We’re excited to spotlight the recently announced collaboration between @G42ai , @core42_ai , and @Qualcomm
, some of our most important strategic partners. Core42, a G42 company and the UAE-based national-scale enabler for cloud and generative AI, announced a significant leap
GENERATIVE AI
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G42 Core42 Qualcomm strategic AI collaboration announcement
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GPT2-Chatbot Viral Mystery: Suspected Side Project Gone Mainstream
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my personal guess on gpt2-chatbot given @osanseviero has been off for the last 10 days at this stage strongly suspect it’s a side project of his, gone viral Prove me wrong
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GPT2-Chatbot RAG capabilities and model transparency concerns
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Can anyone @lmsysorg confirm if gpt2-chatbot has the ability to run RAG against external tools or if it's working entirely from its own weights? A frustrating thing about opaque model releases is that without knowing details like this they're even harder to evaluate
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New AI Model Development Timeline Testing Strategy
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I would expect a completely new model to have an earlier cutoff date, because there's a bunch of extensive testing you have to do on that new model before you release it even as a preview Trained until Nov then 6 months of internal evaluation before public preview makes sense
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New GPT Model Preview: Larger Parameters and Knowledge Base
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From what I've seen so far this looks like a new model, trained differently from the current GPT-4 models – I think it has more "knowledge" baked in, so maybe a larger parameter count? I wouldn't be at all surprised if this turns out to be a preview of an OpenAI "GPT 4.5"
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System Prompts Don’t Guarantee Truthful Model Self-Description
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Worth noting that just because the system prompt says "based on the GPT-4 architecture" doesn't mean that the model is actually based on GPT-4! The goal of a system prompt is to influence the model to behave in certain ways, not to give it truthful information about itself
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GPT2 Model Name Origin: OpenAI 2019 Release Joke
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I'm confident the name is a joke – GPT2 was a model OpenAI released back in 2019
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Benchmarking transparency needed for AI model testing
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I'd prefer it if our benchmarking groups were 100% transparent about how their testing stack works and what models they are including in the mix
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Training cutoff dates help AI models refuse out-of-scope questions
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Doesn't imply much to be honest, the thing that's most interesting for me in there is the training cut-off – it's good to provide an accurate date to the model so it can usefully refuse to answer questions about events beyond that point
