When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model’s predictions.
But machine-learning models are so large and complex that …
Words, data, and algorithms combine,
An article about LLMs, so divine.
A glimpse into a linguistic world,
Where language machines are unfurled.
It was a natural inclination to task a large language model (LLM) like CHATGPT with creating a poem …
In an effort to improve fairness or reduce backlogs, machine-learning models are sometimes designed to mimic human decision making, such as deciding whether social media posts violate toxic content policies.
But researchers from MIT and elsewhere have found that these …
The emergence of generative artificial intelligence has ignited a deep philosophical exploration into the nature of consciousness, creativity, and authorship. As we bear witness to new advances in the field, it’s increasingly apparent that these synthetic agents possess a remarkable …
Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans …
The MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda Pharmaceuticals Company, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning …
Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. …
It’s a dilemma as old as time. Friday night has rolled around, and you’re trying to pick a restaurant for dinner. Should you visit your most beloved watering hole or try a new establishment, in the hopes of discovering something …