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amatriain.net
| | amatria.in
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| | In the landscape of Generative AI (GenAI), we often find ourselves amazed at the rapidity and scale of advancements. GPT-4 stands as a shining example, pushing the boundaries of linguistic understanding and generation. Yet, as we move forward, a compelling new horizon emerges: the Multimodal Generative AI Revolution. By melding GPT-4's textual capabilities with multimodality-integrating diverse data types such as images, voice, and video-we're not just opening a door, but unleashing a tidal wave of transformative potential that promises to redefine our digital experiences.
| | amatria.in
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| | (Interestingly, this is the first time arXiV has declined a submission from me. I would give the editors kudos for finally taking their role seriously if it wasn't because I suspect this is simply the result of a poor algorithmic decision that detected I had used AI assistance in writing the paper and violated a rule of disclosing such use. More discussion on this topic in this LinkedIn post and the comments.)
| | blog.pdebruin.org
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| | RAG Hack hackathon recordings and presentations cover Langchain, vector stores, and multi-language RAG patterns.
| | jalammar.github.io
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| Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Korean, Chinese (Simplified), Russian, Turkish The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let's remove the aura of mystery around GPT3 and learn how it's trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generat...