ai IMAGINATION
You may not know it yet, but you don’t need to look too far to realize that generative AI is already a big part of our lives. Whether you’ve opted for a cartoon version of yourself as a profile picture, peeked at what you might look like at 90 (gulp), or your personal identity has been transformed into a synthetic version of you somewhere out there in the ether—using a synthetic data generator—that’s generative AI doing its thing. And if the last example sounded like something out of a sci-fi movie, know that this is indeed already happening—and you’ll be happy to learn why this is a good thing for you.
But what exactly is generative AI, and why should we care?
As explained in the Complete Guide to Generative AI in 2022, “Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data, in a manner which tricks the user into believing the content is real.”
It’s called training data as the AI learns from existing (raw or production) data to create new content. This may be images, audio files or even structured data sets. Although generative AI may be something almost imaginary in how we perceive it, it’s rooted in reality and based on real data, of which a lot is needed for the training (or generation) to be effective, and accurate.
Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.
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