 1956. The 1960's saw gains in machine translation and analysis. But AI underwent a "winter" from the 1970s through the early '90s. Researchers shelved their work largely because of the problem of "combinatorial explosion." A U.K. professor who first described the AI concept worried that too many variables would make it useless outside of lab settings. In the early '70s, groups like the U.S. Defense Advanced Research Projects Agency pulled funding. Research failures had become the norm. Interest in AI grew during the 1990s and early 2000's. Processing power and data volumes increased. At the same time, data sets grew massively. Algorithms gained more "meat" on which to train. Advances in game theory and data modeling led to new approaches. Today, best-in-class infrastructures can support 100,000 or more computers. Two-and-a-half quintillion bytes of data are now generated every day. Globally, private firms are spending tens of billions of dollars per year researching and improving AI initiatives. In fact, 2018's investment amount is more than 50 percent larger than last year's alone. Add it all up, and AI seems ready for a leap forward unlike any seen in its history. But, after slow decades followed by speedy discoveries, few outside the field feel they truly understand it. A recent Dell Technologies report found that 67 percent of leaders said their companies were struggling to implement AI. A similar two out of three consumers don't even realize they're using it, according to a HubSpot survey. "By far the greatest danger of artificial intelligence is that people conclude too early that they understand it."

With the power to transform the business of government, artificial intelligence offers agencies unprecedented opportunities to discover deeper insights and identify correlations critical to mission success faster than ever. However, faced with legacy infrastructure, an evolving workforce, and limited budgets, how can government actually harness this new wave of technology? During this digital viewcast, hear from government practitioners and industry experts about the practical path to implementing artificial intelligence for agencies of all sizes.