New paper w Nicholas Carlini & @florian_tramer
: "Considerations for Differentially Private Learning with Large-Scale Public Pretraining." We critique the increasingly popular use of large-scale public pretraining in private ML. Comments welcome. https://
arxiv.org/abs/2212.06470 1/n
AI
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Differential Privacy Challenges in Large-Scale AI Pretraining
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FairMOT: Solving Re-identification in Deep Learning Object Tracking
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Most Deep Learning Multiple Object Trackers have no trouble with the detection task but perform poorly in the re-identification task. This is where FairMOT comes into the picture. Check out our read on Object Tracking with FairMOT.https://t.co/0x5fOXrG7N#fairmot #ai #ml #dl pic.twitter.com/z5YnJ1M14c
— Satya Mallick (@LearnOpenCV) 15 décembre 2022Most Deep Learning Multiple Object Trackers have no trouble with the detection task but perform poorly in the re-identification task. This is where FairMOT comes into the picture. Check out our read on Object Tracking with FairMOT. https://
learnopencv.com/object-trackin
g-and-reidentification-with-fairmot/
… #fairmot #ai #ml #dl -
Five Billion Online: Internet Evolution and Future Digital Trends
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30 years from the first website, to 5.41 billion people #online, what’s next in 2023 and beyond? >>> chart by @kepios via @MikeQuindazzi >>> #mobile #social #fintech #digital #cloud
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Challenge Approach: Reimplementing Techniques from Foundations in Python
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(Oh BTW personally for this challenge I'm following the "from the foundations" rules we're using for our current course: only using techniques I've first reimplemented from scratch in pure Python. But y'all can use external libs and stuff if you like…)
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Seeking source for GPT-3 family tree graphic
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great graphic – is there a canonical source for the gpt3 family tree or did you just come up with this? (want to make sure i cite properly when i definitely use this in future)
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Alignment reduces misuse barrier, raising safety concerns.
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Note while models tuned this way are aligned to follow the intent of prose instruction, the barrier to their misuse declines as well, presenting new safety issues.
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RLHF automates human evaluation and generates tuning data
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Later, reinforcement learning was used to automate the task of human evaluation as well. This yielded a endless fountain of tuning data that could be produced entirely by machine — a process known as RLHF.
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Tuning models on filtered self-generations for smarter AI
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By tuning the model on its own generations, filtered to those deemed perfect by human evaluators, greater volumes of data could be used, yielding a more intelligent and obedient model.
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Human demonstrations limited early instruction tuning models
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Early attempts at instruction tuning relied entirely on demonstrations from humans. This made the model easier to prompt, but the approach was limited by the inherent difficulty of manufacturing new humans.