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.

Score-Based Generative Models (SGMs) generate data by evolving samples under a Stochastic Differential Equation (SDE) that may include an auxiliary momentum variable. In this thesis, we investigate the probability density functions describing the evolution of such systems with momentum when the governing SDE slowly changes. For a simple moving Gaussian potential, we show the existence of an intermediate Pseudo-Adiabatic Regime (PAR) in which the momentum variables equilibrate while the position variables continue to evolve. In this regime, we show that the forward and reverse-time SDEs are the same, potentially simplifying the generative process. Using perturbative analysis and numerical experiments, we characterize the conditions under which the PAR emerges, demonstrating that a large damping-to-mass ratio suffices. Our results offer new perspectives on SGMs, and motivate further research into a more efficient generative process for SGMs.