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Source channel @githubtrending · Post #15518 · Feb 24

#rust#ai#ai_ocr#attention_mechanism#gnn#gnn_model#gnns#graph#graph_neural_networks#llm_inference#low_latency#mincut#neo4j#ocr#onnx#rust#vector#wasm RuVector is a free, open-source vector database that gets smarter with every query. Unlike static databases, it learns from usage via GNN layers, runs LLMs locally with no cloud costs, supports graph queries like Neo4j, scales freely across nodes, and deploys as a single self-booting file (125ms startup). Run with `npx ruvector`. You benefit from faster, more accurate AI search that improves automatically, zero operating costs, full offline/privacy control, and easy scaling—perfect for RAG, agents, or edge apps without vendor lock-in. https://github.com/ruvnet/ruvector

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AI & Law

@ai_and_law · Post #286 · 04/16/2024, 07:04 AM

US Federal Agencies Issue Joint Statement on Automated Systems On April 3, 2024, several US federal agencies released a joint statement on the Enforcement of Civil Rights, Fair Competition, Consumer Protection, and Equal Opportunity Laws in Automated Systems. Signatories include leaders from the EEOC, Consumer Financial Protection Bureau, Department of Justice, Federal Trade Commission, Department of Education, Department of Health and Human Services, Department of Homeland Security, Department of Housing and Urban Development, and Department of Labor. The statement underscores the commitment of federal agencies to enforce legal protections applicable to automated systems, defined as software and algorithmic processes, including AI, used to automate workflows and decision-making. Emphasizing the dual objectives of monitoring automated tools' evolution and fostering responsible innovation, agencies reaffirm the relevance of existing laws to automated systems. They stress their role in ensuring compliance with these laws during system development. This follows previous efforts by the EEOC and other agencies to address discrimination and bias in automated systems. The expanded participation in this year's statement reflects the government's heightened focus on regulating automated systems and enforcing relevant laws. The statement emphasizes that AI and automated systems fall under existing laws, dispelling the notion that their "black box" nature exempts them from compliance. It identifies potential sources of unlawful discrimination or bias, including skewed training data, lack of transparency, and inadequate consideration of social context during design and use. Compliance with existing and AI-specific laws is paramount for fostering trust and safe AI innovation, as highlighted in the joint statement. #automatedsystems#AI#AIcompliance