Chatbots, or virtual customer assistants (VCAs), will be used in 25 percent of customer service and support operations by 2020, up from just 2 percent in 2017. According to news from Gartner, a leading research and advisory company, more than half of organizations have already invested in VCAs for customer service so they can utilize all the advantages of automated self-service technology. "As more customers engage on digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks," said Gene Alvarez, managing vice president at Gartner, at the Gartner Customer Experience Summit in Tokyo. According to Gartner's research, companies reported a reduction of up to 70 percent in call, chat and/or email inquiries after implementing a VCA, as well as a boost in customer satisfaction and a 33 percent savings per voice engagement. "A great VCA offers more than just information," said Alvarez.

John Giordani is a Cybersecurity expert. In an article in Forbes today, he describes cybersecurity as a war between machines, and it makes perfect sense. AI has changed the rules of computer science, automating what at one time was manual actions by both the attackers and their victims. This is because AI is an instrument, and while it was made by humans, it is morally agnostic. So it can be either good or bad - depending on how it is used.

Artificial intelligence holds a unique place in our collective psychology. Endless imaginings of superintelligent machines and their impact on the human race have been created in films, books and even songs. Some of these versions are incredibly dark – the short story I Have No Mouth and I Must Scream tells of a frustrated hyper-intelligent machine that endlessly and brutally tortures the few remaining humans for consigning it to perpetual boredom. Marvin the Paranoid Android, of A Hitchhiker's Guide to the Galaxy, finds itself in a similar predicament, though expresses it in a somewhat more passive way. Films like I, Robot present a more practical story about the troubled relationship that people might have with intelligent machines.

The world is living under the threat of nuclear war and a terrifying simulation reveals what would happen if a nuclear bomb went off in a major city. As well as looking at the destruction, scientists used the computer model to work out how people would behave if the worst-case scenario struck. An entire city block was obliterated instantly and buildings blasted for a mile in almost every direction. Researchers found people who did nothing were most likely to die with nearly 280,000 people killed in just 48 hours. In the dystopian-like version of The Sims, researchers simulated a nuke exploding in Washington DC (pictured).

Bayesian approaches have been used extensively in scientific and engineering research to quantify uncertainty and extract information. However, its model-dependent nature means that when the a priori model is incomplete or unavailable, there is a severe risk that Bayesian approaches will yield misleading results. Here, we propose a universal model-free information extraction approach, capable of reliably recovering target signals from complex responses. This breakthrough leverages on a data-centric approach, whereby measured data is reconfigured to create an enriched observable space, which in turn is mapped to a well-adapted manifold, thereby detecting crucial information via a reconstructed low-rank phase-space. A Koopman operator is used to transform hidden and complex nonlinear dynamics to linear one, which enables us to detect hidden event of interest from rapidly evolving systems, and relate it to either unobservable stimulus or anomalous behaviour. Thanks to its data-driven nature, our method excludes completely any prior knowledge on governing dynamics. We benchmark the astonishing accuracy of our method on three diverse and challenging problems in: biology, medicine, and engineering. In all cases, our approach outperforms existing state-of-the-art methods, of both Bayesian and non-Bayesian type. By creating a new reliable information analysis paradigm, it is suitable for ubiquitous nonlinear dynamical systems or end-users with little expertise, which permits the unbiased understanding of various mechanisms in the real world.

Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-stationarity due to agents update their policies simultaneously in stochastic cooperative environments. This paper extends the recently proposed weighted double estimator to the multiagent domain and propose a multiagent DRL framework, named weighted double deep Q-network (WDDQN). By utilizing the weighted double estimator and the deep neural network, WDDQN can not only reduce the bias effectively but also be extended to scenarios with raw visual inputs. To achieve efficient cooperation in the multiagent domain, we introduce the lenient reward network and the scheduled replay strategy. Experiments show that the WDDQN outperforms the existing DRL and multiaent DRL algorithms, i.e., double DQN and lenient Q-learning, in terms of the average reward and the convergence rate in stochastic cooperative environments.

The fundamental problem in short-text classification is \emph{feature sparseness} -- the lack of feature overlap between a trained model and a test instance to be classified. We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex $v_i$ in the ClassiNet where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex $v_j$ represents the conditional probability that given $v_i$ exists in an instance, $v_j$ also exists in the same instance. We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance $\vec{x}$, we find similar features from ClassiNet that did not appear in $\vec{x}$, and append those features in the representation of $\vec{x}$. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.

As e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences between students. This paper addresses the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set which is used in two ways: First, to obtain initial estimates over the learning gains for the set of questions. Second, to update the estimates over time based on the students responses. We show in simulations that MAPLE was able to improve students' learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising results. This work demonstrates the efficacy of using stochastic approaches to the sequencing problem when augmented with information about question difficulty.

Police in China arrested a man attending a concert thanks to facial recognition technology in security cameras. A man wanted by police was nabbed at a pop star's concert thanks to facial recognition technology -- which picked the alleged crook out of a crowd of 60,000 people. Chinese police said the suspect – only identified as Mr. Ao – was attending a concert by pop star Jacky Cheung in Nanchang last weekend when he was spotted by CCTV cameras. "The suspect looked completely caught by surprise when we took him away," police officer Li Jin told state news agency Xinhua. "He didn't think police would be able to catch him from a crowd of 60,000 so quickly."

But it will never fully work. "Proposing AI as the solution leaves a very long time period where the issue is not being addressed, during which Facebook's answer to what is being done is, 'We are working on it,'" Georgia Tech AI researcher Mark Riedl told BuzzFeed News. The algorithm hasn't been trained on enough contextual data. The AI isn't good enough, or maybe there aren't enough Burmese-speaking content moderators -- but don't worry, the tools are being worked on. AI automation also gives the company deniability: If it makes a mistake, there's no holding the software accountable.