R&D topics of applied AI

PRAIA focuses on AI. Thanks to its broad experience in pattern recognition and machine learning, it covers three strategic lines: AI/SC, AI/HEALTH, and AI/3DII.
PRAIA R&D topics of applied AI

Over the past few years, the term Artificial Intelligence (AI) has become part of the popular vocabulary through its constant use in the media and its increased application in all sectors and fields. Much of the expansion of AI is motivated by significant advances in machine learning, or Machine Learning (ML). ML involves using algorithms that allow computers to autonomously learn to perform a given task through analyzing examples rather than explicit programming by a person. Despite the promising prospects for this field, there are multiple challenges in applying these techniques in a real-world environment. It is typical for the industry, healthcare, and other domains to work with moderate-sized datasets characterised by few samples. This reality makes it difficult to establish reliable decision boundaries with a small number of observations, which implies a lower accuracy in the models obtained. On the other hand, it is possible that the problem posed is incomplete because it is not possible to be sure to what extent the objective is related to the information available for each individual.

The combination of these challenges and the high degree of specialization needed to offer significant and innovative results concerning state of the art has made PRAIA focus its efforts on two critical domains: Industry and Health. In addition, a more basal and general line of work is maintained to provide an initial response to problems posed by companies and organizations. Thus, PRAIA focuses its work in AI through its experience in pattern recognition and machine learning, along the following lines:

  • AI applied to 3D Industrial Inspection [3DII]
  • AI applied to the field of Health [HEALTH]
  • Research and development for tasks with a strong AI component [SC]

Experimentation and research in basic AI and applied to different areas

AI, or artificial intelligence, covers a significant number of techniques and is of interest in many domains and topics. In this manner, it covers machine learning methods, going through computer vision, natural language processing or data mining. These techniques can be applied in many domains: healthcare, finances, manufacturing, transportation, etc.

Artificial Intelligence applied to Healthcare

This line of work is framed in the area of Bioinformatics and aims to provide novel solutions to problems related to precision medicine and offer help in the investigation of the mechanisms inherent to diseases. In this way, the application of AI techniques, more specifically Machine Learning (ML), and existing computing capabilities to the field of health will make it possible to generate support systems for physicians. This would facilitate their work and improve the quality of the services provided, resulting in a better quality of life for patients. Thus, the aim is to apply to healthcare techniques that have proven their validity and effectiveness in other fields, such as biometrics, handwritten text recognition, and automatic translation.

3D Industrial Inspection

For this strategic line, the work is focused on applying artificial intelligence in industrial environments, especially for quality control through 3D inspection. It aims to improve industrial processes such as in-line inspection, early detection of defects in the production environment, increasing product quality, and reducing material waste. Machine learning, pattern recognition, and metrology techniques are applied to achieve these objectives, ensuring quality and accuracy standards beyond the reach of traditional industry.