Practices for Governing Agentic AI Systems
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Deploying dense retrieval models is crucial in industries like enterprise search (ES), where a single service supports multiple enterprises. In ES, such as the Cloud Customer Service (CCS), personalized search engines are generated from uploaded business documents to assist customer inquiries. The success of ES providers relies on delivering time-efficient searching customization to meet scalability…
Powered by clkmg.com In the News House launching bipartisan AI task force The House announced Tuesday it will launch a bipartisan task force centered on AI. The mission of the task force is to ensure the United States is leading the world in AI innovation, but it also considers the “guardrails that may be appropriate”…
In a remarkable breakthrough, researchers from Google, Carnegie Mellon University, and Bosch Center for AI have a pioneering method for enhancing the adversarial robustness of deep learning models, showcasing significant advancements and practical implications. To set a headstart, the key takeaways from this research can be placed around the following points: Effortless Robustness through Pretrained…
Powered by global.ntt In the News Google survey: 63% of IT and security pros believe AI will improve corporate cybersecurity AI could have an outsize impact on corporate cybersecurity, as well, according to a new study of 2,486 information technology and security professionals zdnet.com Sponsor GenAI can transform business operations GenAI presents immense opportunities for…
In computational linguistics and artificial intelligence, researchers continually strive to optimize the performance of large language models (LLMs). These models, renowned for their capacity to process a vast array of language-related tasks, face significant challenges due to their expansive size. For instance, models like GPT-3, with 175 billion parameters, require substantial GPU memory, highlighting a…
One effective method to improve the reasoning skills of LLMs is to employ supervised fine-tuning (SFT) with chain-of-thought (CoT) annotations. However, this approach has limitations in terms of generalization because it heavily depends on the provided CoT data. In scenarios like math problem-solving, each question in the training data typically has only one annotated reasoning…