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million public models across various sectors and serves seven million users, proposes an AI Action Plan centred on three interconnected pillars: Hugging Face stresses the importance of strengthening open-source AI ecosystems. The company prioritises efficient and reliable adoption of AI. Hugging Face, which hosts over 1.5
What happened this week in AI by Louie The ongoing race between open and closed-source AI has been a key theme of debate for some time, as has the increasing concentration of AIresearch and investment into transformer-based models such as LLMs. This would be its 5th generation AI training cluster.
Here’s a stylized sort of debate that might occur: A: Great news, our AI-assisted research team has discovered even more improvements than expected! We should be able to build an AImodel 10x as big as the state of the art in the next few weeks. we could be headed for a disaster.
(As a bonus, it doesn’t seem out of the question that transformative AI will be developed extremely soon - 10 years from now or faster. ↩ E.g., Ajeya Cotra gives a 15% probability of transformative AI by 2030; eyeballing figure 1 from this chart on expert surveys implies a >10% chance by 2028.
Data is the fuel of AI applications, but the magnitude and scale of enterprise data often make it too expensive and time-consuming to use effectively. Because of the extremely high volume and various data types, most generative AI applications use a fraction of the total amount of data being stored and generated.
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