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Computer science and technology

An improved, quicker method to stop an AI chatbot from providing harmful replies.

Artificial intelligence (AI) chatbots like ChatGPT, capable of generating computer code, summarizing articles, and potentially even providing instructions for dangerous or illegal activities, pose unique safety challenges. To mitigate this risk, companies use a safeguarding process known as red-teaming, where human testers attempt to prompt inappropriate or unsafe responses from AI models. This process is…

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A fresh artificial intelligence approach identifies ambiguity in medical imaging.

Artificial Intelligence (AI) models are increasingly being employed in the field of biomedicine to assist clinicians with image segmentation, a process that annotates pixels from important structures in a medical image, such as an organ or cell. However, these AI models often offer a singular answer, while image segmentation in the medical field is usually…

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A quicker and more effective method to stop an AI chatbot from providing harmful replies.

Companies that build large language models, like those used in AI chatbots, routinely safeguard their systems using a process known as red-teaming. This involves human testers generating prompts designed to trigger unsafe or toxic responses from the bot, thus enabling creators to understand potential weaknesses and vulnerabilities. Despite the merits of this procedure, it often…

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A novel artificial intelligence approach has been developed to accurately determine the ambiguity in medical imaging.

In the field of biomedicine, segmentation plays a crucial role in identifying and highlighting essential structures in medical images, such as organs or cells. In recent times, artificial intelligence (AI) models have shown promise in aiding clinicians by identifying pixels that may indicate disease or anomalies. However, there is a consensus that this method is…

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An improved and quicker method to guard against an AI chatbot providing harmful responses.

Artificial Intelligence chatbots have the capacity to construct helpful code, summarize articles, and even create more hazardous content. To prevent safety violations like these, companies employed a procedure known as "red-teaming" in which human testers crafted prompts intended to elicit unsafe responses from chatbots, which were then taught to avoid these inputs. However, this required…

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A novel artificial intelligence technique successfully identifies ambiguity in medical imaging.

In biomedicine, the process of segmentation involves marking significant structures in a medical image, such as cells or organs. This can aid in the detection and treatment of diseases visible in these images. Despite this promise, current artificial intelligence (AI) systems used for medical image segmentation only offer a single segmentation result. This approach isn't…

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An improved, quicker method to inhibit an AI chatbot from providing harmful responses.

Researchers from the Improbable AI Lab at MIT and the MIT-IBM Watson AI Lab have developed a new technique to improve "red-teaming," a process of safeguarding large language models, such as AI chatbot, through the use of machine learning. The new approach focuses on the automatic generation of diverse prompts that result in undesirable responses…

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A novel approach in AI successfully identifies ambiguity in medical imaging.

Researchers from MIT, in collaboration with the Broad Institute of MIT and Harvard and Massachusetts General Hospital, have introduced a new artificial intelligence (AI) tool known as Tyche, which can provide multiple, plausible image segmentation possibilities for a given medical image. Unlike conventional AI tools, which typically offer a single definitive interpretation, Tyche generates a…

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Developing and validating robust systems controlled by artificial intelligence in a systematic and adaptable manner.

Neural networks have been of immense benefit in the design of robot controllers, boosting the adaptive and effectiveness abilities of these machines. However, their complex nature makes it challenging to confirm their safe execution of assigned tasks. Traditionally, the verification of safety and stability are done using Lyapunov functions. If a Lyapunov function that consistently…

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A novel artificial intelligence approach has been developed to recognize ambiguity in medical imaging.

Biomedicine often requires the annotation of pixels in a medical image to identify critical structures such as organs or cells, a process known as segmentation. In this context, artificial intelligence (AI) models can be useful to clinicians by highlighting pixels indicating potential disease or anomalies. However, decision-making in medical image segmentation is frequently complex, with…

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Developing and confirming robust AI-operated systems using thorough and adaptable methods.

Researchers from the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an algorithm to mitigate the risks associated with using neural networks in robots. The complexity of neural network applications, while offering greater capability, also makes them unpredictable. Current safety and stability verification techniques, called Lyapunov functions, do not…

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An improved, more efficient method to prohibit an AI chatbot from producing harmful responses.

Researchers from Improbable AI Lab at MIT and the MIT-IBM Watson AI Lab have developed a technique to enhance the safety measures implemented in AI chatbots to prevent them from providing toxic or dangerous information. They have improved the process of red-teaming, where human testers trigger unsafe or dangerous context to teach AI chatbot to…

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