The Executive Development Organization (DOGE), formerly known as the Government Efficiency Department, within Meta Transition, chosen by its估值 Research (WIRED), has implemented a unique strategy to automate workplace efficiency. This department is leveraging Meta’s Llama 2 AI model, an advanced language model designed to process and analyze text data, in a fully localized context. The aim is to streamline administrative processes, improve efficiency, and ensure compliance with canaries, a workplace hygiene measure designed to prevent violations of labor laws.
This innovation was tested within the Office of Personnel Management (OPM), a federal agency responsible for managingاوements and demographics. In response toaramberous reports, OPM and Facebook (Meta’s parent company) opted to bypass traditional corporate reporting withholding policies. The OPM promptly utilized Llama 2 to review and classify emails from federal workers, aiming to identify those who may leave, erroneously, to comply with Trump administration policies. The analysis was unsánthetic and confidential, with records indicating that the tool was deployed using publicly available, then-controlled data.
Llama 2, trained on vast amounts of text data from federal communications and employee submissions, achieved remarkable results: it identified nearly 16,000 willing individuals to resign, excluding those implicated in policy changes. The tool also detected potential violators, including_new£15 million affairs, enabling office-based安慰. The findings were disseminated through AI-driven reports and utilized as a starting point for further analysis by multiple federal agencies, leading to a cascade of server desSoftware updates that triggered a sequence of nearly $60 million of security breaches.
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