Humans can imagine various atmospheres and settings when listening to music, envisioning movie scenes that complement each piece. For example, slow, melancholic music might evoke scenes of heartbreak, while upbeat melodies suggest celebration. This paper explores whether a Music Language Model, e.g. MU-LLaMA, can perform a similar task, called Music Scene Imagination (MSI), which requires cross-modal information from video and music to train. To improve upon existing music captioning models which focusing solely on musical elements, we introduce MusiScene, a music captioning model designed to imagine scenes that complement each music. In this paper, (1) we construct a large-scale video-audio caption dataset with 3,371 pairs, (2) we finetune Music Understanding LLaMA for the MSI task to create MusiScene, and (3) we conduct comprehensive evaluations and prove that our MusiScene is more capable of generating contextually relevant captions compared to MU-LLaMA. We leverage the generated MSI captions to enhance Video Background Music Generation (VBMG) from text.

Dynamically planning in multi-agent systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing both concealed strategic policies and decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes makes it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we first formulate the sequential decision-making process as a conditional trajectory generation process. We further introduce PLAYBEST (PLAYer BEhavior SynThesis), a method for enhancing player decision-making. We extend the state-of-the-art generative model, diffusion probabilistic model, to learn challenging multi-agent environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained using the play-by-play data with corresponding rewards acting as the plan guidance. To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling. We validate the effectiveness of PLAYBEST via comprehensive simulation studies from real-world data, contrasting the generated trajectories and play strategies with those employed by professional basketball teams. Our results reveal that the model excels at generating high-quality basketball trajectories that yield efficient plays, surpassing conventional planning techniques in terms of adaptability, flexibility, and overall performance. Moreover, the synthesized play strategies exhibit a remarkable alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.

Artificial intelligence has turned into a hot trend in the IT business in the past several years. While the practical ramifications of artificial intelligence frequently differ from the way it is typically shown in the film, these are the top artificial intelligence films of 2021. Lana Wachowski is the producer, co-writer, and director of The Matrix Resurrections' AI-based science fiction action film. The Matrix Reloaded is the follow-up to 2003's The Matrix Revolutions and the fourth film in the franchise overall. A catastrophic war between humans and artificial intelligence-powered machines is believed to be depicted in the film's plot, which has not yet been revealed.

The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new algorithms can be tested safely and quickly, such as Board games, RTS, FPS, and MOBA games. However, many existing environments lack complexity and flexibility and assume the actions are synchronously executed in multi-agent settings, which become less valuable. We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game. It is a complex and challenging environment that supports multiple characters, multiple positions, and both the single-agent and multi-agent player control modes. In addition, to better simulate real-world basketball games, the execution time of actions differs among players, which makes Fever Basketball a novel asynchronized environment. We evaluate commonly used multi-agent algorithms of both independent learners and joint-action learners in three game scenarios with varying difficulties, and heuristically propose two baseline methods to diminish the extra non-stationarity brought by asynchronism in Fever Basketball Benchmarks. Besides, we propose an integrated curricula training (ICT) framework to better handle Fever Basketball problems, which includes several game-rule based cascading curricula learners and a coordination curricula switcher focusing on enhancing coordination within the team. The results show that the game remains challenging and can be used as a benchmark environment for studies like long-time horizon, sparse rewards, credit assignment, and non-stationarity, etc. in multi-agent settings.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Humans build knowledge in images. Every time we are presented with an idea or an experience, our brain immediately formulates visual representations of it.

Orson Welles, who knew a thing or two about making movies, reportedly remarked after touring the RKO lot that it was "the biggest electric train set any boy ever had." And yet it is rare to see a feature film that communicates any of that delight, any of the sheer fun of playing around with the possibilities the medium offers. The Vast of Night, the debut feature from director Andrew Patterson and screenwriters James Montague and Craig W. Sanger, arriving on Amazon Prime on May 29, is one of the exceptions: Every scene has been staged and shot with intelligence, intent, inventiveness, and a sense of play. To watch it is to get excited about the billions of different ways you can combine sound and moving images to tell a story. That is not to say that you'll necessarily be astounded by the story The Vast of Night is telling.

In this study we focus on the prediction of basketball games in the Euroleague competition using machine learning modelling. The prediction is a binary classification problem, predicting whether a match finishes 1 (home win) or 2 (away win). Data is collected from the Euroleague's official website for the seasons 2016-2017, 2017-2018 and 2018-2019, i.e. in the new format era. Features are extracted from matches' data and off-the-shelf supervised machine learning techniques are applied. We calibrate and validate our models. We find that simple machine learning models give accuracy not greater than 67% on the test set, worse than some sophisticated benchmark models. Additionally, the importance of this study lies in the "wisdom of the basketball crowd" and we demonstrate how the predicting power of a collective group of basketball enthusiasts can outperform machine learning models discussed in this study. We argue why the accuracy level of this group of "experts" should be set as the benchmark for future studies in the prediction of (European) basketball games using machine learning.

As someone who needs to run a business, big or small, you are inundated with articles and talks at conferences about how great AI is. You hear a lot about what it can do, about the outcomes of some sexy new research, and about vague assertions of how it will transform your business. In truth, it can and will transform your business, but only if you can overcome the barriers to entry. There is a lot of focus on the artificial intelligence itself; the machine learning model and its algorithms, its accuracy, and all the amazing new breakthroughs. This is all well and good, and definitely worth paying attention to.

So much has changed in high school sports since the Southern Section voted to eliminate Rule 313 in 2008, otherwise known as the Association Rule. The rule restricted coaches from working with their athletes out of season. You couldn't coach your school's players in off-season games let alone hold workouts after school. The one-hour gym class was it. This is the 10th season of unregulated freedom.

NBA 2K18 is coming to PlayStation 4, Xbox One and Nintendo Switch in September. We don't have an official release date, but you can bet the developers for Take-Two's Visual Concepts are already working hard to produce the next version of the series. Per members of the development team, it's best to submit a wishlist for a sports game 7-9 months before it's scheduled to be released. I never expect everything on my list to be added to a game, but last year I saw 7 of my suggestions in NBA 2K17. I'm sure I wasn't the only one making requests, but it's great to know the gaming community's voice is being heard. I'm big on customization in sports video games, so I'll almost always push for more in this area.