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►[Mit Technology Review]  What comes next for AI copyright lawsuits?

• Last week, the technology companies Anthropic and Meta each won landmark victories in two separate court cases that examined whether or not the firms had violated copyright when they trained their large language models on copyrighted books without permission. 
• The rulings are the first we’ve seen to come out of copyright cases of this kind. This is a big deal!
• The use of copyrighted works to train models is at the heart of a bitter battle between tech companies and content creators. That battle is playing out in technical arguments about what does and doesn’t count as fair use of a copyrighted work. But it is ultimately about carving out a space in which human and machine creativity can continue to coexist.

►[Wired] OpenAI Leadership Responds to Meta Offers: ‘Someone Has Broken Into Our Home’

• Mark Chen, the chief research officer at OpenAI, sent a forceful memo to staff on Saturday, promising to go head-to-head with the social giant in the war for top research talent. 
• This memo, which was sent to OpenAI employees in Slack and obtained by WIRED, came days after Meta CEO Mark Zuckerberg successfully recruited four senior researchers from the company to join Meta’s superintelligence lab.
  “I feel a visceral feeling right now, as if someone has broken into our home and stolen something,” Chen wrote. “Please trust that we haven’t been sitting idly by.”

►[Yann LeCun] How to Build a Vibrant Technology Industry (by attracting top scientists to your country)

• Renforcer et consolider vos connaissances mathématiques pour la finance quantitative 
• Scientific research is the main engine of economic growth
• Economic growth is powered by product innovation.
• Product innovation often builds on technological progress.
• When a new technology platform becomes widely available, it creates an innovation ecosystem that opens the door to new products.
• But technological progress often relies on fundamental research carried out 5, 10, or 20 years earlier in universities, public research institutions, and (more rarely) in industry research labs.

►[machinelearning.apple.com] The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

• Renforcer et consolider vos connaissances mathématiques pour la finance quantitative 
• Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. 
• While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. 
• Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. 

Matheuses, Les filles, avenir des mathématiques - CNRS Editions

Par Clémence Perronnet, Claire Marc et Olga Paris-Romaskevich, préface de Catherine Goldstein

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