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  • Smaller is Smarter
    Concerns about the environmental impacts of Large Language Models (LLMs) are growing. While AI is still developing, there is a need for more sustainable solutions. Small Language Models (SLMs) offer an alternative, running on local infrastructure with a smaller carbon footprint and ensuring data confidentiality. Article published in Towards Data Science.
  • They don’t think!
    Yann LeCun, Meta’s chief of artificial intelligence, has consistently maintained the same stance on the topic: large language models are not the future of AI and will not reach human intelligence. A recent study, conducted by six Apple engineers, demonstrates that there is no evidence that LLMs can reason. Article published on LinkedIn.
  • Towards an Inclusive AI: Closing Gaps, Protecting Humanity
    On 19 September 2024, the UN Secretary-General released the final report on artificial intelligence: “Governing AI for Humanity”. This comprehensive report highlights the urgent need for international governance of artificial intelligence to ensure its benefits are distributed equitably and its risks managed effectively.  Published in LinkedIn.
  • LLMs: main concepts simply explained
    Need to Catch Up or Lack of Time to Keep Abreast: Understanding LLMs without Delving into the Specifics. Article published in Medium.
  • Understanding LLM Prompt Hacking and Attacks
    IBM released a new Youtube video, AI: the new attack surface, which could be beneficial for the readers of this article. Below is a preview that could serve as a useful introduction. Article published in Medium.
  • Key Facts: The Impact of Generative AI on the Labour Market
    After one year since the seismic impact caused by the launch of ChatGPT in November 2022, analyses and predictions about the future of our work are emerging. Article published in Medium.
  • Pitching Your Data Strategy: Translating Tech Talk for Management and Users
    Three-dimensional simplicity: data, technology and people… and a tool to help along the way. Article published in Towards Data Science.
  • Data-driven organization with managers on board
    A possible approach to reconcile data scientists and management behind the same objective. Article published in Towards Data Science.