Skip to content Skip to sidebar Skip to footer

MIT Schwarzman College of Computing

A novel method enables AI chatbots to engage in conversations throughout the day without any interruptions.

A team of researchers from MIT and other institutions has discovered a key issue with large-scale machine learning models causing chatbot performance to degrade. When engaged in extensive dialogues, the huge language models behind bots like ChatGPT sometimes begin to fail. However, the team devised a solution enabling nonstop conversation without deterioration or lag. The…

Read More

An innovative approach allows AI chatbots to engage in conversation throughout the day without experiencing any failure.

When engaging in lengthy dialogues, advanced AI-powered chatbots often become inept, resulting in a significant performance downturn. A team of researchers from MIT alongside others have deduced a reason for this issue and devised a straightforward solution to prevent the bot from crashing or slowing down. The method, StreamingLLM, effectively ensures a continuous discussion irrespective…

Read More

An innovative method allows AI chatbots to engage in conversation all day without experiencing any system failures.

A team of researchers from MIT and other institutions has discovered a remarkable cause of performance deterioration in chatbots and found a simple solution that allows persistent, uninterrupted dialogue. This problem occurs when human-AI interaction involves continuous rounds of conversation, which can overburden the large language machine-learning models that power chatbots like ChatGPT. The researchers have…

Read More

A novel approach has been developed to allow AI chatbots to engage in conversation throughout the entire day without collapsing.

Researchers from MIT and other institutions have developed a method that prevents large AI language machines from crashing during lengthy dialogues. The solution, known as StreamingLLM, tweaks the key-value cache (a sort of conversation memory) of large language models to ensure the first few data pieces remain in memory. Typically, once the cache's capacity is…

Read More

A novel method enables AI chatbots to communicate continuously without failure.

Researchers from MIT and other institutions have found a solution to an issue that causes machine-learning model-run chatbots to malfunction during long, continuous dialogues. They found that significant delays or crashes happen when the key-value cache, essentially the conversation memory, becomes overloaded leading to early data being ejected and the model to fail. The researchers…

Read More

Scientists employ generative artificial intelligence to tackle intricate queries in the field of physics.

Researchers from MIT and the University of Basel in Switzerland have developed a new machine-learning framework that can map phase diagrams for novel physical systems automatically. By applying generative artificial intelligence models, the team has developed a more efficient method for tracking and understanding phase transitions in water and other complex physical systems, which offers…

Read More

An innovative method enables AI chatbots to engage in conversations all day without experiencing errors or shutdowns.

A team of researchers from MIT, Meta AI, Carnegie Mellon University, and NVIDIA, have found a solution to the problem of the performance degradation of AI chatbots during extended human-AI conversations. They identified a challenge associated with AI conversation memory, known as the key-value cache, where data is bumped out when the cache exceeds its…

Read More

Improving the dependability of language models by leveraging concepts from game theory.

Researchers from MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have designed a new type of game to enhance how artificial intelligence (AI) comprehends and produces text. This "consensus game" includes two parts of an AI system - the part that generates sentences and a part that evaluates those sentences. This model significantly improved the…

Read More

The potential of App Inventor: Making mobile application creation accessible to everyone.

In June 2007, Apple introduced the first iPhone, featuring an App Store exclusively for approved applications. This decision, however, excluded educators from incorporating burgeoning mobile app development into education. Simultaneously, Hal Abelson, an MIT professor on sabbatical at Google, was contemplating Google's response to Apple's grip on the mobile software market. Both Abelson and Google…

Read More