The media localization industry usually requires a verbatim script of the final film or TV production in order to create subtitles or dubbing scripts in a foreign language. In particular, the verbatim script (i.e. as-broadcast script) must be structured into a sequence of dialogue lines each including time codes, speaker name and transcript. Current speech recognition technology alleviates the transcription step. However, state-of-the-art speaker diarization models still fall short on TV shows for two main reasons: (i) their inability to track a large number of speakers, (ii) their low accuracy in detecting frequent speaker changes. To mitigate this problem, we present a novel approach to leverage production scripts used during the shooting process, to extract pseudo-labeled data for the speaker diarization task. We propose a novel semi-supervised approach and demonstrate improvements of 51.7% relative to two unsupervised baseline models on our metrics on a 66 show test set.

This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models. This data comprises the past five years and captures changes in n-gram frequency, similarity, sentiment and topic distribution. The interface built on top of this data enables temporal analysis for detecting and characterizing shifts in meaning, including complementary information to trending metrics, such as sentiment and topic association over time. We release an online demo for easy experimentation, and we share code and the underlying aggregated data for future work. In this paper, we also discuss three case studies unlocked thanks to our platform, showcasing its potential for temporal linguistic analysis.

Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks.

A new piece of technology has been engineered by Revolute Robotics that resembles a hamster ball, yet has the ability to fly. Our world is filled with many incredible inventions and feats of engineering. But, occasionally, something comes along that genuinely revolutionizes our perspective on what technology can do. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S – SIGN UP FREE HERE It's a flying, crawling, autonomous robot that resembles a hamster ball. It sounds unbelievable, but this little sphere of technological wonder is already turning heads in the industry.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' EXCLUSIVE: Republican groups are calling on Senate Majority Leader Chuck Schumer, D-N.Y., to recuse himself from efforts to regulate artificial intelligence because of his daughters' work with Big Tech firms Meta and Amazon. Schumer has been leading a bipartisan group of senators that are examining guardrails for AI. But Republican groups Bull Moose Project and New York Young Republican Club, among other organizations, argued in a letter to Schumer that his family ties to these companies should disqualify him from the push to regulate AI. "As the Senate considers regulatory approaches to artificial intelligence (AI), it is crucial that lawmakers' personal conflicts of interest do not impact policy decisions," the representatives of the groups wrote. The groups said during last year's push to regulate Big Tech, some said the fact that his daughter Alison Schumer worked at Meta as a privacy and politics product marketing manager and his daughter Jessica Schumer was a registered Amazon lobbyist created a conflict of interest.

Members of Congress shared whether Hollywood's striking actors and writers should be concerned about artificial intelligence ultimately taking their jobs. WASHINGTON, D.C. – Lawmakers were torn on whether actors and writers should be concerned about artificial intelligence taking their jobs, with one Republican lawmaker saying the "spoiled" Hollywood professionals should get back to work at their "overpaid" jobs. "Hollywood is a bunch of spoiled brat degenerates, and they ought to get back to work," Tennessee Rep. Tim Burchett, a Republican, said. "They are overpaid and under worked. The rest of this country gets by on a lot less."

Countries looking to fully utilize artificial intelligence (AI)'s potential and capabilities will need to look for upgrades to data storage and processing, turning to either blockchains or quantum computing for the way forward, experts told Fox News Digital. "You're going to have massive data storage issues and issues for computation when you get into pattern recognition," Christopher Alexander, chief analytics officer of Pioneer Development Group, told Fox News Digital. The race to develop and implement AI systems cannot occur without proper infrastructure, according to TS2 Space, a Polish internet service provider for the U.S. Army in areas like Iraq and Afghanistan. In a blog post on the company website, TS2 Space highlighted the challenges AI infrastructure faces, including "the sheer volume of data" and "the complexity of AI algorithms and models." "Developing and deploying AI applications require a deep understanding of the underlying algorithms and models, as well as the ability to fine-tune them for specific use cases," the company wrote.

Automatic synthesis of analog and Radio Frequency (RF) circuits is a trending approach that requires an efficient circuit modeling method. This is due to the expensive cost of running a large number of simulations at each synthesis cycle. Artificial intelligence methods are promising approaches for circuit modeling due to their speed and relative accuracy. However, existing approaches require a large amount of training data, which is still collected using simulation runs. In addition, such approaches collect a whole separate dataset for each circuit topology even if a single element is added or removed. These matters are only exacerbated by the need for post-layout modeling simulations, which take even longer. To alleviate these drawbacks, in this paper, we present FuNToM, a functional modeling method for RF circuits. FuNToM leverages the two-port analysis method for modeling multiple topologies using a single main dataset and multiple small datasets. It also leverages neural networks which have shown promising results in predicting the behavior of circuits. Our results show that for multiple RF circuits, in comparison to the state-of-the-art works, while maintaining the same accuracy, the required training data is reduced by 2.8x - 10.9x. In addition, FuNToM needs 176.8x - 188.6x less time for collecting the training set in post-layout modeling.

Conversational agents are consistently growing in popularity and many people interact with them every day. While many conversational agents act as personal assistants, they can have many different goals. Some are task-oriented, such as providing customer support for a bank or making a reservation. Others are designed to be empathetic and to form emotional connections with the user. The Alexa Prize Challenge aims to create a socialbot, which allows the user to engage in coherent conversations, on a range of popular topics that will interest the user. Here we describe Athena 2.0, UCSC's conversational agent for Amazon's Socialbot Grand Challenge 4. Athena 2.0 utilizes a novel knowledge-grounded discourse model that tracks the entity links that Athena introduces into the dialogue, and uses them to constrain named-entity recognition and linking, and coreference resolution. Athena 2.0 also relies on a user model to personalize topic selection and other aspects of the conversation to individual users.