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- AI Replace Teaching Staff👨🏫
AI Replace Teaching Staff👨🏫
From AI classrooms in Arizona achieving in 2 hours what traditional schools do in 6, to DeepSeek's 671B-parameter breakthrough, and ARMOR's human-like robot navigation - the future is now!
As the dawn of 2025 breaks upon us, may this year bring unprecedented breakthroughs in technology that continue to reshape our world for the better. We hope this year fills your journey with curiosity, discovery, and the joy of learning. May your ventures be bold and your innovations transformative.
In a sun-drenched Arizona classroom, twelve-year-old Maya logs into her two-hour school day at Unbound Academy. Her AI tutor greets her with a personalized lesson plan that seems to know exactly how her mind works. "Today, we're exploring quantum physics," announces the AI, adapting its teaching style in real-time as Maya's eyes light up with curiosity.
Meanwhile, in a bustling research lab in Pittsburgh, a humanoid robot named ARMOR gracefully weaves through a maze of obstacles. Its movements are nearly indistinguishable from a human's, thanks to an array of depth sensors that create an invisible protective field around it. Dr. Sarah Chen, the lead researcher, watches in amazement as her creation demonstrates a level of spatial awareness that was once thought impossible for machines.
Across the Pacific, in a high-tech facility in China, the DeepSeek-V3 AI model processes information with the power of 671 billion parameters, each firing in perfect harmony. Like a master conductor leading an orchestra of data, it orchestrates responses with a precision that makes previous AI models seem like simple calculators in comparison.
Join us as we dive deeper into these groundbreaking developments that are not just making headlines but making history. Welcome to the future – welcome to 2025.
Let the journey begin.
Unbound Academy: AI-Driven Education with Two-Hour School Days

In Arizona, Unbound Academy is experimenting with an innovative educational model that centers around artificial intelligence (AI) and a condensed two-hour school day. Approved by the Arizona State Board for Charter Schools, this program is designed for students in grades four through eight, where personalized AI will deliver academic lessons using platforms like IXL and Khan Academy. The goal is to enhance student engagement and learning efficiency, allowing more time for life skills workshops in areas such as financial literacy and public speaking, guided by mentors instead of traditional teachers. This approach builds on previous successes in private schools and aims to demonstrate that AI can facilitate effective learning while preparing students for real-world challenges. However, concerns about the absence of human teachers and the emotional support they provide remain prevalent in discussions about this model's future.
Qwen 72B: AI Advancements in Education

DeepSeek, a Chinese AI company, has launched the DeepSeek-V3 AI model, an open-source large language model (LLM) featuring an impressive 671 billion parameters, surpassing Meta's Llama 3.1 model, which has 405 billion parameters. This new model employs a mixture-of-experts (MoE) architecture, allowing it to activate only the relevant parameters for specific tasks, enhancing efficiency and accuracy. The DeepSeek-V3 is hosted on Hugging Face and is designed for efficient inference and cost-effective training, utilizing techniques like Multi-head Latent Attention and DeepSeekMoE. Pre-trained on 14.8 trillion tokens, it incorporates supervised fine-tuning and reinforcement learning to produce high-quality responses. Internal tests suggest that DeepSeek-V3 outperforms other models such as Meta Llama 3.1 and Qwen 2.5 across various benchmarks, although these results have not been independently verified. The model's code is available under an MIT license for both personal and commercial use, and it can be accessed through an online chatbot platform or via API for developers.
ARMOR: Enhanced Collision Avoidance for Humanoid Robots

ARMOR (Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning) is a cutting-edge system that enhances humanoid robots' spatial awareness and motion planning. Developed by researchers from Carnegie Mellon University and Apple, ARMOR employs low-profile, distributed depth sensors to create a comprehensive point cloud perception around the robot, significantly reducing occlusions. The system uses a data-driven motion planning policy called ARMOR-Policy, which is trained using imitation learning on a dataset of human motions. This approach allows for dynamic collision avoidance, resulting in a 63.7% reduction in collisions and a 78.7% increase in success rates compared to traditional setups with external cameras. ARMOR also demonstrates superior performance over existing sampling-based motion planners, achieving 31.6% fewer collisions and being nearly 26 times more computationally efficient. By integrating these advanced sensors directly onto the robot, ARMOR provides a scalable solution for safe navigation in complex environments
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