AI acceleration has been dominated by GPUs, but the growing need for lower latency, energy efficiency, and fine-grained hardware control exposes the limits of fixed architectures. In this context, Field-Programmable Gate Arrays (FPGAs) emerge as a reconfigurable platform that allows mapping AI algorithms directly into device logic. Their ability to implement parallel pipelines for convolutions, attention mechanisms, and post-processing with deterministic timing and reduced power consumption makes them a strategic option for workloads that demand predictable performance and deep customization. Unlike CPUs and GPUs, whose architecture is immutable, an FPGA can be reconfigured in the field to adapt its physical structure to a specific model, integrate as a SoC with embedded processors, and run inference near the sensor without sending raw data to the cloud. This reduces latency and required bandwidth, improves privacy, and frees GPUs from specialized tasks in data centers. Partial reconfiguration and compilation flows from AI frameworks are shortening the path from prototype to deployment, enabling hardware--algorithm co-design.

--Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification achieved modest performance, while the fine-tuned ConvNeXt model outperformed the BioMedCLIP linear probe. Although linear probing demonstrated potential with pretrained embeddings, it was less effective than full fine-tuning. These findings suggest that despite the promise of multimodal learning, CNN-based models with end-to-end fine-tuning provide stronger performance for specialized medical imaging. The study underscores the need for more detailed textual representations and domain-specific adaptations in future radiology applications. Accurate breast density classification plays a critical role in assessing breast cancer risk.

Large Language Models based on transformer algorithms have revolutionized Artificial Intelligence by enabling verbal interaction with machines akin to human conversation. These AI agents have surpassed the Turing Test, achieving confusion rates up to 50%. However, challenges persist, especially with the advent of robots and the need to humanize machines for improved Human-AI collaboration. In this experiment, three GPT agents with varying levels of agreeableness (disagreeable, neutral, agreeable) based on the Big Five Inventory were tested in a Turing Test. All exceeded a 50% confusion rate, with the highly agreeable AI agent surpassing 60%. This agent was also recognized as exhibiting the most human-like traits. Various explanations in the literature address why these GPT agents were perceived as human, including psychological frameworks for understanding anthropomorphism. These findings highlight the importance of personality engineering as an emerging discipline in artificial intelligence, calling for collaboration with psychology to develop ergonomic psychological models that enhance system adaptability in collaborative activities.

Machine learning (ML) algorithms are predictively competitive algorithms with many human-impact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.

Given the changes that the world economy is undergoing with digital transformation, artificial intelligence and the metaverse will lead the advances of the future, considered David Garza, rector of the Tecnológico de Monterrey, who represented Mexico at the World Economic Forum (WEF) held in Davos, Switzerland. In an interview, the rector and executive president of the educational institution spoke about the participation they had in the tables held within the framework of the world economic meeting. "It was a great opportunity to have this vision of the geopolitical, economic, and technological issues that are to come," he said. He indicated that the WEF has a lot to do with the future and what are the challenges to face, as well as the opportunities. "Consistently in the different talks, panels, conversations, there were very relevant issues, one of which has to do with the issue that the economy is going to have a great transformation, there is going to be a new economy," he said.

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The career path of Colombian physicist Luisa Fernanda Chaparro Sierra took her from studying the Higgs Boson at CERN, to using similar machine learning techniques to gauge perceptions of crime in the Colombian capital of Bogota. Chaparro, currently a Research Professor at Tecnológico de Monterrey in Monterrey, México, says that after finishing her Phd, she had the opportunity to be part of the DataLab (Laboratorio de Datos) of the Universidad Nacional de Colombia where she used the techniques of handling large databases to help understand the problem of the perception of security in Bogota via machine learning methods. "At CERN, we handled large amounts of data and to differentiate between signal and background; we used supervised machine learning techniques, so I used similar methods and adapted others for the case of perception of security," she says, adding that DataLab was composed of mathematicians, physicists, and engineers with knowledge in programming and statistics. "We used Twitter as our data source and reviewed tweets that talked about security in the city for a year," Chaparro says, "The goal was to design a model that would allow us to quantify something as subjective as perception." The researchers were also hoping to find a relationship between it and real crimes by comparing the results with the databases provided by the National Police.

Doctors use several types of imaging to detect kidney stones including high resolution CT scans and kidney-ureter-bladder x-ray. Doctors analyze the images to assess the stone's size, shape, and position to choose the best treatment to remove the stones. Once the stone is removed, it is examined to determine what type of stone it is. Doctors also test the patient's blood and urine for calcium, phosphorus, and uric acid to determine what caused the stone to form. Doctors use this information to help patients reduce the risk of developing kidney stones in the future.

The artificial intelligence revolution has arrived. McKinsey estimates the global impact of AI will deliver the equivalent of an additional $13 trillion into world economies by 2030. Others believe those estimates are too conservative. The tech investor Tej Kohli sees AI adding $150 trillion – more than the net worth of the United States – in just five years. Latin Americans are as enthusiastic about exploring AI as any other region.

The Fourth International Symposium on Artificial Intelligence (ISAI) was held in Cancun, Mexico, 13-15 November 1991. What, another international AI conference, you say? The first symposium was held in 1988. This fourth consecutive annual conference drew the participation of visitors from several international AI communities, including the United States, Mexico, Canada, Germany, Japan, England, France, Italy, The Netherlands, Spain, China, Belgium, Australia, and Singapore--an impressive breadth of participants for a conference that has existed for only four years. ISAI was born in the summer of 1987 when Francisco Cantu-Ortiz, the director of the Centro de Inteligencia Artificial at Instituto Tecnologico y Estudios Superiores de Monterrey (ITESM), with support from senior ITESM administrators, decided that hosting an international AI conference would help build an awareness of the technological importance of AI and help create an opportunity for Mexican researchers and industrial developers to learn more about AI from the international community.