jrob77.bsky.social
👨🏾💻Data Science 💻ML 🤖AI 👷🏾♂️Builder of Things
#DataScience #MachineLearning #AI #DeepLearning #ComputerVision
LinkedIn: https://linkedin.com/in/jasrobinson12
GitHub: https://github.com/techthumb1
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199 followers
1,095 following
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They created custom instructions within the LLM specifically according to username, for a similar scenario, from that user. Positive those actions aren’t isolated to one user, but likely a distribution list of Superusers of similar celebrity.
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Happy Birthday🎂
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Congratulations!
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Competitive Scrabble 👀
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Scaling just means making the app bigger. Functionality, security, etc.
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Thanks for sharing! Currently working on a project of different modalities for segmentation and classification tasks. This surely will help!
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Hilarious 🤣
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I personally agree that LLMs do not “know anything”, but extremely useful “tools”, particularly in research. They will guide you to an area of possible solutions, not absolute solutions.
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Great point! The simplified dataset will allow the LLM to find patterns easier than a trained physician, but at the cost of loss context.
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Congratulations 🎉
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Yea there can be moments of fun but the reality when the camera is off is far from fun it. Nothing related to violence. But they can only go out at certain times, never at night, told when to go to sleep when to wake up. Work 30hrs a week for $10-$15 a month. Nah, them tiktoks a facade. Stay free💯
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Great questions. Yes, technically the bias introduced by medical technicians would affect the model’s capability to better generalize the data. However, there are methods that are used to account for the bias in models, in conjunction with the technicians and human raters to mitigate bias.
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Bro, they’re not, trust me!
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Possibly that would apply to text generation, but not specific for image classification or segmentation tasks.
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Great article! I perform my work using with LLMs, much in the same way, and my productivity as increased drastically. It’s not about relenting your entire thought process to the LLM, it is a true collaboration when used efficiently.