Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Mashable Selects Mashable Voices Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series Will Forte and John Cena face off over the fate of Wile E. Coyote in this silly but smart (and nearly shelved) comedy. Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

We present ACME: A Chatbot for asylum-seeking Migrants tool that goes beyond the checklists used for handling well-defined, in Europe. ACME relies on computational argumentation and simple procedures since there is not only a problem of evaluating aims to help migrants identify the highest level of protection they legal and factual data, but there is also an issue with understanding can apply for. This would contribute to a more sustainable migration which procedures are relevant. Indeed, there is not only one type of by reducing the load on territorial commissions, Courts, and humanitarian protection but several ones. Importantly, since applicants may be political organizations supporting asylum applicants. We describe the refugees and victims of abuse, discrimination, and persecution, context, system architectures, technologies, and the case study used the collection and processing of their personal data for immigration to run the demonstration.

Multimodal Emotion Recognition in Conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information, rendering it hard to adequately extract complementary and associative information from multimodal data. To cope with this problem, in CFN-ESA, textual modalities are treated as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFN-ESA mainly consists of the unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together the data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture related information, thereby guide the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform the state-of-the-art models.

Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is important in many scientific applications to identify the underlying mechanisms of a treatment effect. However, in many scientific applications the mediator is unobserved, but there may exist related measurements. For example, we may want to identify how changes in brain activity or structure mediate an antidepressant's effect on behavior, but we may only have access to electrophysiological or imaging brain measurements. To date, most CMA methods assume that the mediator is one-dimensional and observable, which oversimplifies such real-world scenarios. To overcome this limitation, we introduce a CMA framework that can handle complex and indirectly observed mediators based on the identifiable variational autoencoder (iVAE) architecture. We prove that the true joint distribution over observed and latent variables is identifiable with the proposed method. Additionally, our framework captures a disentangled representation of the indirectly observed mediator and yields accurate estimation of the direct and mediated effects in synthetic and semi-synthetic experiments, providing evidence of its potential utility in real-world applications.

DeepMind is known for leading the way in deep reinforcement learning research. Creating novel agents to conquer the most advanced environments requires the use of some sophisticated infrastructure. In ACME, you'll find everything from deep Q learning all the way up to the R2D2 algorithm. Better yet, it includes all the building blocks to start creating your own custom agents. In this tutorial, I'll show you how to setup ACME and get started making our own deep Q learning and deep deterministic policy gradient agent.

Deep reinforcement learning has led to many recent-and groundbreaking-advancements. However, these advances have often come at the cost of both the scale and complexity of the underlying RL algorithms. Increases in complexity have in turn made it more difficult for researchers to reproduce published RL algorithms or rapidly prototype ideas. To address this, we introduce Acme, a tool to simplify the development of novel RL algorithms that is specifically designed to enable simple agent implementations that can be run at various scales of execution. Our aim is also to make the results of various RL algorithms developed in academia and industrial labs easier to reproduce and extend. To this end we are releasing baseline implementations of various algorithms, created using our framework. In this work we introduce the major design decisions behind Acme and show how these are used to construct these baselines. We also experiment with these agents at different scales of both complexity and computation-including distributed versions. Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.

The traditional insurance model has had a pretty good run. It has been slowly evolving over the past few hundred years to include new coverages, multiple distribution channels (broker, agent, online), and create more complex actuarial models. The financial industry has been known to be relatively slow adopters of new technology, mostly because companies simply cannot take undue risk – and those working in insurance are experts in minimizing risk. This article is going to outline the current state of insurance, and the current progress technology has made, and will argue that the industry may be on the verge of significant disruption – the likes of which has the potential to render most policies, companies, employees and value added services obsolete. Two things to keep in mind as we move through this piece.

Like many in my generation, I grew up watching the Jetsons, and the idea of a maid robot (like Rosie) was appealing for obvious reasons. I now have a robot that can vacuum my apartment and a machine that washes my dishes. While I don't have Rosie doing these manual tasks for me, technology is indeed automating mundane tasks in my home... The vision that science fiction and Hollywood sells to us as it relates to Artificial Intelligence (AI) is the one of a fully-functional humanoid robot, or a computer with human intelligence. They might want to kill you (the Terminator, or H.A.L. from 2001: A Space Odyessy) or they might be here to help you (Data from Star Trek: The Next Generation), but they are always highly cognitive machines, sometimes with human like personalities with emotions included (sorry Data!).

Of course, the book's authors, psychologist Keith Holyoak and philosopher Paul Thagard, have good reason for this discussion: to focus on the "analogy war" that went on for years in the upper echelons of the U.S. government. Politicians think by analogy all the time, and the fates of nations hang on their idiosyncratic analogical instincts, wise or not. Military leaders, too, are guided by precedents, and Holyoak and Thagard ironically note that generals often prepare for the war that they last fought. However, they also point out that one can select one's precedents in a deeper manner than that. In fact, they devote three pages to George Ball, undersecretary of state in the Johnson administration, "who history must now credit as the greatest American political analogist of his time" (p. To be sure, Ball saw the appeal of the Korea, Munich, and dominochain analogies, but in each, he also saw serious weaknesses; more important, he felt he saw deeper similarities to the situation the ...

In the right architecture, machine-learning functionality takes data analytics to the next level of value. Editor's note: This guest post (translated from Italian and originally published in late 2016) by Lorenzo Ridi, of Google Cloud Platform partner Noovle of Italy, describes a POC for building an end-to-end analytic pipeline on GCP that includes machine-learning functionality. "Black Friday" is traditionally the biggest shopping day of the year in the United States. Black Friday can be a great opportunity to promote products, raise brand awareness and kick-off the holiday shopping season with a bang. During that period, whatever the type of retail involved, it's also becoming increasingly important to monitor and respond to consumer sentiment and feedback across social media channels.