Navigating new horizons: Pioneering AI framework enhances robot efficiency and planning
Summarized by: Liam Patel [techxplore.com]
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Researchers from Shanghai University have developed the “Correction and Planning with Memory Integration” (CPMI) framework, leveraging large language models (LLMs) to enhance robot efficiency in complex tasks. Traditionally dependent on explicit programming, robots often struggled with unexpected challenges. The CPMI framework integrates memory and planning capabilities, allowing robots to adapt and learn from experiences in real-time. This approach enables robots to break down complex instructions, plan effectively, and correct their actions in response to obstacles. Tested in the ALFRED simulation environment, CPMI outperformed existing models, demonstrating higher success rates and improved task adaptability. The framework’s memory module mimics human memory, enhancing efficiency and adaptability over time. Potential applications span from household assistance to industrial automation, with ongoing refinements aimed at further enhancing memory capabilities and testing in diverse environments.
Paige launches Foundation Model service to support AI models
Summarized by: Aisha Kim [www.pathologyinpractice.com]
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Paige, a digital pathology and AI firm, has launched a new service line based on its Foundation Models, which include the world’s largest multi-modal AI model in pathology and oncology. This service allows AI developers, computational pathology product builders, and life sciences companies to license and use these models for research, clinical trials, and commercial purposes, adhering to high privacy, security, and clinical standards. The models, such as the Virchow and PRISM Foundation Models, are pre-trained and can be adapted to various tasks, reducing development time and resource requirements. Paige also offers dedicated AI support to accelerate R&D timelines, enhancing drug discovery and cancer treatment.
Singapore Publishes Generative AI Model Governance Framework
Summarized by: Liam Patel [www.privacyworld.blog]
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Singapore’s AI Verify Foundation and Infocomm Media Development Authority released a Model AI Governance Framework for Generative AI (GenAI Framework) on May 30, 2024. This framework, developed after public consultation, addresses accountability, data quality, trusted development, incident reporting, testing, security, content provenance, and R&D investment. It emphasizes the importance of responsible practices throughout the AI development chain, transparent data usage, and the need for industry best practices. While not mandatory, the framework encourages businesses to adapt its guidelines based on specific use cases and associated risks. Compliance with other applicable laws remains necessary.
OpenAI is restarting its robotics research group
Summarized by: Aisha Kim [www.therobotreport.com]
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OpenAI is resuming its robotics research group, applying generative AI to tasks such as manipulation. The San Francisco-based company, known for ChatGPT, had paused its robotics efforts in July 2021. The revival follows increased interest in generative AI and its applications in robotics, highlighted by the integration of large language models (LLMs) with physical robots. OpenAI is currently hiring for a research robotics engineer to train multimodal robotics models and improve core models. The company has also invested in humanoid developer Figure AI, signaling its renewed focus on robotics.
NIST Invites AI Developers to Submit Models for Risk Assessment Testing — AI: The Washington Report
Summarized by: Aisha Kim [www.mintz.com]
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The National Institute of Standards and Technology (NIST) has launched the Assessing Risks and Impacts of AI (ARIA) program, inviting AI developers to submit their models for risk assessment. The initiative aims to refine AI testing methodologies and provide feedback to enhance model safety and robustness. ARIA 0.1, the inaugural program, focuses on large language models and involves three layers of evaluation: general model testing, red teaming, and large-scale field testing. The program builds on NIST’s AI Risk Management Framework, developed to foster trustworthy AI systems. Interested developers can join the ARIA mailing list for more information.
2024 ESIF Economics and AI+ML Meeting | The Econometric Society
Summarized by: Liam Patel [www.econometricsociety.org]
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The 2024 ESIF Economics and AI+ML Meeting will take place on August 13-14, 2024, at Cornell University, Ithaca, NY. This interdisciplinary conference, hosted by Cornell’s Department of Computer Science and Economics, will feature plenary lectures by notable academics such as Susan Athey (Stanford), Avrim Blum (Toyota Technological Institute), and Michael I. Jordan (UC Berkeley). The event aims to explore the intersection of economics and AI/ML, fostering collaboration and innovation. Additionally, an AI Replication Game on August 12, 2024, will engage researchers in reproducing quantitative studies to ensure scientific rigor. Registration for non-presenters is open until July 15, 2024.
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The Staff
Editor: Elena Martinez
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