The A-Team Group presented the award on Sept. 22nd at the 12th Annual Data Management Summit in New York. The A-Team Group recognizes leading data management solutions, services, and consultancy providers to the capital markets. The A-Team's editorial team and Advisory Board determine the winners by considering the depth of involvement in the capital markets, the relevance of a solution or service to a selected award category, and the potential interest of a solution or service to the Data Management Insight community. "At eClerx, we are excited that Robowork has won the Data Management Insight Awards 2022," said eClerx Global Head of Technology Sanjay Kukreja. "Roboworx is our leading RPA and Intelligent Automation platform and has been adopted by a number of our clients to deliver intelligent automation in their processes. It is an award-winning platform that has won a number of accolades across the globe for its use cases and adoption."

On the evening of Sunday, Sept. 23, 1962, millions of American families finished their dinners, turned on their televisions and were introduced to The Jetsons, a cartoon sitcom produced by the legendary team of Hanna-Barbera. Set in 2062, The Jetsons captured the technological optimism of the time and projected it into a space-age, gadget-fueled vision of the future, inviting its viewers to imagine the dazzling possibilities that the current wave of technological achievement could one day realize. In the end, The Jetsons was a rather tame, pedestrian sitcom about a family that reinforced traditional gender and family roles, knew little of the social issues of the time (it was, for example, unbearably white), and effectively glorified the consumerist, suburban lifestyle. But as a template for a technology-driven American future, it was no less than iconic. The Jetsons debuted five years after the Soviets had launched Sputnik, four years after the opening of the first commercial nuclear power plant in the U.S., and 16 months after President John F. Kennedy set a goal of putting a man on the moon by the decade's end. Fifteen years earlier, scientists at AT&T's Bell Labs invented the transistor, and soon after, miniature (by contemporary standards) transistor radios were found in many households.

Disney has a seemingly endless selection of Marvel movies and plenty of Star Wars and Pixar fare, too. Problem is, there's so much stuff, it's hard to know where to begin. WIRED is here to help. Below are our picks for the best movies on Disney right now. For more viewing ideas, try our guides to the best movies on Netflix and the best movies on Amazon Prime. This content can also be viewed on the site it originates from. Sam Raimi's sequel to 2016's Doctor Strange isn't the beloved director's first superhero movie, but it is his first foray into the Marvel Cinematic Universe style of making movies, which ultimately proves to be both a blessing and a curse. On the plus side, the movie is probably the closest thing the Marvel franchise has gotten to a straight-up horror film, and it's full of Raimi's signature practical effects (plus the ever-important Bruce Campbell cameo). Yet, because the MCU is such a box office powerhouse, the movie never goes full Raimi--which is understandable, but somewhat disappointing for fans of The Evil Dead maestro.

Cognitive computing has been on a lot of minds lately. Looking into the capabilities of Artificial Intelligence to imitate human perception to some extent, the technology innovators have discovered that cognitive computing is a better fit for that. We suddenly realized that a lot more can be done in that regard -- instead of imitating only the perception, we can have technology make decisions like humans. Sharing the idea among the team members of AIHunters, we have tasked ourselves with an ambition of cognitive business automation in the media and entertainment industry. Let us take you on a tour of how we did that -- deliver the solution that puts innovation towards optimizing the video processing and post-production, while pushing beyond the limitations of regular AI analysis.

Confirming rumors, Google has unveiled the Chromecast with Google TV (HD) device that offers features from the $50 4K model at a significantly cheaper $30 price. Unlike the original $35 Chromecast, it comes with a remote control that eliminates the need for a smartphone, though you can control it with a mobile device as well. The other key feature is right there in the name -- lower 1080p resolution, albeit with HDR support. The new device looks nigh-on identical to the 4K model, with an oval shape, short HDMI cable and a USB-C input. As before, it comes with all the popular streaming services including Netflix, Disney, Apple TV, HBO Max, Prime Video, YouTube and others.

In some respects, Andor is a new frontier: a series spinning off from not only the Skywalker Saga but specifically one of the two Star Wars Story films (in this case, Rogue One). The new series--the first three episodes of which debuted on Disney this week--is also largely disconnected from anything to do with the Force, the Jedi, or any of that flashy lightsaber stuff. Many comics, novels, and even video games have explored the same time period in the saga, and the same ideas. If three episodes only whets your appetite for more stories from the earliest days of the conflict between the Galactic Empire and the nascent Rebellion, these comics will fill that void. Andor might be the origin story for a character that audiences already saw at the end of Rogue One, but it's not the first time Star Wars fans have had a chance to see Cassian Andor in his prime. For that particular pleasure, look to this one-off special issue released by Marvel to tie in with the movie's 2017 theatrical release.

The participants in last week's final of TV show America's Got Talent included a Lebanese dance troupe, a pole dancer, the singer from Hootie & the Blowfish and… two tech entrepreneurs who "build Artificial Intelligence tools and software to create hyper-realistic synthetic media at scale", according to their social media biographies. Tom Graham and Chris Umé are the emerging godfathers of so-called deepfake technology, a mind-bending illusion by which a person in a video is digitally altered so they appear to be someone else. Thus the America's Got Talent final saw four largely unknown singers line up on stage and, thanks to futuristic AI trickery embedded in the cameras in front of them, become deepfake versions of Elvis Presley and judges Simon Cowell, Heidi Klum and Sofía Vergara on the big screen behind them. This all happened in real time, as if the extraordinarily lifelike quartet were singing Devil in Disguise live on stage. Cowell, Klum and Vergara (the real versions) looked on with mouths agape from their judges' seats in the stalls.

Voice Activity Detection (VAD) aims at detecting speech segments on an audio signal, which is a necessary first step for many today's speech based applications. Current state-of-the-art methods focus on training a neural network exploiting features directly contained in the acoustics, such as Mel Filter Banks (MFBs). Such methods therefore require an extra normalisation step to adapt to a new domain where the acoustics is impacted, which can be simply due to a change of speaker, microphone, or environment. In addition, this normalisation step is usually a rather rudimentary method that has certain limitations, such as being highly susceptible to the amount of data available for the new domain. Here, we exploited the crowd-sourced Common Voice (CV) corpus to show that representations based on Self-Supervised Learning (SSL) can adapt well to different domains, because they are computed with contextualised representations of speech across multiple domains. SSL representations also achieve better results than systems based on hand-crafted representations (MFBs), and off-the-shelf VADs, with significant improvement in cross-domain settings.

Modern NLP models are becoming better conversational agents than their predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term Memory (LSTM) features allow the agent to better store and use information about semantic content, a trend that has become even more pronounced with the Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have become known to be able to construct and follow a narrative, which enables the system to adopt personas on the go, adapt them and play along in conversational stories. However, practical experimentation with GPT-3 shows that there is a recurring problem with these modern NLP systems, namely that they can "get stuck" in the narrative so that further conversations, prompt executions or commands become futile. This is here referred to as the "Locked-In Problem" and is exemplified with an experimental case report, followed by practical and social concerns that are accompanied with this problem.

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.