Reinforcement learning (RL) algorithms have become indispensable tools in artificial intelligence, empowering agents to acquire optimal decision-making policies through interactions with their environment and feedback mechanisms. This study explores the performance of RL agents in both two-dimensional (2D) and three-dimensional (3D) environments, aiming to research the dynamics of learning across different spatial dimensions. A key aspect of this investigation is the absence of pre-made libraries for learning, with the algorithm developed exclusively through computational mathematics. The methodological framework centers on RL principles, employing a Q-learning agent class and distinct environment classes tailored to each spatial dimension. The research aims to address the question: How do reinforcement learning agents adapt and perform in environments of varying spatial dimensions, particularly in 2D and 3D settings? Through empirical analysis, the study evaluates agents' learning trajectories and adaptation processes, revealing insights into the efficacy of RL algorithms in navigating complex, multi-dimensional spaces. Reflections on the findings prompt considerations for future research, particularly in understanding the dynamics of learning in higher-dimensional environments.

The English-speaking world was rocked by the advent of ChatGPT in November 2022. Here, suddenly, was a chatbot that could do a credible imitation of a human being, producing text that seemed like it was written by a real person. People worried that ChatGPT, Google's Bard, and the like would cause widespread cheating as students turned over their writing assignments to a machine, or that they would lead to the mass production of misinformation and propaganda, outstripping the abilities of Russian troll farms. Those concerns arise in languages other than English as well. So far, however, chatbots based on Large Language Models (LLMs) appear to perform best in English, while sometimes struggling to mimic humans in other tongues.

Ewan Morgan, National Luxury Ambassador and Head of Whisky Outreach at Diageo North America, told Fox News Digital how SmokeDNAi technology is being used to understand the aging process of whisky. For two years, Diageo analyzed various Scotch whiskies using AI and algorithms. Diageo, an alcohol beverage company, invested 230 million into a portfolio of whisky tourism projects. Of this lump sum, more than 44 million was dedicated to the exploration of whisky maturation using technology called SmokeDNAi. Using SmokeDNAi, teams tested and analyzed the flavor profiles and mouthfeel of non-identical twin whiskies distilled in different casks – one remnant and one original.

Generative AI has been predicted to add trillions to the world economy in a productivity boost never before seen in history (if it doesn't wipe out humanity first). A growing number of sceptics, including some leading AI scientists, are wondering whether the tech might not deliver on its promises to boost the world economy. Goldman Sachs famously predicted that generative AI would bring about'sweeping changes' to the world economy, driving a 7 trillion increase in global GDP and lifting productivity growth by 1.5 percent this decade. Professor Gary Marcus of New York University wrote on Substack that'we are starting to see signs' that generative AI might be a'dud'. Among the warning signs was a report in the Wall Street Journal suggesting that customers found the 30 a month price of Microsoft's new AI-boosted Copilot software too expensive.

One late-January day, the director Lila Neugebauer was at a gun range--or an antiseptic, fluorescent-white version of one--tucked inside the Specialists, Ltd., a theatrical-props behemoth in Ridgewood, Queens. Neugebauer, accompanied by two members of her team, had come to discuss a gun for her upcoming production of Anton Chekhov's "Uncle Vanya," at Lincoln Center Theatre. The production is a starry one, with Steve Carell in the title role, alongside Alfred Molina, Alison Pill, Anika Noni Rose, and William Jackson Harper. With a new translation by the playwright Heidi Schreck--who was nominated for a Tony for her women's-rights jeremiad "What the Constitution Means to Me"--this is the first Broadway staging of Chekhov's masterpiece in more than twenty years. Neugebauer is small and quick, with flyaway black hair, straight black brows crossing a narrow face, and intent gray-green-golden eyes, like a fox's.

Modern contextual lemmatizers often rely on automatically induced Shortest Edit Scripts (SES), namely, the number of edit operations to transform a word form into its lemma. In fact, different methods of computing SES have been proposed as an integral component in the architecture of several state-of-the-art contextual lemmatizers currently available. However, previous work has not investigated the direct impact of SES in the final lemmatization performance. In this paper we address this issue by focusing on lemmatization as a token classification task where the only input that the model receives is the word-label pairs in context, where the labels correspond to previously induced SES. Thus, by modifying in our lemmatization system only the SES labels that the model needs to learn, we may then objectively conclude which SES representation produces the best lemmatization results. We experiment with seven languages of different morphological complexity, namely, English, Spanish, Basque, Russian, Czech, Turkish and Polish, using multilingual and language-specific pre-trained masked language encoder-only models as a backbone to build our lemmatizers. Comprehensive experimental results, both in- and out-of-domain, indicate that computing the casing and edit operations separately is beneficial overall, but much more clearly for languages with high-inflected morphology. Notably, multilingual pre-trained language models consistently outperform their language-specific counterparts in every evaluation setting.

Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images. However, they often struggle to faithfully capture the intended semantics of complex input prompts that include multiple subjects. Recently, numerous layout-to-image extensions have been introduced to improve user control, aiming to localize subjects represented by specific tokens. Yet, these methods often produce semantically inaccurate images, especially when dealing with multiple semantically or visually similar subjects. In this work, we study and analyze the causes of these limitations. Our exploration reveals that the primary issue stems from inadvertent semantic leakage between subjects in the denoising process. This leakage is attributed to the diffusion model's attention layers, which tend to blend the visual features of different subjects. To address these issues, we introduce Bounded Attention, a training-free method for bounding the information flow in the sampling process. Bounded Attention prevents detrimental leakage among subjects and enables guiding the generation to promote each subject's individuality, even with complex multi-subject conditioning. Through extensive experimentation, we demonstrate that our method empowers the generation of multiple subjects that better align with given prompts and layouts.

Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discriminatory practices like partiality for certain genders, ethnicities or disabled people. With the ubiquitous dissemination of AI systems, researchers and practitioners are becoming more aware of unfair models and are bound to mitigate bias in them. Significant research has been conducted in addressing such issues to ensure models don't intentionally or unintentionally perpetuate bias. This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. We explore the different definitions of fairness existing in the current literature. We create a comprehensive taxonomy by categorizing different types of bias and investigate cases of biased AI in different application domains. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. Moreover, we also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models. We hope this survey helps researchers and practitioners understand the intricate details of fairness and bias in AI systems. By sharing this thorough survey, we aim to promote additional discourse in the domain of equitable and responsible AI.

To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images.

Detecting problematic research articles timely is a vital task. This study explores whether Twitter mentions of retracted articles can signal potential problems with the articles prior to retraction, thereby playing a role in predicting future retraction of problematic articles. A dataset comprising 3,505 retracted articles and their associated Twitter mentions is analyzed, alongside 3,505 non-retracted articles with similar characteristics obtained using the Coarsened Exact Matching method. The effectiveness of Twitter mentions in predicting article retraction is evaluated by four prediction methods, including manual labelling, keyword identification, machine learning models, and ChatGPT. Manual labelling results indicate that there are indeed retracted articles with their Twitter mentions containing recognizable evidence signaling problems before retraction, although they represent only a limited share of all retracted articles with Twitter mention data (approximately 16%). Using the manual labelling results as the baseline, ChatGPT demonstrates superior performance compared to other methods, implying its potential in assisting human judgment for predicting article retraction. This study uncovers both the potential and limitation of social media events as an early warning system for article retraction, shedding light on a potential application of generative artificial intelligence in promoting research integrity.