News from the AI & ML world
DeeperML - #mit
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Zach Winn@news.mit.edu
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MIT spinout Themis AI is tackling a critical issue in the field of artificial intelligence: AI "hallucinations" or instances where AI systems confidently provide incorrect or fabricated information. These inaccuracies can have serious consequences, particularly in high-stakes applications like drug development, autonomous driving, and information synthesis. Themis AI has developed a novel tool called Capsa, designed to quantify model uncertainty and enable AI models to recognize their limitations. Capsa works by modifying AI models to identify patterns in their data processing that indicate ambiguity, incompleteness, or bias. This allows the AI to "admit when it doesn't know," thereby improving the reliability and transparency of AI systems.
The core idea behind Themis AI's Capsa platform is to wrap existing AI models, identify uncertainties and potential failure modes, and then enhance the model's capabilities. Founded in 2021 by MIT Professor Daniela Rus, Alexander Amini, and Elaheh Ahmadi, Themis AI aims to enable safer and more trustworthy AI deployments across various industries. Capsa can be integrated with any machine-learning model to detect and correct unreliable outputs in seconds. The platform has already demonstrated its value in diverse sectors, including helping telecom companies with network planning, assisting oil and gas firms in analyzing seismic imagery, and contributing to the development of more reliable chatbots. Themis AI’s work builds upon years of research at MIT into model uncertainty. Professor Rus's lab, with funding from Toyota, studied the reliability of AI for autonomous driving, a safety-critical application where accurate model understanding is paramount. The team also developed an algorithm capable of detecting and mitigating racial and gender bias in facial recognition systems. Amini emphasizes that Themis AI's software adds a crucial layer of self-awareness that has been missing in AI systems. The goal is to enable AI to forecast and predict its own failures before they occur, ensuring that these systems are used responsibly and effectively in critical decision-making processes.
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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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