There is always a chance that the information you hear or read may not be accurate, whether it comes from a physical newspaper or magazine, an internet source, or the radio. False information has existed for as long as human culture, but the sheer volume of information we receive from the linked, online world makes us particularly susceptible to inadvertently ingesting material that has been twisted or falsified. Garry M. Paxinos, CTO of netTALK CONNECT and NOOZ.AI, shares how AI can help tackle the issue of fake news and the complexities of bias. Consumers are accustomed to having their opinions influenced by what they read, see, and hear online, such as through influencer marketing or celebrity endorsements. Opinions have a lot of power, whether or not facts support them, and a lot of false news depends on stirring up strong emotions.

Recent years have seen an increased interest in the computational speech processing of Maltese, but resources remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for low-resource languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.

This report reviews all results derived from performance benchmarking conducted during Phase I of the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell, using the test methods specified in the DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. Using those 20 test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2 GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for each applicable test method and the sUAS that match this criteria are named for each test method, including a high-level executive summary of their performance.

This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. For sUAS deployment in subterranean and constrained indoor environments, this puts forth two assumptions about applicable sUAS to be evaluated using these test methods: (1) able to operate without access to GPS signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in) wide (i.e., can physically fit through a typical doorway, although successful navigation through is not guaranteed). All test methods are specified using a common format: Purpose, Summary of Test Method, Apparatus and Artifacts, Equipment, Metrics, Procedure, and Example Data. All test methods are designed to be run in real-world environments (e.g., MOUT sites) or using fabricated apparatuses (e.g., test bays built from wood, or contained inside of one or more shipping containers).

Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and efficient learning on the source-target mapping. In this paper, we present a learning paradigm called regeneration learning for data generation, which first generates Y' (an abstraction/representation of Y) from X and then generates Y from Y'. During training, Y' is obtained from Y through either handcrafted rules or self-supervised learning and is used to learn X-->Y' and Y'-->Y. Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e.g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y. We show that regeneration learning can be a widely-used paradigm for data generation (e.g., text generation, speech recognition, speech synthesis, music composition, image generation, and video generation) and can provide valuable insights into developing data generation methods.

Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and have more narrativity compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and have more narrativity than stories generated with the current state-of-the-art model.

Netflix uses machine learning techniques, including matrix factorization, deep learning, and reinforcement learning, to power its recommendation system and deliver personalized recommendations to its users. Netflix is a leading streaming service that has revolutionized the way we consume TV shows and movies. One key factor in its success is its sophisticated recommendation system, which suggests content to users based on their past viewing history and preferences. In this article, we will explore how Netflix uses machine learning to power its recommendation system and deliver a personalized viewing experience to its users. Netflix's recommendation system is based on collaborative filtering, which involves gathering data on user behavior and preferences, and using this information to make recommendations to other users with similar tastes.

When it comes to video-game adaptations, TV and film producers have historically had an unfortunate habit of using the game as a kind of Mad Libs prompt for something completely unrelated. Characters you've spent 30 hours getting to know in a game might remain in name and appearance only, given personality transplants to fit into new, incongruous plots. There has been an endemic lack of respect for video games from decades' worth of film-makers who, in the words of games satire site Hard Drive News, have been excited to take a beloved franchise and adapt it into something not for dumb little babies. HBO's The Last of Us finally marks the end of this era. There's been a shift in the tenor of game adaptations in the past few years; you could tell that Detective Pikachu was written by huge Pokémon fans, Cyberpunk 2077's Netflix series was actually better than the game, and the plot of Paramount's TV version of the military space-opera Halo is just as ponderous and self-important as the games.

Ammaar Reshi used AI to write and illustrate a children's book in a weekend. He became the center of a fiery debate: Are these tools a grim reaper for the arts?

Artificial intelligence has been deployed to handle facial recognition, recommend movies, and auto-complete your typing. The news that CNET had been using it to generate entire stories, however, sent a ripple of anxiety through the news media for its seeming threat to journalists. The robot-brained yet conversational ChatGPT can produce copy without lunch or bathroom breaks and never goes on strike.