According to @AnthropicAI, Selective Gradient Masking (SGTM) splits model weights into retain and forget subsets during pretraining and directs specified knowledge into the forget subset, according to Anthropic's alignment site. The forget subset can then be removed prior to release to limit hazardous capabilities in high-risk settings, according to Anthropic's alignment article. The announcement does not reference cryptocurrencies or tokenized AI projects and does not state any market or pricing impact, according to Anthropic's post.

According to @AnthropicAI, a classifier setup reduced CBRN accuracy by 33% beyond a random baseline; source: @AnthropicAI. The source also reports no particular effect on a range of other benign tasks, addressing concerns that filtering CBRN data would harm harmless scientific capabilities; source: @AnthropicAI.

According to Vitalik Buterin, the correlation greater than zero is necessary in crypto markets, emphasizing that competence and alignment are separate factors. This insight is crucial for traders focusing on strategic alignment and competence in their trading activities.

According to Vitalik Buterin, there is a need for a positive correlation in blockchain operations rather than sole reliance on one factor. He mentions that competence and alignment are separate axes in blockchain technology and emphasizes that failing in alignment could render competence ineffective, potentially impacting trading strategies and market stability (source: Twitter).