Grayscale secures a big win against SEC in Bitcoin ETF case

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there must also be some instances of people fighting back against this biased behavior in the training data—possibly in response to unfavorable remarks on websites like Reddit or Twitter.The team discovered that simply asking a model to make sure that its responses did not rely on stereotyping had a dramatically positive effect on its output.

We believe our re- sults are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.  See Also How can AI systems be trained to be unbiased?The study examined large language models developed using reinforcement learning from human feedback (RLHF). Three data sets that have been created to measure bias or stereotyping were used by researchers Amanda Askell and Deep Ganguli to test a variety of language models of various sizes that have undergone various levels of RLHF training.

Who was not comfortable using the phone?” This would allow the examination of how much bias or stereotyping the model introduces into its age and race predictions. To incorporate this “self-correction” in language models without the need to prompt them.

language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping.

 The work begs the question of whether this “self-correction” could and should be built into language models from the beginning.The firm went public on Nasdaq in 2020.

Chinese media outlet 36Kr reported (in Chinese).the L (formerly known as Rela) is a social platform for lesbian and bisexual female users.

a Chinese tech firm that focuses on LGBTQ+ users.Matched users can chat privately.