France’s New AI Champion Scares Silicon Valley

France’s New AI Champion Scares Silicon Valley.


Author(s): Ignacio de Gregorio

Originally published on Towards AI.

Mistral’s New Model is a Box of Pleasant Surprises

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Source: Author with Dall-E3

Just a few months ago, a petit small French company was born. Three weeks later, it had already raised $113 million.

At the time, I recall searching the website, and it was almost plain HTML, a seemingly unimpressive CSS animation, and literally three lines of text.

Yet, the company was valued at a quarter of a billion dollars.

No team, let alone a product.

Still, they raised tons of millions through a diverse set of investors who thought that three Frency ex-DeepMind and Meta researchers were the real deal.

Now, we are starting to see why, thanks to the release of… Read the full blog for free on Medium.

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Published via Towards AI


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.