News from the AI & ML world
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@techstrong.ai
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Amazon is making a substantial investment in artificial intelligence infrastructure, announcing plans to spend $10 billion in North Carolina. The investment will be used to build a cloud computing and AI campus just east of Charlotte, NC. This project is anticipated to create hundreds of good-paying jobs and provide a significant economic boost to Richmond County, positioning North Carolina as a hub for cutting-edge technology.
This investment underscores Amazon's commitment to driving innovation and advancing the future of cloud computing and AI technologies. The company plans to expand its AI data center infrastructure in North Carolina, following a trend among Big Tech companies who are building out infrastructure to meet escalating AI resource requirements. The new "innovation campus" will house data centers containing servers, storage drives, networking equipment, and other essential technology. Amazon is also focused on improving efficiency by enhancing warehouse operations through the use of AI. The company unveiled AI upgrades to boost warehouse efficiency. These upgrades center around the development of "agentic AI" robots. These robots are designed to perform a variety of tasks, from unloading trailers to retrieving repair parts and lifting heavy objects, all based on natural language instructions. The goal is to create systems that can understand and act on commands, transforming robots into multi-talented helpers, ultimately leading to faster deliveries and improved efficiency.
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@www.marktechpost.com
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MIT researchers are making significant strides in artificial intelligence, focusing on enhancing AI's ability to learn and interact with the world more naturally. One project involves developing AI models that can learn connections between vision and sound without human intervention. This innovative approach aims to mimic how humans learn, by associating what they see with what they hear. The model could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval.
The new machine-learning model can pinpoint exactly where a particular sound occurs in a video clip, eliminating the need for manual labeling. By adjusting how the original model is trained, it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The enhancements improved the model’s ability to retrieve videos based on an audio query and predict the class of an audio-visual scene, like the sound of a roller coaster in action or an airplane taking flight. Researchers also made architectural tweaks that help the system balance two distinct learning objectives, which improves performance. Additionally, researchers from the National University of Singapore have introduced 'Thinkless,' an adaptive framework designed to reduce unnecessary reasoning in language models. Thinkless reduces unnecessary reasoning by up to 90% using DeGRPO. By incorporating a novel algorithm called Decoupled Group Relative Policy Optimization (DeGRPO), Thinkless separates the training focus between selecting the reasoning mode and improving the accuracy of the generated response. This framework equips a language model with the ability to dynamically decide between using short or long-form reasoning, addressing the issue of resource-intensive and wasteful reasoning sequences for simple queries.
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