Professor of Statistical Learning at École Polytechnique (IP Paris)
Hatim Bourfoune
AI research engineer at IDRIS (CNRS)
Pierre Cornette
AI support engineer at IDRIS (CNRS)
Key takeaways
Generative AI can create content from a database that has been ingested and according to the indications they are given.
These technologies, which remain new, are still being developed and there are still several areas for improvement: reliability, bias in the database, etc.
ChatGPT and Bloom are just two models of generative AI, but the concept can be extended to a multitude of applications.
These technologies raise a few questions, such as their ecological impact and the risk of using them for potentially malicious purposes.
Economist with the International Labour Organisation (UN)
Key takeaways
While generative AI worries workers, ILO economists have studied its impact on the global labour market.
The risk is not so much the massive replacement of jobs by bots, but rather the transformation of professions, which will affect 10-13% of occupations worldwide.
The professional category of low-skilled office jobs will be particularly affected by AI, since 82% of tasks could be entrusted to bots.
Women are particularly affected by automation, as they are twice as likely to be employed in these administrative positions.
AI will also increase inequalities, as low-income countries that do not have access to these technologies will have more jobs that could potentially be automated.
The current challenge is to support, organise and reflect on the deployment of AI to limit the social consequences.
Researcher at the CNRS i³-CRG* laboratory and Professor at École Polytechnique (IP Paris)
Erwan Le Pennec
Professor at École Polytechnique (IP Paris)
Key takeaways
Many myths and misconceptions surround AI, especially since the rise of generative AI such as DALL-E.
In reality, these types of AI do not represent a technological revolution, from an innovation point of view, since their existence predates the advent of ChatGPT.
What we are witnessing is a change in usage, thanks to the start-ups that have “opened up” access to A.I. for the general public.
In reality, the training protocols for these types of AI are kept secret by the companies, but programming interfaces give users the illusion of mastering the algorithm.
Despite concerns, this wide and open use of AI will make human expertise more necessary than ever.
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