Check out all the on-demand sessions from the Intelligent Security Summit here. AI has made and will continue to make significant headlines. Most of these are fairly sensational; AI is becoming sentient; AI-generated art wins a contest; AI can now compose music (and more). However, what rarely makes the headlines is just how transformational AI can be when it comes to business --specifically, how AI can help brands connect with their customers without becoming a flashy sci-fi headline. Because of the general "sci-fi" perception of AI, many business leaders haven't seriously considered how to apply it to their business beyond data analytics or cutting-edge research labs.

ByteDance, the Chinese parent company of popular video app TikTok, said Thursday that some employees improperly accessed the TikTok user data of two journalists and were no longer employed by the company, an email seen by Reuters shows. ByteDance employees accessed the data as part of an unsuccessful effort to investigate leaks of company information earlier this year, and were aiming to identify potential connections between two journalists, a former BuzzFeed reporter and a Financial Times reporter, and company employees, the email from ByteDance general counsel Erich Andersen said. The employees looked at IP addresses of journalists attempting to learn if they were in the same location as employees suspected of leaking confidential information. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

NBCUniversal owns and operates over 20 different businesses across 30 countries including a valuable portfolio of news and entertainment television networks, a premier motion picture company, significant television production operations, a leading television stations group, world-renowned theme parks and a premium ad-supported streaming service. Here you can be your authentic self. As a company uniquely positioned to educate, entertain and empower through our platforms, Comcast NBCUniversal stands for including everyone. We strive to foster a diverse and inclusive culture where our employees feel supported, embraced and heard. We believe that our workforce should represent the communities we live in, so that together, we can continue to create and deliver content that reflects the current and ever-changing face of the world.

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.

The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot-based models leveraging motion cues have recently shown great promise in learning to represent, segment, and track objects without direct supervision, but they still fail to scale to complex real-world multi-object videos. In an effort to bridge this gap, we take inspiration from human development and hypothesize that information about scene geometry in the form of depth signals can facilitate object-centric learning. We introduce SAVi++, an object-centric video model which is trained to predict depth signals from a slot-based video representation. By further leveraging best practices for model scaling, we are able to train SAVi++ to segment complex dynamic scenes recorded with moving cameras, containing both static and moving objects of diverse appearance on naturalistic backgrounds, without the need for segmentation supervision. Finally, we demonstrate that by using sparse depth signals obtained from LiDAR, SAVi++ is able to learn emergent object segmentation and tracking from videos in the real-world Waymo Open dataset.

Content ratings can enable audiences to determine the suitability of various media products. With the recent advent of fan fiction, the critical issue of fan fiction content ratings has emerged. Whether fan fiction content ratings are done voluntarily or required by regulation, there is the need to automate the content rating classification. The problem is to take fan fiction text and determine the appropriate content rating. Methods for other domains, such as online books, have been attempted though none have been applied to fan fiction. We propose natural language processing techniques, including traditional and deep learning methods, to automatically determine the content rating. We show that these methods produce poor accuracy results for multi-classification. We then demonstrate that treating the problem as a binary classification problem produces better accuracy. Finally, we believe and provide some evidence that the current approach of self-annotating has led to incorrect labels limiting classification results.

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.

ByteDance says it has fired four employees who accessed the data of several TikTok users located in the US, including journalists. According to The New York Times, an investigation conducted by an outside law firm found that the employees were trying to locate the sources of leaks to reporters. Two of the employees were in the US and two were in China, where ByteDance is based. The company reportedly determined that members of a team responsible for monitoring employee conduct accessed the IP addresses and other data linked to the TikTok accounts of a reporter from BuzzFeed News and Cristina Criddle of the Financial Times. The employees are also said to have accessed the data of several people with ties to the journalists.

Artificial intelligence(AI) has been a popular theme in comics for decades, often depicted as either a helpful ally or a formidable enemy. Here are ten comics with storylines involving artificial intelligence (AI). The singularity is a hypothetical future event in which artificial intelligence(AI) surpasses human intelligence and becomes capable of rapid, exponential growth. This concept is often associated with the idea of a "singularity trap," in which humans become reliant on AI to the point where it becomes difficult or impossible to control or predict its actions. There is ongoing debate about the likelihood and potential consequences of the singularity, with some experts arguing that it could bring about great technological progress and improvements in quality of life, while others warn that it could pose significant risks to humanity.

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