By Amit Aggarwal The modern era of innovation and scientific growth has been largely led by the rise of the machine age. The successful incorporation of automation capabilities with basic human intelligence has resulted in what is termed as "Artificial Intelligence". Artificial Intelligence is used in almost every field today ranging from automated vehicles, healthcare, aviation, finance, entertainment, education, heavy industries and so on. With each passing day, machines with higher and higher capabilities of learning and autonomous thinking are being conceived and implemented. AI has the potential to challenge the core standards that are edifice of Patent law.

The lines are blurring, any which way you look. Thanks to the incredible pace of scientific progress, the very definition of what it means to be'human' is becoming an increasingly open question. In a world where living beings like genetically edited babies and human-animal hybrids are made to exist, the questions aren't only ethical, two biomedical experts argue in a new paper - they're legal, too. The legal definition of what a'human' being is has to adapt or expand somehow, they say, to recognise and protect whatever level of humanity, altered or otherwise, these lab-made life-forms actually possess. "Bioscientific advances are nibbling away at classical legal boundaries that form the bedrock of the normative structures on which societies are based," authors Bartha Maria Knoppers and Henry Greely write in their new policy forum paper.

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.

After San Francisco in May placed new controls, including a ban on facial recognition, on municipal surveillance, city employees began taking stock of what technology agencies already owned. They quickly learned that the city owned a lot of facial recognition technology--much of it in workers' pockets. City-issued iPhones equipped with Apple's signature unlock feature, Face ID, were now illegal--even if the feature was turned off, says Lee Hepner, an aide to supervisor Aaron Peskin, the member of the local Board of Supervisors who spearheaded the ban. Around the same time, police department staffers scurried to disable a facial recognition system for searching mug shots that was unknown to the public or Peskin's office. The department called South Carolina's DataWorks Plus and asked it to disable facial recognition software the city had acquired from the company, according to company vice president Todd Pastorini.

Tweet classification has attracted considerable attention recently. Most of the existing work on tweet classification focuses on topic classification, which classifies tweets into several predefined categories, and sentiment classification, which classifies tweets into positive, negative and neutral. Since tweets are different from conventional text in that they generally are of limited length and contain informal, irregular or new words, so it is difficult to determine user intention to publish a tweet and user attitude towards certain topic. In this paper, we aim to simultaneously classify tweet purpose, i.e., the intention for user to publish a tweet, and position, i.e., supporting, opposing or being neutral to a given topic. By transforming this problem to a multi-label classification problem, a multi-label classification method with post-processing is proposed. Experiments on real-world data sets demonstrate the effectiveness of this method and the results outperform the individual classification methods.

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

Most may at the time of writing associate EU with Brexit since the United Kingdom is pulling out of the union. The European Union and their member countries does together have a population of approximately 500 million and about $22.0 trillion GDP which places EU as the 2nd largest economic force in the world. Therefore by some measures it is an important area to keep track of, and the international strategy for EU relating to AI may be of interest. By summarising some of these policies in a pragmatic way I hope you as a reader understand that this is no substitute for reading the documents, rather an attempt to bring together a few key points. What I provide is of course not a complete picture, rather small excerpts from an ongoing discussion. Looking at the EU strategy it can be hard to understand where to start.

The irony of the ethical scandal enveloping Joichi Ito, the former director of the MIT Media Lab, is that he used to lead academic initiatives on ethics. After the revelation of his financial ties to Jeffrey Epstein, the financier charged with sex trafficking underage girls as young as 14, Ito resigned from multiple roles at MIT, a visiting professorship at Harvard Law School, and the boards of the John D. and Catherine T. MacArthur Foundation, the John S. and James L. Knight Foundation, and the New York Times Company. Many spectators are puzzled by Ito's influential role as an ethicist of artificial intelligence. Indeed, his initiatives were crucial in establishing the discourse of "ethical AI" that is now ubiquitous in academia and in the mainstream press. In 2016, then-President Barack Obama described him as an "expert" on AI and ethics. Since 2017, Ito financed many projects through the $27 million Ethics and Governance of AI Fund, an initiative anchored by the MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University.

Author's Guild v. Google has easily set one of the most important precedents for the field of artificial intelligence, and more explicitly, machine learning. Google claimed that its project represented fair use of the data and that its implementation was the equivalent of a digital age card catalog. The Authors Guild of America and the Publishers Association teamed up against Google and a settlement was proposed after several years of litigation. For various reasons, the settlement was rejected on March 22, 2011. The Publishers Association settled with Google, but the lawsuit with the Author's Guild continued.

We discuss why James wrote this book 3 years ago now, how far away he really thinks we are from artificial human intelligence, the warning bells recently being sounded about artificial intelligence, and why he thinks there will not be another AI winter. Our guest today is James Barrat author of the book "Our final Invention" Artificial Intelligence and the end of the Human Era". It's good to be here. Kathleen Walch: [00:00:39] Great, I'd like to get started by having you introduce yourself to our listeners and to tell us a little bit about your book and also what additional things that you're doing in the field of AI and let's go from there. I got into artificial intelligence, or the study of artificial intelligence, and the critique of AI because I made a film about 17 years ago now about artificial intelligence. I interviewed Ray Kurzweil and Rodney Brooks and Arthur C. Clarke among others … and Ray Kurzweil of course who is now chief engineer at Google and the Google brain project.