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The AI policy battle is heating up, and Hugging Face is making its case at the White House. They argue that open-source AI can match, if not surpass, proprietary models while being more secure and affordable. This puts them at odds with OpenAI, which favors a more closed, lightly regulated approach. The stakes? Whether AI development will be controlled by a few powerful players or remain open and accessible to all.

Meanwhile, AI is proving it has a sense of humor—sort of. A new study found that AI-generated meme captions are generally funnier and more shareable than human-made ones, though the best jokes still come from people. Interestingly, human-AI collaborations produce the most engaging content, showing that AI might be best as a comedic sidekick rather than the main act. Could this be the future of internet humor?

Top AI news

1. Microsoft expands solar energy to power AI data centers
Microsoft is expanding its renewable energy portfolio with a new 475 MW solar power deal to support its AI data centers. The agreement with AES includes three solar projects across the Midwest, reinforcing Microsoft's commitment to sustainability. Solar energy is favored for its rapid deployment and cost efficiency, but energy stability remains a challenge. Battery storage solutions are increasingly being integrated to ensure continuous power supply. As demand for computing power surges, Microsoft’s renewable strategy could shape the future of data center energy consumption.

2. Hugging Face pushes open-source AI in White House policy debate
Hugging Face is pushing for open-source AI in the White House’s AI policy discussions, arguing that open models can rival proprietary ones at lower costs. Their submission highlights breakthroughs like OlympicCoder and OLMo 2, which match or surpass closed systems. They also emphasize the security and economic benefits of transparency. This contrasts with OpenAI’s push for minimal regulation and proprietary control. The debate will shape the future of AI in the U.S., determining whether AI development remains centralized or becomes more democratized.

3. Pruna AI open-sources its AI model optimization framework
Pruna AI, a European startup specializing in AI model compression, has open-sourced its optimization framework. The tool applies techniques like caching, pruning, quantization, and distillation to improve model efficiency while maintaining performance. It also evaluates quality loss post-compression. Inspired by Hugging Face, Pruna AI aims to standardize these methods for broader accessibility. Its optimization agent automatically fine-tunes models based on performance needs. The company offers a pro version with hourly billing and has raised $6.5 million in funding. Currently, it focuses on optimizing image and video generation models.

Bonus. AI vs Humans: Meme Battle
A recent study found that AI-generated meme captions outperform human-made ones in humor, creativity, and shareability on average. However, the funniest individual memes were still created by humans, and human-AI collaborations produced the most engaging content. AI’s strength lies in generating broadly appealing humor, but humans excel at crafting unique, standout jokes. The study also highlights that AI-assisted meme creators generate more content with less effort, though quality remains a human advantage. While AI can enhance meme creation, human wit and creativity remain irreplaceable.

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AI & Robotics. The scientific paper
Imagine a robot navigating through a cluttered room. To move efficiently, it must optimize its path—avoiding obstacles, minimizing energy consumption, and reaching its goal as quickly as possible. Similarly, in robotics, optimization plays a crucial role in various tasks like motion planning, object recognition, and reinforcement learning. Optimization is the mathematical process of finding the "best" solution to a problem, given certain constraints. In robotics, this could mean determining the ideal trajectory for a robotic arm in a factory, training an AI to walk like a human, or enabling self-driving cars to react optimally to their surroundings. The challenge is that these problems are often complex, requiring efficient techniques to find solutions quickly and accurately.

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