0 bookmark(s) - Sort by: Date ↓ / Title / - Bookmarks from other users for this tag
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.
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.
The December 2024 newsletter from Obsolescence Guaranteed covers updates on new projects such as PiDP-10 and PiDP-1, news about existing projects like PiDP-8 and PiDP-11, and upcoming plans for 2025 including a PiDP-1 replica and an annual programming competition.
A team from MIT has developed an algorithm to identify causal links in complex systems by measuring interactions between variables over time.
The versatile algorithm identifies variables that likely influence others in complex systems. This method analyzes data collected over time to measure interactions between variables and estimate the impact of changes in one variable on another. It generates a "causality map" showing which variables are strongly linked.
The algorithm distinguishes between different types of causality:
The algorithm also estimates "causal leakage," indicating that some unknown influence is missing.
A new program from MIT helps children understand AI by letting them build small-scale language models.
First / Previous / Next / Last / Page 1 of 0