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Description
Artificial intelligence (AI) as a science must be able to predict, describe and control the phenomena it studies; using techniques and/or tools that exploit the extracted information and transform it into knowledge in order to prevent failures and optimize systems. In applied artificial intelligence (Weak AI or Applied AI), the groups of neuronal networks integrate the use of algorithms and guided learning with Machine Learning with the objective of parsing data, learn from it and then be able to make a prediction or suggestion about a certain task as accurate as possible. In this way, the machine is trained using a large amount of data giving the algorithms the opportunity to be perfected. The algorithms have evolved in order to analyze and obtain better results and using different techniques: decision trees, inductive logic programming (ILP), clustering to store and read large volumes of data, Bayesian networks, static pattern recognition, time series prediction or classification based on numerical information.Other information
Number of researchers:
2
Development status:
In research and development phase
Intellectual Property Rights:
Susceptible de Propiedad Intelectual
Differentiation in the market:
Tendencias de mercado
Applicability of technology:
Yes
Companies and markets:
Machine Learning results especially effective with problems of complex nature where the application of algorithms helps to obtain precise solutions with the resulting savings in time that this method implies. Among other activities, some of the day-to-day activities that are driven by Machine Learning are the following: - Facial, voice or object recognition: applied, for example, to the screening of patients with genetic disorder (Down syndrome). - Prediction and forecast. Weather, traffic, or to prevent technological failures in equipment. - Text comprehension. This is applied to structured news summaries or comments about a specific topic. - Autonomous vehicles and robots. Coast to coast car. - Faster and more flexible optimization methods. The adequate moment for a particular task is evaluated. - High-quality image analysis. - Economic data analysis. To operate in the stock market or avoid fraudulent transactions. - Consumer behavior and productivity analysis. For the identification of potential clients and the identification of behavior patterns. - Applications in the biohealth/healthcare sector. For example, patient classification based on dry eye studies (project in cooperation with IOBA).- Industrial engineering for engine classification (project in cooperation with the Department of Electrical Engineering of the University of Valladolid). - Other particular applications of different nature.
Advantages:
Representation of knowledge and reasoning in a more structured way through which to learn to act faster and better. These calculations allow for continuous learning to ultimately generate reliable decisions and results and optimize planning.
Additional Information:
Knowledge of the research group taught in undergraduate teaching.
UNESCO Code:
1203 - Computer Sciences (see 3304)
Other members:
Joaquín Adiego Rodriguez
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