This paper explores generative AI-based agents for autonomous, personalized content recommendations, utilizing state-of-the-art software for high-performance custom workflows, high-dimensional vector storage and searching, language-based tasks, and autonomous 24/7 running capabilities. Using unstructured, unseen, real-time data from YouTube, we utilize large language models to quantitatively handle subjective tasks and evaluate the outcomes. In essence, we created a recommendation system that uses artificial intelligence to autonomously find content and reduce the time spent on manual search. Content recommendation is a prominent problem in the industry, and we find that the performance of our system is satisfactory, and the scope of such systems is substantial. If used in correlation with default recommendation systems, the system can provide an improved interactive recommendation experience.

Planar linkage mechanisms, or linkages, are systems of rigid links and joints that translate an input motion into desirable output motions. In doing so, linkages enable us to perform complex tasks in fields such as manufacturing automation, robotics, computer graphics, and more with minimal input complexity. Recently, deep generative modeling solutions have been applied to generate linkage designs since these designs live in an intractable distribution; however, no scalable, conditional generative model has been found yet that can generate a set of optimal planar mechanical linkage designs, ranging in complexity, that best fit any type of generated path of motion by the user. In this work, we develop a generative flow network conditioned on linkage mechanism specifications to sample a diverse set of planar mechanisms. While developing this generative model, we also gain a much better understanding of the vast design space of linkages with linear and geometric algebra, graph neural networks, and implicit differentiation.

Obscene and indecent content in popular music is becoming more prevalent, the historical unreliability of explicit markers has seemingly worsened with the rise of streaming as the primary method of music consumption, and the tools to remove the explicit content from song lyrics or ’clean’ a song pose a financial burden or require a degree of technical knowledge not common in the general population. This paper compares open source software for Music Source Separation, focusing on the ability to separate vocals from a musical track, and introduces an application exposing the champion through an intuitive graphic user interface. The resulting application allows users to extract vocals from an audio file, selectively remove vocals, and recombine the edited vocals with their source, effectively allowing users to create their own clean versions of their favorite music. The aim is to alleviate the financial burden associated with professional Music Source Separation software as well as increase accessibility of MSS for the layman.

Superconducting high-entropy alloys (HEAs) have recently garnered significant attention across numerous fields due to their unique blend of properties such as increased mechanical strength, structural stability, and tunable electronic properties. These attractive features thus position HEAs as a strong candidate for multiple real-world applications, especially as next-generation superconducting qubit materials considering their robust performance under extreme conditions such as low temperatures and high magnetic fields. However, the creation of HEAs consists of a vast compositional space, enabling researchers to choose from a great range of elements in different proportions heated at multiple cycles. In order to navigate this complex field, this study utilizes Bayesian optimization as a data-driven strategy to expedite the process of discovering and optimizing HEAs with high superconducting performance. Due to the high cost and time often associated with carrying out experiments in laboratories, this approach of iteratively updating a probabilistic model with an initial set of training data proves to be beneficial in focusing efforts on only the most promising configurations. It is also crucial to note that this research study is the first in literature to explore and computationally optimize a novel composition of seven specific elements of Gold, Tin, Antimony, Palladium, Silver, Tellurium, and Indium. This combination of Bayesian optimization and superconducting HEAs demonstrates a dynamic convergence between machine learning and materials innovation, broadening research horizons for quantum technology and engineering.

Dominion is a modern deck-building game characterized by a combinatorially large state space, imperfect information, indirect player interaction, and stochasticity. Recent successes in artificial intelligence (AI) developed for classical games like Chess and Go, exemplified by AlphaGo and its derivatives, suggest that current machine learning methods may be sufficient to tackle Dominion. We use Upper Confidence Trees (UCT) trained via self-play and a rollout policy modeled by logistic regressors to beat Big Money (BM) in a no-action, two-player, sandbox version of Dominion. We combine the same rollout policy with different parameterizations of UCT to train AI agents in a preset kingdom of full Dominion that match (UCT-F), surpass (UCT-P), and outclass (UCT-DW) the Double Witch (DW) strategy in terms of relative win rate. We highlight the buy strategies of our methods and suggest that UCT for Dominion is bottlenecked by the game's high branching factor of buy decisions, an effect exacerbated by the current lack of suitable value functions for the game.