Trusted AI

Trop souvent, l’open source est encore perçu comme une solution gratuite ou communautaire. En réalité, il s’agit d’un véritable atout industriel. Adopter des modèles ouverts permet de garder le contrôle sur ses déploiements, d’adapter les modèles à ses besoins métier, et surtout d’assurer une transparence sur leur fonctionnement.

Dans cet article découvrez comment créer une IA générative souveraine entièrement sécurisée et pourquoi c’est essentiel pour toute entreprise et institution française !

Dans cet article, découvrez les risques de sécurité liés à l'utilisation de ChatGPT et l'alternative souveraine et 100% sécurisée Craft GPT.

Let's take stock of where AI stands from a scientific, business, political and societal point of view.

Using Machine Learning to solve a problem is good, understanding how it does is better. Indeed, many AI-based systems are characterized by their obscure nature. When seeking an explanation, looking to understand how a given result was produced, exposing the system’s architecture or its parameters alone is rarely enough. Explaining that a CNN recognized a dog by detailing the Neural Network weights is, to say the least, obscure. Even for models deemed as glass-box such as decision trees, a proper explanation is never obvious.

As a general rule, all data ought to be treated as confidential by default. Machine learning models, if not properly designed, can inadvertently expose elements of the training set, which can have significant privacy implications. Differential privacy, a mathematical framework, enables data scientists to measure the privacy leakage of an algorithm. However, it is important to note that differential privacy necessitates a tradeoff between a model's privacy and its utility. In the context of deep learning there are available algorithms which achieve differential privacy. Various libraries exist, making it possible to attain differential privacy with minimal modifications to a model.