Ukrainian President Volodymyr Zelenskyy announced that the United States has set a June deadline for Ukraine and Russia to end the ongoing war, even as discussions regarding $12 trillion in economic deals that could impact Ukraine are underway, according to NPR Politics. In other news, an advocacy group is seeking discovery to collect documents it says the Federal Communications Commission (FCC) has wrongfully kept private regarding the Department of Government Efficiency (DOGE), as reported by The Verge.
The announcement regarding the war's deadline came over the weekend, as stated by Zelenskyy. Meanwhile, an advocacy group's legal action seeks to uncover information about the FCC's activities related to DOGE. The group's pursuit follows a year-long effort involving nearly 2,000 pages of documents, according to The Verge.
In the tech sector, Discord is facing backlash after announcing mandatory age verification for all users to access adult content. The platform plans to implement age checks using AI technology that analyzes facial structures or compares selfies to government IDs, as detailed by Ars Technica. Discord emphasized that selfie data will remain on users' devices and will be promptly deleted after age estimation. The global rollout is scheduled to begin in early March.
Also in the realm of technology, Nvidia has released DreamDojo, an AI system designed to teach robots how to interact with the physical world by watching tens of thousands of hours of human video, VentureBeat reported. The research, involving collaborators from UC Berkeley, Stanford, and the University of Texas at Austin, introduces what the team calls "the first robot world model of its kind that demonstrates strong generalization to diverse objects and environments after post-training."
Furthermore, the performance of AI is increasingly dependent on the data delivery layer between storage and compute, according to VentureBeat. Mark Menger, solutions architect at F5, noted that "They're waiting on data," highlighting the often-overlooked bottleneck in AI infrastructure. As enterprises invest heavily in GPU infrastructure, the data delivery layer is becoming a critical factor in maximizing AI workload efficiency.
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