MIT's Tech Square has played a significant role in the evolution of computing, hosting key figures and research from time-shared computing to the World Wide Web.
A new MIT study shows that both humans and animals continue to explore different approaches to a task even after learning the optimal strategy, due to potential benefits of discovering new, better alternatives or adapting to changes in the environment.
A study by MIT suggests that humans and animals have a built-in tendency to continuously tweak their methods, driven by the potential for discovering superior strategies and adapting to unforeseen changes.
The article from Earth.com discusses a study revealing that both humans and animals have an inherent tendency to experiment and explore, even after mastering a task. Conducted by researchers at MIT, the study suggests that this behavior serves two main purposes: adapting to potential changes in task rules and discovering potentially better solutions. The study involved humans and marmosets performing a task that required them to react when an image disappeared. Despite learning optimal strategies, participants continued to alter their responses based on past experiences, indicating an exploratory approach to improve their internal model of the environment. This behavior has implications for understanding learning processes and could provide insights into autism spectrum disorders, as marmosets are increasingly used in related research. The full study was published in the journal Current Biology.
Quotes:
> First, he says, simply because a task's rules seem set one moment doesn't mean they'll stay that way in this uncertain world, so altering behavior from the optimal condition every so often could help reveal necessary adjustments.
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>Second, and of equal importance, continuous exploration could also offer a chance to discover something superior to our current best.
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>"If the goal is to maximize reward, you should never deviate once you have found the perfect solution, yet you keep exploring. Why? It's like food. We all like certain foods, but we still keep trying different foods because you never know, there might be something you could discover," noted the researchers.
The article from Earth.com discusses a study revealing that both humans and animals have an inherent tendency to experiment and explore, even after mastering a task. Conducted by researchers at MIT, the study suggests that this behavior serves two main purposes: adapting to potential changes in task rules and discovering potentially better solutions. The study involved humans and marmosets performing a task that required them to react when an image disappeared. Despite learning optimal strategies, participants continued to alter their responses based on past experiences, indicating an exploratory approach to improve their internal model of the environment. This behavior has implications for understanding learning processes and could provide insights into autism spectrum disorders, as marmosets are increasingly used in related research. The full study was published in the journal Current Biology.
Quotes:
> First, he says, simply because a task's rules seem set one moment doesn't mean they'll stay that way in this uncertain world, so altering behavior from the optimal condition every so often could help reveal necessary adjustments.
>
>Second, and of equal importance, continuous exploration could also offer a chance to discover something superior to our current best.
>
>"If the goal is to maximize reward, you should never deviate once you have found the perfect solution, yet you keep exploring. Why? It's like food. We all like certain foods, but we still keep trying different foods because you never know, there might be something you could discover," noted the researchers.
Dan Weinreb's thesis details the development of ZWEI, a real-time display-oriented editor for the Lisp Machine. It emphasizes ZWEI's design, implementation using Lisp, and integration with the Lisp environment. Key aspects include the use of buffer pointers (bps), intervals, and Lisp macros, as well as the impact of the Lisp Machine's architecture on the editor's functionality.
Researchers discovered long-lost computer code and used it to resurrect the early chatbot ELIZA from MIT. Named after Eliza Doolittle from 'Pygmalion,' ELIZA was developed in the 1960s by MIT professor Joseph Weizenbaum. It was designed to emulate a psychotherapist in conversation and used a unique programming language called MAD-SLIP. Rediscovered in 2021, the original code was brought back to life after 60 years, demonstrating the chatbot's functionality and highlighting the historical significance of early artificial intelligence.
The ELIZA chatbot, created in the 1960s by Joseph Weizenbaum at MIT, has been painstakingly reconstructed from archived records and run for the first time in over half a century. This effort marks a significant step in preserving one of the earliest examples of artificial intelligence. Despite its rudimentary nature compared to modern AI, ELIZA's resurrection highlights its historical importance.
The original 1965 chatbot restored on the world's first time-sharing system, ELIZA, created by Joseph Weizenbaum at MIT in 1964-6, is running again on a reconstructed version of MIT's CTSS, running on an emulated IBM 7094.
This article details the implementation of electronic mail and text messaging on the Compatible Time-Sharing System (CTSS) from 1965 to 1973, providing historical context and development insights.
MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
Henry Minsky, son of AI pioneer Marvin Minsky, co-founded Leela AI, an MIT-connected startup using novel visual intelligence to optimize manufacturing production lines through video analysis.
