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…
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…
Ductal carcinoma in situ (DCIS), a type of tumor that can develop into an aggressive form of breast cancer, accounts for approximately 25% of all breast cancer diagnoses. DCIS can be challenging for clinicians to accurately categorize, leading to frequent overtreatment for patients. A team of researchers from the Massachusetts Institute of Technology (MIT) and…
Methods for evaluating the dependability of a multi-functional AI model prior to its implementation.
Foundation models, or large-scale deep-learning models, are becoming increasingly prevalent, particularly in powering prominent AI services such as DALL-E, or ChatGPT. These models are trained on huge quantities of general-purpose, unlabeled data, which is then repurposed for various uses, such as image generation or customer service tasks. However, the complex nature of these AI tools…
MIT researchers have developed a technique for improving the accuracy of uncertainty estimates in machine-learning models. This is especially important in situations where these models are used for critical tasks such as diagnosing diseases from medical imaging or filtering job applications. The new method works more efficiently and is scalable enough to apply to large…
Generative AI, renowned for its capability to autonomously produce text and images, plays a crucial role in creating realistic synthetic data from diverse scenarios, helping organizations optimize operations. A notable initiative in the field is the Synthetic Data Vault (SDV), developed by DataCebo, an MIT spinoff. This generative system aids organizations in creating synthetic data…
Generative AI has vast potential in creating synthetic data that can mimic real-world scenarios, which in turn can aid organizations in improving their operations. In line with this, DataCebo, a spinout from MIT, has developed a generative software system referred to as the Synthetic Data Vault (SDV), which has been employed by thousands of data…
Generative AI, which can create text and images, is becoming an essential tool in today's data-driven society. It's now being utilized to produce realistic synthetic data, which can effectively solve problems where real data is limited or sensitive. For the past three years, DataCebo, an MIT spinoff, has been offering a Synthetic Data Vault (SDV)…
Generative AI, which can create text and images, also has extensive potential in creating realistic synthetic data for various applications. Being able to produce synthetic data can assist organizations, particularly in situations where real-world data is lacking or sensitive. For instance, it can help in patient care, rerouting of flights due to adverse weather, or…
Generative AI has the capability to produce realistic synthetic data that could help organizations in various sectors such as healthcare, aviation, and software development efficiently carry out operations. For the last three years, MIT spinout DataCebo has been offering the Synthetic Data Vault (SDV), a generative software system that can design synthetic data, useful in…