Machine learning

Automated discourse analysis / Analyse automatique du discours

Over the past few years, artificial intelligence has been used in lots of applications. Its history is already paved of several milestones such as the golden years in the 70s, the fall in the 80s, the boom of big data, the rise of deep learning or more recently, better-than-human strategy game players. The past two years are considered by several AI prominent figures like Yann Le Cun to be the latest revolution in AI, with a focus on natural language processing (or NLP). Indeed, in 2019, OpenAI kicked the anthill once more on the topic of NLP when they released an AI capable of generating amazingly realistic text. They even decided to refrain from publishing the code – contrary to most of their projects – because they were worried it could be used to create fake news more easily.

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AI & Art with Cali Rezo (4): Explainability and uncertainty of AI models

In the third article of the series, we discussed how to apply AI to art analysis. Even if our results were not as conclusive as we’d hoped, they still raised a few questions that we will tackle today: what is really happening in these black box models that are neural networks? And to which extent can we assess how certain a model is of its predictions?

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AI & Art with Cali Rezo (1): Project & Goals

Nowadays, machine learning (ML) is a red-hot topic which is discussed everywhere in various ways. More and more companies are relying on AI as part of their production process, be it in the domain of finance, medicine, management, art… This last application of ML algorithms, in particular, is really interesting to me. And, since the great abstract painter Cali Rezo shares this interest, we decided we would collaborate on a project to study how to apply AI to art.

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