This paper preliminarily investigates the duality between flow matching in generative models and particle swarm optimization (PSO) in evolutionary computation. Through theoretical analysis, we reveal the intrinsic connections between these two approaches in terms of their mathematical formulations and optimization mechanisms: the vector field learning in flow matching shares similar mathematical expressions with the velocity update rules in PSO; both methods follow the fundamental framework of progressive evolution from initial to target distributions; and both can be formulated as dynamical systems governed by ordinary differential equations. Our study demonstrates that flow matching can be viewed as a continuous generalization of PSO, while PSO provides a discrete implementation of swarm intelligence principles. This duality understanding establishes a theoretical foundation for developing novel hybrid algorithms and creates a unified framework for analyzing both methods. Although this paper only presents preliminary discussions, the revealed correspondences suggest several promising research directions, including improving swarm intelligence algorithms based on flow matching principles and enhancing generative models using swarm intelligence concepts.

Cohort Intelligence or CI is one of its kind of novel optimization algorithm. Since its inception, in a very short span it is applied successfully in various domains and its results are observed to be effectual in contrast to algorithm of its kind. Till date, there is no such type of bibliometric analysis carried out on CI and its related applications. So, this research paper in a way will be an ice breaker for those who want to take up CI to a new level. In this research papers, CI publications available in Scopus are analyzed through graphs, networked diagrams about authors, source titles, keywords over the years, journals over the time. In a way this bibliometric paper showcase CI, its applications and detail outs systematic review in terms its bibliometric details.

In this day and age of technological advancements, people are looking for solutions to automate regular and repetitive tasks as much as possible. As such, the development of artificial intelligence algorithms has come a long way to help in automation and reduce human labor. It would give us humans enough time to focus on the development of our own skills and pursue our dreams and aspirations. With the potential artificial intelligence has shown, many industries are bound to change their working strategies and rules. Industries will change how they operate and adopt newer and more efficient methods which depend on AI algorithms. The application of Artificial Intelligence to automate medical procedures and negate possible complications is not a new idea.

"Many computer games are expensive and require a lot of data and power. We need games that require little computing power to train algorithms in industrial environments," says Per-Arne Andersen, assistant professor at the University of Agder's (UiA's) Department of ICT. He recently earned his PhD with a thesis on how artificial intelligence in computer games can function well even if there is not much computing power. Andersen has developed artificial intelligence algorithms that can be used in systems where frequent decisions have to be made. Here, computer games are widely used to train artificial intelligence in game environments that are modelled on complicated industrial environments.

The advancement of technology is changing how we interact with the world. There isn't a single business globally that hasn't been impacted by artificial intelligence in some way or another (AI). From virtual reality (VR) gaming systems to artificial intelligence (AI) robots in industrial production, technology is advancing exponentially. However, since learning technologies and algorithms are already transforming the industry, eCommerce is especially susceptible to artificial intelligence (AI) disruption. It could alter the way we buy and sell items on the internet.

As exciting as all this might seem, this decision seems to be more of an aberration than the rule. Before it was finally granted a patent in South Africa, the DABUS application had been rejected by patent offices in the US, Europe and the UK. The European Patent Office (EPO), justifying its decision to reject the patent application, pointed out that the law designates a natural person as the inventor of a work in order to preserve her moral right over the invention as well as to secure for her the economic rights made available by the patent. In order to be entitled to these benefits, an inventor needs to have actually "performed the creative act of invention". While artificial intelligence algorithms today are capable of perform complex computational functions that are often way beyond the capability of humans, the EPO pointed out that in all these instances, the programs are doing little more than just following the broad instructions of the humans who designed them.

Imagine looking for the optimal configuration to build an organic solar cell made from different polymers. Does the active layer need to be very thick, or very thin? Does it need a large or a small amount of each polymer? Knowing how to predict the specific composition and cell design that would result in optimum performance is one of the greatest unresolved problems in materials science. This is, in part, due to the fact that the device performance depends on multiple factors.

"Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model": Researchers from New Hyde Park, N.Y.-based Northwell Health's research arm developed an artificial intelligence tool to predict which patients will remain stable overnight and don't need to be awoken for vital monitoring. The tool cut in half the number of patients who were awoken during the night for vital sign checks, misclassifying less than two of 10,000 cases. "Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies": Researchers developed an artificial intelligence tool to improve pathologists' grading of prostate needle biopsies, finding significant increases in grading agreement. "Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study": The research team developed and validated a real-time deep convolutional neural networks system for the detection of early gastric cancer. "Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography": Researchers developed a deep learning-based artificial intelligence algorithm to help detect myocardial infarction using electrocardiography to speed up the diagnosis process.

Self-driving cars have begun to become a reality in the fields of agriculture, transportation, and military, and the day when ordinary consumers use self-driving cars in their daily lives is quickly approaching. An autonomous vehicle performs necessary operations based on sensor information and AI algorithms. It needs to collect data, plan trajectories, and execute driving routes. These tasks, especially planning and executing trajectories, require non-traditional programming methods, which rely on machine learning techniques in AI. Traditional heuristic algorithms in computer science can be used for path planning and control, such as Bellman-Ford algorithm and Dijkstra algorithm.

What makes us humans so good at making sense of visual data? That's a question that has preoccupied artificial intelligence and computer vision scientists for decades. Efforts at reproducing the capabilities of human vision have so far yielded results that are commendable but still leave much to be desired. Our current artificial intelligence algorithms can detect objects in images with remarkable accuracy, but only after they've seen many (thousands or maybe millions) examples and only if the new images are not too different from what they've seen before. There is a range of efforts aimed at solving the shallowness and brittleness of deep learning, the main AI algorithm used in computer vision today.