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In order to improve an AI assistant, initiate by imitating the unpredictable actions of humans.

Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedJuly 25, 202428Views0Likes0Comments
Researchers from MIT and the University of Washington have developed a method to model the behaviour of an agent, including its computational limitations, predicting future behaviours by examining prior actions. The method applies to both humans and AI, and has a wide range of potential applications, including predicting navigation goals from past routes and forecasting…

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To develop a superior AI assistant, begin by simulating the unpredictable actions of humans.

Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedJuly 24, 202434Views0Likes0Comments
In an effort to improve AI systems and their ability to collaborate with humans, scientists are trying to better understand human decision-making, including its suboptimal aspects, and model it in AI. A model for human or AI agent behaviour, developed by researchers at MIT and the University of Washington, takes into account an agent’s unknown…

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Research: The use of randomness in AI can enhance equity when distributing limited resources.

Algorithms, Artificial Intelligence, Computer science and technology, Data, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedJuly 24, 202438Views0Likes0Comments
Machine learning (ML) models are increasingly used by organizations to allocate scarce resources or opportunities, such as for job screening or determining priority for kidney transplant patients. To avoid bias in a model's predictions, users may adjust the data features or calibrate the model's scores to ensure fairness. However, researchers at MIT and Northeastern University…

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Researchers from MIT have made significant progress in enhancing the automatic understanding in AI models.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Language, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Research, School of Engineering, UncategorizedJuly 24, 202435Views0Likes0Comments
As AI models become increasingly integrated into various sectors, understanding how they function is crucial. By interpreting the mechanisms underlying these models, we can audit them for safety and biases, potentially deepening our understanding of intelligence. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have been working to automate this interpretation process, specifically…

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Even though we may anticipate large language models to operate similarly to humans, they do not.

Algorithms, Artificial Intelligence, Computer science and technology, Economics, Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine learning, MIT Schwarzman College of Computing, Research, School of Engineering, School of Humanities Arts and Social Sciences, UncategorizedJuly 23, 202432Views0Likes0Comments
Large language models (LLMs), such as GPT-3, are powerful tools due to their versatility. They can perform a wide range of tasks, ranging from helping draft emails to assisting in cancer diagnosis. However, their wide applicability makes them challenging to evaluate systematically, as it would be impossible to create a benchmark dataset to test a…

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An improved, speedier 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 22, 202438Views0Likes0Comments
While artificial intelligence (AI) chatbots like ChatGPT are capable of a variety of tasks, concerns have been raised about their potential to generate unsafe or inappropriate responses. To mitigate these risks, AI labs use a safeguarding method called "red-teaming". In this process, human testers aim to elicit undesirable responses from the AI, informing its development…

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An improved, quicker method to stop 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 22, 202440Views0Likes0Comments
Artificial Intelligence (AI) Chatbots like OpenAI's ChatGPT are capable of performing tasks from generating code to writing article summaries. However, they can also potentially provide information that could be harmful. To prevent this from happening, developers use a process called red-teaming, where human testers write prompts to identify unsafe responses in the model. Nevertheless, this…

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A more efficient and improved method to inhibit AI chatbots 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 21, 202436Views0Likes0Comments
AI chatbots like ChatGPT, trained on vast amounts of text from billions of websites, have a broad potential output which includes harmful or toxic material, or even leaking personal information. To maintain safety standards, large language models typically undergo a process known as red-teaming, where human testers use prompts to elicit and manage unsafe outputs.…

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An improved and speedier method to stop 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 21, 202436Views0Likes0Comments
AI chatbots pose unique safety risks—while they can write computer programs or provide useful summaries of articles, they can also potentially generate harmful or even illegal instructions, including how to build a bomb. To address such risks, companies typically use a process called red-teaming. Human testers aim to generate unsafe or toxic content from AI…

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A quicker, more efficient method to safeguard against an AI chatbot providing harmful or inappropriate 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 20, 202435Views0Likes0Comments
To counter unsafe responses from chatbots, companies often use a process called red-teaming, in which human testers write prompts designed to elicit such responses so the artificial intelligence (AI) can be trained to avoid them. However, since it is impossible for human testers to cover every potential toxic prompt, MIT researchers developed a technique utilizing…

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An improved, quicker method to avoid AI chatbots from delivering 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 20, 202436Views0Likes0Comments
Large language models powering AI chatbots possess the potential for generating harmful content due to their exposure to countless websites, putting users at risk if the AI generates illegal activities description, illicit instructions, or personal information leakage. To mitigate such threats, AI-developing companies use a procedure known as red-teaming, where human testers compose prompts aimed…

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An improved, quicker 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, 202437Views0Likes0Comments
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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