ChatGPT search vs. Google: A deep dive analysis of 62 queries (25 minute read) A study analyzing 62 queries compared ChatGPT search and Google, highlighting strengths and weaknesses. Google led in informational, local, and commercial queries, while ChatGPT search showed promise in content gap analysis and disambiguation tasks. Both platforms had issues with errors, omissions, and incomplete responses, but Google generally provided more reliable search results. | 6G-AI Mashups Will Reshape the Telecom Industry (7 minute read) The EU-U.S. 6G-XCEL project and joint efforts like ACCoRD and COSMOS are advancing 6G research through collaborations on AI-integrated network architectures. Workshops at Rutgers highlighted innovations in 6G technology and emphasized open-source initiatives and industry partnerships. These initiatives aim to accelerate progress and create interoperability frameworks for the next generation of wireless networks. | Why Google bought Character AI (2 minute read) Google acquired Character AI primarily for its cost-effective inference technology, which enabled scalable AI interactions. This efficiency allows Google to offer AI models for free via AI Studio without harming unit economics. The acquisition complements the trend toward inference-time optimizations as pre-training returns diminish. | | Transfusion in Pytorch (GitHub Repo) Lucidrains has written up a great reimplementation of Meta's token + diffusion model Transfusion which can do images and text in a single model. | | Fast LLM Inference From Scratch (6 minute read) The article outlines the process of building an LLM inference engine using C++ and CUDA without external libraries, focusing on optimizing inference speed for consumer devices. It explores techniques like multithreading, vectorization, warp reductions, coalescing, and quantization to maximize performance and successfully surpass llama.cpp's throughput in specific scenarios. Additionally, the piece discusses potential for further optimization and the advantages of using established libraries for production-grade implementations. | Computing inside an AI (11 minute read) Shifting from a model-as-person to a model-as-computer metaphor could enhance AI usefulness by enabling graphical interfaces and direct manipulation, rather than relying on slow conversational inputs. This new interaction paradigm could allow users to engage with AI like a dynamic, customizable app, offering more efficient and versatile functionality. Generative interfaces could eventually transform computing, allowing users to modify and create applications on demand for specific tasks. | | Love TLDR? Tell your friends and get rewards! | Share your referral link below with friends to get free TLDR swag! | | Track your referrals here. | Want to advertise in TLDR? ๐ฐ | If your company is interested in reaching an audience of AI professionals and decision makers, you may want to advertise with us. If you have any comments or feedback, just respond to this email! Thanks for reading, Andrew Tan & Andrew Carr | | | |
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