🎓Former OpenAI founding member announces Eureka Labs

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• Robots are taking over the Army in almost every nation.

• Microsoft just announced its AI-powered Designer is out of preview and now available worldwide, bringing advanced image generation, editing, and design capabilities to users on mobile and Windows platforms.

• Anthropic is teaming up with VC firm Menlo Ventures to put $100 million toward early-stage companies who use Anthropic’s Claude AI model in their products.

• Donald Trump’s allies are reportedly drafting an AI executive order aimed at boosting military AI development, rolling back current regulations, and more — signaling a potential shift in the country’s AI policy if the party returns to the White House.

• Hugging Face introduced SmolLM, a family of high-performing small language models with 135M, 360M, and 1.7B parameters — which outperform competitors in their size categories across benchmarks.

• Proton has launched a new AI-enabled ‘Privacy-first’ writing assistant that can help users compose emails with simple prompts, redraft them and even proofread them before they’re sent.

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EDUTECH

🎓 Andrej Karpathy launches AI-powered education platform

Former OpenAI founding member and Tesla AI Director Andrej Karpathy announces Eureka Labs, an AI-integrated education startup.

The Details 📊

• Eureka Labs is an "AI-native" school leveraging advanced AI for enhanced learning

• First course, LLM101n, teaches students to train their own AI models

• Platform combines human expertise with AI teaching assistants

• Plans to offer both digital and physical cohorts

• Karpathy left OpenAI in February to pursue personal projects

Why It Matters: Karpathy's entry into AI-powered education signals a potential transformation in the sector. With his expertise and vision, Eureka Labs could pioneer new approaches to personalized learning and AI-assisted education, potentially reshaping how we acquire knowledge and skills in the AI era.

OPENAI

🔍 OpenAI enhances AI explainability

OpenAI has developed a new method to make large language models produce more understandable and verifiable outputs using a game played between two AIs to make generations more ‘legible’ to humans.

The Details 📊

• Technique uses a "Prover-Verifier Game" between two AI models

• Stronger model (prover) convinces weaker model (verifier) of answer correctness

• Process leads to solutions that are both correct and easier to verify

• Method improved accuracy by about 50% compared to optimizing solely for correctness

• Tested on grade-school math problems, with plans to expand to more complex domains

• Aims to keep AI outputs interpretable as capabilities surpass human intelligence

• Highlights challenge in balancing AI capability with explainability

Why It Matters: As AI systems become increasingly complex, ensuring their outputs remain interpretable is crucial for safety and trust. This research offers a scalable approach to potentially keep AI systems 'honest' while maintaining their advanced capabilities.

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