Posts

What are neural networks?

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Neural networks are AI models designed by machine learning. They are inspired by the workings of the human brain and allow machines to carry out different tasks autonomously. Using large quantities of data as examples, they can be trained in image recognition or text analysis. Beyond their predictive performance, a number of scientific challenges still need to be resolved, concerning the understanding of their limits, reliability of their use, and their acceptability, in addition to ethical issues such as energy consumption. Stephan Clémençon, a researcher in artificial intelligence at Télécom Paris, explains more.

Protecting sensitive sites: AI in radar systems

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Radar systems are safe and durable, and used for both detection and surveillance. However, they deliver signals, not images, which can be difficult to interpret. Using artificial intelligence to complement current algorithms offers a technological solution to this problem. The RadaR-IO laboratory, shared by IMT Mines Albi and the company EPSI, is looking to develop and industrialize this solution.
Comprendre informations du langage, algorithms

Making algorithms understand what we are talking about

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Human language contains different types of information. We understand it all unconsciously, but explaining it systematically is much more difficult. The same is true for machines. The NoRDF Project Chair "Modeling and Extracting Complex…
Eclairer boites noires, algorithms

Shedding some light on black box algorithms

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In recent decades, algorithms have become increasingly complex, particularly through the introduction of deep learning architectures. This has gone hand in hand with increasing difficulty in explaining their internal functioning, which has become an important issue, both legally and socially. Winston Maxwell, legal researcher, and Florence d’Alché-Buc, researcher in machine learning, who both work for Télécom Paris, describe the current challenges involved in the explainability of algorithms.