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Taking a responsible path to AGI

We’re exploring the frontiers of AGI, prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community. Artificial general intelligence (AGI), AI that’s at least as capable as humans at most cognitive tasks, could be here within the coming years. Integrated with agentic capabilities, AGI could supercharge AI to understand, reason, plan, and execute…

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Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute Allocation

Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget forcing has emerged as an efficient technique in LLMs, enabling improved performance…

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The Art of Hybrid Architectures

In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images. This time, I want to go a step further and explore a new question: Can different architectures complement each other to build an AI that “sees” like an expert? Introduction: Rethinking Model Architecture Design While building a…

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This AI Paper from UC Berkeley Introduces TULIP: A Unified Contrastive Learning Model for High-Fidelity Vision and Language Understanding

Recent advancements in artificial intelligence have significantly improved how machines learn to associate visual content with language. Contrastive learning models have been pivotal in this transformation, particularly those aligning images and text through a shared embedding space. These models are central to zero-shot classification, image-text retrieval, and multimodal reasoning. However, while these tools have pushed…

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Optimizing Imitation Learning: How X‑IL is Shaping the Future of Robotics

Designing imitation learning (IL) policies involves many choices, such as selecting features, architecture, and policy representation. The field is advancing quickly, introducing many new techniques and increasing complexity, making it difficult to explore all possible designs and understand their impact. IL enables agents to learn through demonstrations rather than reward-based approaches. The increasing number of…

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