For the past five years Chinese tech titan Tencent has published content produced by automated software called Dreamwriter, with a focus on business and financial stories. In 2018, an online platform operated by a company called Shanghai Yingxun Technology Company replicated an AI-generated financial report from Tencent on its own website. While the defendant had already removed the article from its own website, it was still required to pay a fine of 1,500 yuan ($217). AI has increasingly infiltrated most industries in recent years, and the creative arts are no different. Google has created an AI-powered tool called AutoDraw, for example, that identifies what someone is trying to draw and then recreates it in digital form.

Just about everyone agrees cybersecurity will be paramount in 2020, and governments and regulatory bodies are already taking action. While GDPR allows citizens in Europe to manage their digital footprint and data, the EU's Cybersecurity Act provides strong support for member nations to alert one another and act against bad actors. Still, cybersecurity is a difficult line of work. It's dynamic, and IT pros often feel harrowed by the amount of ground they're expected to cover. Instead of seeing what new cybersecurity trends will develop in 2020, we thought we'd ask the experts.

Associate Matt Scherer, member of Littler's Robotics, AI and Automation Practice Group and Data Analytics team, and Andrew Arruda, founder of Ross Intelligence, discuss how AI is being used in the practice of law, such as in legal research and contract review. Matt and Andrew differentiate between tasks that are well-suited for AI to tackle (with human oversight), and tasks that continue to require human reasoning and creativity. They also consider how AI may transform legal services, including how machines may be used by the judicial system and may enable easier access to representation and justice. For more information, see Garry Mathiason's article, AI's Transformational Role in Making HR More Objective While Overcoming the Challenge of Illegal Algorithm Biases.

Data collaboration is the process of combining datasets together to generate new value from data-driven insights. The datasets being combined can come from different organizations, or they can come from data silos internal to an organization. A number of use cases are possible through data collaboration: fraud detection, advances in healthcare research, real-world data, cross-selling, churn analysis, etc. However, there are significant blockers in realizing the potential benefits of data collaboration. Some of these blockers are so severe that they can stymie potentially valuable collaborations. The blockers originate from a host of areas -- fear of loss of IP (intellectual property), privacy regulations, data residency restrictions, and reputational risk (just to name a few).

Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.

Diversity is a concept relevant to numerous domains of research varying from ecology, to information theory, and to economics, to cite a few. It is a notion that is steadily gaining attention in the information retrieval, network analysis, and artificial neural networks communities. While the use of diversity measures in network-structured data counts a growing number of applications, no clear and comprehensive description is available for the different ways in which diversities can be measured. In this article, we develop a formal framework for the application of a large family of diversity measures to heterogeneous information networks (HINs), a flexible, widely-used network data formalism. This extends the application of diversity measures, from systems of classifications and apportionments, to more complex relations that can be better modeled by networks. In doing so, we not only provide an effective organization of multiple practices from different domains, but also unearth new observables in systems modeled by heterogeneous information networks. We illustrate the pertinence of our approach by developing different applications related to various domains concerned by both diversity and networks. In particular, we illustrate the usefulness of these new proposed observables in the domains of recommender systems and social media studies, among other fields.

Industry is wary of broad government regulation which could hamper product innovation. In turn, regulators are cautious of what geopolitical impact the tech industry's global growth might have. The focus of the regulation strikes a balance between the two forces. Due to the open source availability of some of the technological elements that power geospatial software, rules in this field are "potentially less impactful than one might imagine," said Robert Cheetham, founder and CEO of Azavea, in an interview with CIO Dive. "Because so much of this work is happening in an open intellectual commons, from which everyone is drawing, contributing and participating in, it narrows the scope of what the regulation could cover," said Cheetham, whose B-corporation builds geospatial applications for civic and social impact.

As we enter into 2020 and make predictions on what lies ahead in the New Year, it's time to look at how artificial intelligence could advance even more rapidly in the next 12 months. During the past few years, AI garnered a vast amount of global attention and became the most buzzworthy term of the decade as tech's next biggest thing. However, too much speculation led some to believe AI might not live up to its hype, and the workforce is eager to start seeing its potential and tangible results. This past year saw a move toward more practical applications in response to this concern. We saw AI become tangible to the enterprise, providing an efficient and scalable method to gain value from information.

The number of patents for inventions based on artificial intelligence, machine learning and deep learning continues to grow rapidly. Some of these inventions relate to AI technology per se, and some relate to the use of AI in specific applications, including many in healthcare, financial services and blockchain, among other industries. The USPTO has addressed various aspects of intellectual property issues with these technologies in various ways, including in an event it hosted entitled "Artificial Intelligence: Intellectual Property Policy Considerations (January 2019)." Due to some of the unique issues with these technologies, the USPTO is considering whether it should make any changes to how it handles examination of these applications. As part of this analysis, the USPTO issued a request for public comments on protection and examination of these inventions.

On Nov. 25, an article headlined "Spot the deepfake. The editors would not have placed this piece on the front page a year ago. If they had, few would have understood what its headline meant. This technology, one of the most worrying fruits of rapid advances in artificial intelligence (AI), allows those who wield it to create audio and video representations of real people saying and doing made-up things. As this technology develops, it becomes increasingly difficult to distinguish real audio and video recordings from fraudulent misrepresentations created by manipulating real sounds and images. "In the short term, detection will be reasonably effective," says Subbarao Kambhampati, a professor of computer science at Arizona State University. "In the longer run, I think it will be impossible to distinguish between the real pictures and the fake pictures."2 The longer run may come as early as later this year, in time for the presidential election.