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Source channel @githubtrending · Post #15239 · Oct 21

#python#artificial_intelligence#cloud_ml#computer_systems#courseware#deep_learning#edge_machine_learning#embedded_ml#machine_learning#machine_learning_systems#mobile_ml#textbook#tinyml You can learn how to build real-world AI systems from start to finish with an open-source textbook originally from Harvard University. It teaches you not just how to train AI models but how to design scalable systems, manage data pipelines, deploy models in production, monitor them continuously, and optimize for devices like phones or IoT gadgets. This helps you become an engineer who can create efficient, reliable, and sustainable AI systems that work well in practice. The book offers hands-on labs, community support, and free online access, making it easier to gain practical skills in machine learning systems engineering. https://github.com/harvard-edge/cs249r_book

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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