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

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 19, 202434Views0Likes0Comments
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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An improved and quicker method to guard against an AI chatbot providing harmful responses.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 18, 202434Views0Likes0Comments
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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An improved, quicker method to inhibit an AI chatbot from providing harmful responses.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 18, 202434Views0Likes0Comments
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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Developing and validating robust systems controlled by artificial intelligence in a systematic and adaptable manner.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Drones, Electrical Engineering & Computer Science (eecs), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Networks, Research, Robotics, Robots, Safety, School of Engineering, UncategorizedJuly 18, 202446Views0Likes0Comments
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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Developing and confirming robust AI-operated systems using thorough and adaptable methods.

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Drones, Electrical Engineering & Computer Science (eecs), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Networks, Research, Robotics, Robots, Safety, School of Engineering, UncategorizedJuly 18, 202440Views0Likes0Comments
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.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 17, 202432Views0Likes0Comments
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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An improved, quicker method to restrict an AI chatbot from delivering harmful replies.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 17, 202437Views0Likes0Comments

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An improved and quicker method to stop an AI chatbot from providing harmful reactions.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 17, 202434Views0Likes0Comments
Artificial intelligence (AI) advancements have led to the creation of large language models, like those used in AI chatbots. These models learn and generate responses through exposure to substantial data inputs, opening the potential for unsafe or undesirable outputs. One current solution is "red-teaming" where human testers generate potentially toxic prompts to train chatbots to…

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Methods for evaluating the dependability of a multi-functional AI model prior to its implementation.

Algorithms, Artificial Intelligence, Computer science and technology, Data, Human-computer interaction, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedJuly 16, 202434Views0Likes0Comments
Foundation models, or large-scale deep-learning models, are becoming increasingly prevalent, particularly in powering prominent AI services such as DALL-E, or ChatGPT. These models are trained on huge quantities of general-purpose, unlabeled data, which is then repurposed for various uses, such as image generation or customer service tasks. However, the complex nature of these AI tools…

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The cognitive abilities of extensive linguistic models are frequently exaggerated.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Language, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Quest for Intelligence, Research, School of Engineering, UncategorizedJuly 12, 202446Views0Likes0Comments
Artificial intelligence (AI) and particularly large language models (LLMs) are not as robust at performing tasks in unfamiliar scenarios as they are positioned to be, according to a study by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The researchers focused on the performance of models like GPT-4 and Claude when handling “default tasks,”…

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“They have the ability to envision influencing the world they reside in.”

academics, Algorithms, Analytics, Apps, Artificial Intelligence, astronomy and planetary science, Climate change, Collaboration, Computer science and technology, Data, Digital technology, Education, Environment, Global, Human-computer interaction, Internet, K-12 education, Machine learning, Media Lab, MIT Schwarzman College of Computing, nasa, Office of Open Learning, Open access, Programming, Social Media, Space, Special events and guest speakers, STEM education, teaching, Technology and society, UncategorizedJuly 9, 202450Views0Likes0Comments
A group of New England Innovation Academy students have developed a mobile app that highlights deforestation trends in Massachusetts as part of a project for the Day of AI, a curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative. The TreeSavers app aims to educate users about the effects of…

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Researchers from MIT present a generative artificial intelligence for databases.

Algorithms, Artificial Intelligence, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Data, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, Programming, Programming languages, Research, School of Engineering, School of Science, UncategorizedJuly 8, 202432Views0Likes0Comments
GenSQL, a new AI tool developed by scientists at MIT, is designed to simplify the complex statistical analysis of tabular data, enabling users to readily understand and interpret their databases. To this end, users don't need to grasp what is happening behind the scenes to develop accurate insights. The system's capabilities include making predictions, identifying anomalies,…

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