8. A Survey of Context Engineering for LLMs This survey defines Context Engineering as a formal discipline for optimizing information given to LLMs, outlining its core components, retrieval, processing, and management, and their integration in systems like RAG, memory, and
SYSTEMS
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LLMs in AIOps: Survey of 183 Papers and Methods
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10. A Survey of AIOps This survey analyzes 183 papers to evaluate how LLMs are being used in AIOps, focusing on data sources, task evolution, applied methods, and evaluation practices.
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Edge-Native Systems Transforming Industry 4.0 Operations
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Legacy systems weren’t designed for the speed, scale, or autonomy Industry 4.0 demands.
Edge-native systems are.
Here’s how they’re reshaping the future of industrial operations: https://
buff.ly/Ij8YA7W #blackpearl_iiot #EdgeNative #IndustrialIoT @IIoT_World via @fogoros -
Scaling AI Evaluation: Building Meaningful Benchmarks from Single GPU
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coming up with evals that start at 1-gpu but are meaningful at eventual scale will be the hardest part.
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Paradigm Shifts in AI: Collective Intelligence and Artificial Life
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Blaise Agüera explains interrelated paradigm shifts which he believes are core to the future development in AI. I like his take on collective intelligence, the future of artificial life research, and the (somewhat philosophical) discussions about consciousness and theory of mind.
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Transformers: The Standard AI Model’s Strengths and Gaps
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Transformers are the standard model of AI. They’re both:
– The result of a long series of breakthroughs
– Surprisingly powerful
– A big mess
– Missing key pieces -
Advanced Reasoning Capabilities in AI Systems and Problem-Solving
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Reasoning as in science, math, and hard problem-solving capabilities.
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Portable User Preferences and AI Memory Across Tools
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i hope we end up in a place where we can easily port and combine user preferences and other memories that ai tools learn through our interactions
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ChatGPT Gets Computer Access and Computing Capabilities
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ChatGPT gets a computer https://
stratechery.com/2023/chatgpt-l
earns-computing/
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Axelera Metis Delivers Stable Energy Efficiency Across Vision Models
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On 14 HD video feeds running models from SSD-MobileNet to YOLOv8l, Axelera’s Metis kept energy cost/frame stable & lowest across the board—no spikes on heavier models. That kind of real-world predictability is what edge vision builders need Check it out: