And that increased volume and velocity is manifesting itself in obvious and non obvious ways https://
wired.com/story/scammy-a
i-generated-books-flooding-amazon/
…
GENERATIVE AI
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AI-Generated Scam Books Flooding Amazon Platform
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AI Content Authenticity: How Much Online Content Is Genuinely Human?
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IMO: 100% AI content? Easy to spot. Human + AI mix? Much harder. Makes you wonder: how much online content is truly authentic human expression? Some even argue for a "Dead Internet Theory" – that bots overtook us circa 2017. Wdyt? How often do we encounter AI content online?
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AI limitations and exponential improvement potential ahead
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La ia está lejos de ser perfecta, y la experiencia fallida de McDonalds es la mejor prueba de que todavía queda mucho camino por recorrer.
— Juan Merodio (@juanmerodio) 25 août 2024
Al ritmo que está avanzando es cuestión de poco tiempo que mejore exponencialmente y nos sorprenda con lo que es capaz de hacer. pic.twitter.com/4tyVsWBsZWLa ia está lejos de ser perfecta, y la experiencia fallida de McDonalds es la mejor prueba de que todavía queda mucho camino por recorrer. Al ritmo que está avanzando es cuestión de poco tiempo que mejore exponencialmente y nos sorprenda con lo que es capaz de hacer.
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Synthetic Data Enables Superhuman LLM Performance via RL Training
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Synthetic data for LLMS and RL/RLHF/DPO can both train superhuman performance, model permitting.
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Amazon Q AI Saves $260M Through Automated Java Upgrades
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Amazon's AI assistant, Amazon Q, has saved the company $260M and 4,500 developer-years of work by drastically cutting down software upgrade times. Average app upgrade to Java 17 used to take 50 dev days. Now takes just a few hours. @ajassy confirmed that devs shipped 79% of
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LLM Challenges: Infrastructure, Architecture, Data and Applications
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10). Challenges and Responses in the Practice of LLMs – curates a set of important questions with insightful answers; questions are categorized across topics such as infrastructure, software architecture, data, application, and brain science.
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MagicDec: Speculative Decoding Enhances LLM Throughput and Latency
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7). MagicDec – shows how speculative decoding can enhance throughput, reduce latency, and maintain accuracy in long context generation scenarios; it finds that as sequence length and batch size increase, bottlenecks shift from compute-bound to memory-bound…
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Controllable Text Generation Methods for Large Language Models Survey
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8). Controllable Text Generation for LLMs – provides a comprehensive survey on methods for controllable text generation in LLMs; discusses issues like safety, consistency, style, and helpfulness.
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GraphRAG Methods: Graph-Based Indexing and Enhanced Retrieval Techniques
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6). A Comprehensive Overview of GraphRAG Methods – focuses on techniques applied to the GraphRAG workflow (graph-based indexing, graph-guided retrieval, and graph-enhanced generation).
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Meta Agent Search: Automated Design of Agentic Systems
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1). Automate Design of Agentic Systems – presents Meta Agent Search, a meta agent that iteratively programs and tests new agents based on a growing archive of previous discoveries; claims that with their approach it is possible to learn any possible agentic system including