Facebook's F8 developer conference has only been going for a few hours, and already there's been some huge announcements. Gone are the days when F8 was just for announcing newsfeed redesigns - in the opening keynote speeches, we've heard about a brand new Messenger chatbot platform, a solar-powered WiFi plane, and all kinds of live video innovations. Here's some of the main things we learned at the F8 opening event. Zuckerberg straight away took a thinly-veiled swipe at the Republican presidential candidate while talking about Facebook's plan to'connect the world'. Speaking to the assembled crowd of developers, Zuckerberg said: "As I look around the world, I'm starting to see people and nations turning inwards, against the idea of a connected world and global community."

Cuphead, due later this year for Xbox One and PC, looks like Betty Boop meets a shoot'em up meets a miracle. Studio MDHR's game a 2D side-scroller where you do battle with giant paranormal carrots, boxing frogs, angry birds, queen bees, gambling contraptions and not-so-little mermaids. And all of that's hand-sketched, inked and painted to resemble a 1930s Max Fleischer cartoon--an interactive mashup no one's ever attempted before. Cuphead lead artist Chad Moldenhauer gave TIME an exclusive look behind the scenes of how the studio animates the game. TIME: I think it's accurate to say that Cuphead looks like nothing we've seen in video games before. Moldenhauer: We were worried when we first set out with this style that there wouldn't be a huge fan base, just that this had never been tried in games in the past. And there was no real love for cartoons, at least that you could see around people's blogs, or around the Internet, or just talking to our friends.

If robots are taking our jobs, they may also support our retirement. Bank of America Merrill Lynch estimates that 47 per cent of US jobs and 33 per cent of UK jobs have the potential to be automated. But the technology that could fill those jobs – robotics and artificial intelligence – is both a threat to some workers and a potential area of growth for investors, notably pension funds, seeking to capitalise on the new wave of industrialisation. "It's the next generation," says Philippe Cerf, Europe, Middle East and Africa co-head of the technology, media and telecommunications group at investment bank Credit Suisse. "Machine-learning is applied to some extent across all tech these days."

ONLY a handful of South Africans are using artificial intelligence to help solve business and social problems, and to make money. The past year's devastating drought might have been mitigated if information about previous droughts had been used to predict it and to prepare, says Bhekisipho Twala, director of the Institute for Intelligent Systems at the University of Johannesburg. Data scientists could also help Eskom predict the where and when of electricity use, easing its task of keeping the lights on when power supply is constrained. "The whole point of data mining or machine learning is finding hidden information that can make business (or other) decisions easier," says Obakeng Moepya, one of the three founders of Johannesburg-based Isazi Consulting, which uses advanced mathematics and statistics to solve difficult problems. Machine learning -- also known as artificial intelligence -- is a field of computer science that gives computers the ability to learn without being explicitly programmed. Data mining is the analysis step in machine learning, with the goal of extracting patterns and knowledge from large amounts of data.

Major players in the tech industry including Google and Microsoft have bet big money on the rise of internet bots as a form of communication between man and machine. While not the eerily humanoid robots in sci-fi movies (yet), the advancement and refinement of the bots' mechanical psyche parallel to the development of robotic parts is one step closer to merging the two. That is if it hasn't happened already. Isaac Asimov was a prolific sci-fi writer and Professor of Biochemistry at Boston University and is regarded by many as the father of robotics. "The Three Laws of Robotics 1: A robot may not injure a human being or, through inaction, allow a human being to come to harm; 2: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law; 3: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law; The Zeroth Law: A robot may not harm humanity, or, by inaction, allow humanity to come to harm."?

In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we extend BPR to adversarial settings, in particular, to opponents that switch from one stationary strategy to another. Our proposed extension enables learning new models in an online fashion when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach is capable of efficiently detecting opponent strategies and reacting quickly to behavior switches, thereby yielding better performance than state-of-the-art approaches in terms of average rewards.

Planning in hybrid systems is important for dealing with real world applications. PDDL+ supports this representation of domains with mixed discrete and continuous dynamics, and supports events and processes modeling exogenous change. Motivated by numerous SAT-based planning approaches, we propose an approach to PDDL+ planning through SMT, describing an SMT encoding that captures all the features of the PDDL+ problem as published by Fox and Long (2006). The encoding can be applied on domains with nonlinear continuous change. We apply this encoding in a simple planning algorithm, demonstrating excellent results on a set of benchmark problems.

Wildlife poaching presents a serious extinction threat to many animal species. In order to save wildlife in designated wildlife parks, park rangers conduct patrols over the park area to combat such illegal activities. An important aspect of the patrolling activity of the rangers is to anticipate where the poachers are likely to catch animals and then respond accordingly. Previous work has applied defender-attacker Stackelberg Security Games (SSGs) to solve the problem of wildlife protection, wherein attacker behavioral models are used to predict the behaviors of the poachers. However, these behavioral models have several limitations which limit their accuracy in predicting poachers' behavior. First, existing models fail to account for the rangers' imperfect observations w.r.t poaching activities (due to the limited capability of rangers to patrol thoroughly over a vast geographical area). Second, these models are built upon discrete choice models that assume a single agent choosing targets, while it is infeasible to obtain information about every single attacker in wildlife protection. Third, these models do not consider the effect of past poachers' actions on the current poachers' activities, one of the key factors affecting the poachers' behaviors. In this work, we attempt to address these limitations while providing three main contributions. First, we propose a novel hierarchical behavioral model, HiBRID, to predict the poachers' behaviors wherein the rangers' imperfect detection of poaching signs is taken into account --- a significant advance towards existing behavioral models in security games. Furthermore, HiBRID incorporates the temporal effect on the poachers' behaviors. The model also does not require a known number of attackers. Second, we provide two new heuristics: \textit{parameter separation} and \textit{target abstraction} to reduce the computational complexity in learning the model parameters. Finally, we use the real-world data collected in Queen Elizabeth National Park (QENP) in Uganda over 12 years to evaluate the prediction accuracy of our new model.

Perel told ABC News that he initially thought a bomb had been detonated outside the building, adding he was "99 percent ok, I just have a bruise." After realizing what had happened, he quickly removed the memory card from the GoPro camera attached to the DJI drone. "While sitting at my desk I heard what sounded like a missile followed by a huge bang and glass all over me," he wrote in the YouTube video description. "Turns out someone lost control of their drone.

We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). Our estimators generalize those typically used to calculate these statistics on point process data, allowing them to be calculated on RMs which assign continuous values to spatial regions, for instance to model protein intensity. The clustering index (H*) compares Ripley's H function calculated empirically to its distribution under complete spatial randomness (CSR), leading us to consider CSR null hypotheses for RMs which are not point-processes when generalizing this statistic. We thus consider restricted classes of completely random measures which can be simulated directly (Gamma processes and Marked Poisson Processes), as well as the general class of all CSR RMs, for which we derive an exact permutation-based H* estimator. We establish several properties of the estimators, including bounds on the accuracy of our general Ripley K estimator, its relationship to a previous estimator for the cross-correlation measure, and the relationship of our generalized H* estimator to previous statistics. To test the ability of our approach to identify spatial patterning, we use Fluorescent In Situ Hybridization (FISH) and Immunofluorescence (IF) data to probe for mRNA and protein subcellular localization patterns respectively in polarizing mouse fibroblasts on micropattened cells. We observe correlated patterns of clustering over time for corresponding mRNAs and proteins, suggesting a deterministic effect of mRNA localization on protein localization for several pairs tested, including one case in which spatial patterning at the mRNA level has not been previously demonstrated.