'Phrenology' has an old-fashioned ring to it. It sounds like it belongs in a history book, filed somewhere between bloodletting and velocipedes. We'd like to think that judging people's worth based on the size and shape of their skull is a practice that's well behind us. However, phrenology is once again rearing its lumpy head. In recent years, machine-learning algorithms have promised governments and private companies the power to glean all sorts of information from people's appearance.

As the use of AI accelerates around the world, policymakers are asking questions about what frameworks should guide the design and use of AI, and how it can benefit society. The EU is the first institution to take a major step to answer these questions through a proposed legal framework for AI released on 21 April 2021. In doing so, the EU is seeking to establish a safe environment for AI innovation and to position itself as a leader in setting "the global gold standard" for regulating AI. This is a positive aspect of the proposal as AI is a broad set of technology, tools and applications. Shifting the focus away from AI technology, which can have significantly different impacts depending on the application for which it is used, helps to mitigate the risk of divergent requirements for AI products and services.

On July 20, California filed an explosive workplace discrimination and harassment lawsuit against Activision Blizzard, publisher of immensely popular video games including World of Warcraft, Overwatch, and the Call of Duty franchise. It has resulted in a shockwave of response from employees, other games studios and players. The lawsuit alleges a "frat bro" culture was allowed to flourish in the office, creating an environment in which women were sexually harassed and discriminated against in advancement and compensation decisions. Activision Blizzard is one of the largest video game publishers in the world, owning studios who have created and released some of the most popular titles over the past decade. Its 2016 acquisition of Candy Crush publisher King, expanded its audience by millions more.

An artificial intelligence system is capable of being an "inventor" under Australian patent law, the federal court has ruled, in a decision that could have wider intellectual property implications. University of Surrey professor Ryan Abbott has launched more than a dozen patent applications across the globe, including in the UK, US, New Zealand and Australia, on behalf of US-based Dr Stephen Thaler. They seek to have Thaler's artificial intelligence device known as Dabus (a device for the autonomous bootstrapping of unified sentience) listed as the inventor. The applications claimed Dabus, which is made up of artificial neural networks, invented an emergency warning light and a type of food container, among other inventions. Several countries, including Australia, had rejected the applications, stating a human must be named the inventor.

All the sessions from Transform 2021 are available on-demand now. Ethics and artificial intelligence have become increasingly intertwined due to the pervasiveness of AI. But researchers, creators, corporations, and governments still face major challenges if they hope to address some of the more pressing concerns around AI's impact on society. Much of this comes down to foresight -- being able to adequately predict what problems a new AI product, feature, or technology could create down the line, rather than focusing purely on short-term benefits. "If you do believe in foresight, then it should become part of what you do before you make the product," AI researcher and former Googler Margaret Mitchell said during a fireside chat at VentureBeat's Transform 2021 event today.

Written by Philip Boucher and Carl Pierer. The European Commission published the much-anticipated Artificial Intelligence Act (AIA), an ambitious cross-sectoral attempt to regulate artificial intelligence (AI) applications on 21 April 2021. Its aim is to ensure that all European citizens can trust AI by providing proportionate and flexible rules – harmonised across the single market – to address the specific risks posed by AI systems and set the highest standards worldwide. The proposal sets out a risk-based approach to regulating AI applications: those presenting an'unacceptable risk' would be banned, those presenting a'high-risk' would be subjected to additional requirements before entering the market, and others, such as chatbots and'deep fakes', would be subject to new transparency requirements. Applications presenting'low or minimal risk' – the vast majority of AI applications – could enter the market without restrictions, although voluntary codes of conduct may be developed. Other proposed measures include a European AI Board to monitor implementation and regulatory sandboxes to facilitate innovation.

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.

This paper presents a novel fingerprinting scheme for the Intellectual Property (IP) protection of Generative Adversarial Networks (GANs). Prior solutions for classification models adopt adversarial examples as the fingerprints, which can raise stealthiness and robustness problems when they are applied to the GAN models. Our scheme constructs a composite deep learning model from the target GAN and a classifier. Then we generate stealthy fingerprint samples from this composite model, and register them to the classifier for effective ownership verification. This scheme inspires three concrete methodologies to practically protect the modern GAN models. Theoretical analysis proves that these methods can satisfy different security requirements necessary for IP protection. We also conduct extensive experiments to show that our solutions outperform existing strategies in terms of stealthiness, functionality-preserving and unremovability.

Apache TVM, Apache, the Apache feather, and the Apache TVM project logo are either trademarks or registered trademarks of the Apache Software Foundation. Apache TVM, Apache, the Apache feather, and the Apache TVM project logo are either trademarks or registered trademarks of the Apache Software Foundation.

Today's supply chains are laden with inefficiencies, as most companies rely on antiquated practices to oversee and manage how goods get from place to place. The supply chain is delicate -- even one disruption among suppliers, buyers, and logistics providers can have a trickle-down effect that causes waste, time loss, and increased carbon emissions. With the supply chain still managed manually, logistics managers are operating under intense pressure, with the sheer amount of data about material supply, demand, and transportation routes overwhelming. Even with machine learning providing managers with intelligent analysis, logistics managers can only react so quickly to the thousands of changes along a single supply chain. As managers are overburdened, their slow reactions to real-time problems and disruptions cause the supply chain inefficiencies that create higher costs, waste, and even greater environmental impact.