A technician works in a lab at GeseDNA Technology in Beijing. To counter China, the U.S. plans to impose new export restrictions on "emerging and foundational technology" that researchers say could affect the way they share genetic materials with international labs. A technician works in a lab at GeseDNA Technology in Beijing. To counter China, the U.S. plans to impose new export restrictions on "emerging and foundational technology" that researchers say could affect the way they share genetic materials with international labs. For the last 15 years, Addgene has dedicated itself to accelerating medical research.

Artificial Intelligence is on the cusp of becoming the biggest technology of the information age, says Horacio Rozanski, president and CEO of Booz Allen Hamilton. However, we need to bake human judgement into it before it is too late, he writes in this opinion piece. The exponential pace of technological advancement has made it more challenging than ever before to address its unintended consequences. We have seen it with digital innovation, especially social media, and we are only just beginning to contemplate it with artificial intelligence, which holds the promise of being the most transformational technology of the information era. The unanticipated consequences of digital innovation have been well documented.

Industry has mobilized to shape the science, morality and laws of artificial intelligence. On 10 May, letters of intent are due to the US National Science Foundation (NSF) for a new funding programme for projects on Fairness in Artificial Intelligence, in collaboration with Amazon. In April, after the European Commission released the Ethics Guidelines for Trustworthy AI, an academic member of the expert group that produced them described their creation as industry-dominated "ethics washing". In March, Google formed an AI ethics board, which was dissolved a week later amid controversy. In January, Facebook invested US$7.5 million in a centre on ethics and AI at the Technical University of Munich, Germany.

We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially. Each other node in the network is a potential buyer with a valuation derived from a common distribution. With a standard fixed-price mechanism, the seller can only sell the item among her neighbours. To improve her revenue, she needs more buyers to join in the sale. To achieve this, we propose the very first fixed-price mechanism to incentivize the seller's neighbours to inform their neighbours about the sale and to eventually inform all buyers in the network to improve seller's revenue. Compared with the existing mechanisms for the same purpose, our mechanism does not require the buyers to reveal their valuations and it is computationally easy. More importantly, it guarantees that the improved revenue is at least 1/2 of the optimal.

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal distributions based on a type of bidirectional continuous-time LSTM: Bidirectionality lets the proposals condition on future observations, not just on the past as in particle filtering. Our method can sample an ensemble of possible complete sequences (particles), from which we form a single consensus prediction that has low Bayes risk under our chosen loss metric. We experiment in multiple synthetic and real domains, using different missingness mechanisms, and modeling the complete sequences in each domain with a neural Hawkes process (Mei & Eisner 2017). On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.

Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list. In this paper we study the effect of an adversarial contamination model on the performance of a spectral graph embedding-based vertex nomination scheme. In both real and simulated examples, we demonstrate that this vertex nomination scheme performs effectively in the uncontaminated setting; adversarial network contamination adversely impacts the performance of our VN scheme; and network regularization successfully mitigates the impact of the contamination. In addition to furthering the theoretic basis of consistency in vertex nomination, the adversarial noise model posited herein is grounded in theoretical developments that allow us to frame the role of an adversary in terms of maximal vertex nomination consistency classes.

Automated Planning is one of the main research field of Artificial Intelligence since its beginnings. Research in Automated Planning aims at developing general reasoners (i.e., planners) capable of automatically solve complex problems. Broadly speaking, planners rely on a general model characterizing the possible states of the world and the actions that can be performed in order to change the status of the world. Given a model and an initial known state, the objective of a planner is to synthesize a set of actions needed to achieve a particular goal state. The classical approach to planning roughly corresponds to the description given above. The timeline-based approach is a particular planning paradigm capable of integrating causal and temporal reasoning within a unified solving process. This approach has been successfully applied in many real-world scenarios although a common interpretation of the related planning concepts is missing. Indeed, there are significant differences among the existing frameworks that apply this technique. Each framework relies on its own interpretation of timeline-based planning and therefore it is not easy to compare these systems. Thus, the objective of this work is to investigate the timeline-based approach to planning by addressing several aspects ranging from the semantics of the related planning concepts to the modeling and solving techniques. Specifically, the main contributions of this PhD work consist of: (i) the proposal of a formal characterization of the timeline-based approach capable of dealing with temporal uncertainty; (ii) the proposal of a hierarchical modeling and solving approach; (iii) the development of a general purpose framework for planning and execution with timelines; (iv) the validation{\dag}of this approach in real-world manufacturing scenarios.

Swarms of AI driven robots that can patrol borders from the land, sea and sky could soon be manning Europe's borders. A mass network of drones, land vehicles and water crafts is being developed into fully automated surveillance systems with the support of EU countries. Multiple sensors on roaming robots will be able to identify and track human activity and report risks to border officials. Concerns have been raised that such powerful technology in the wrong hands have could be used to develop'killer robots'. Swarms of AI driven robots that can patrol borders by surveillance of the sea (top left) and land (top right) could soon be manning Europe's borders.

A soft plastic'robot' that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond. It has been made using 3D printing technology with a mould and liquid plastic that cools to form the final structure. It has been designed with internal air pockets that can be inflated and deflated to create different shapes, sizes and strengths out of silicon. Their plasticity means they are more resistance to hard hits and can take on multiple functions, even joining together to form temporary mega structures. A soft plastic'robot' (pictured) that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond Two interns, Chuck Sullivan and Jack Fitzpatrick,working in NASA's Langley's Makerspace Lab, have created a small plastic device that could be the prototype of a future'collapsible' space robot.

Locations of enemy snipers shooting at troops may soon be revealed instantly on the smartphones of the ambushed troops. Cutting-edge audio technology is being developed to use microphones in the ears of the soldiers to track two notable noises from a bullet - supersonic air in front of the bullet and the blast as it leaves the muzzle. Technology is being developed to use these two sounds to trace the original location and reveal where it was fired from. The data and location will then be relayed to the handset of the beleaguered troops to help them identify and neutralise the threat. Audio experts that developed the technology say it builds on existing technology and could be employed on the battlefield in just two years.