New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
Category: Algorithms
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Precision home robots learn with real-to-sim-to-real
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
Study: When allocating scarce resources with AI, randomization can improve fairness
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
An AI dataset carves new paths to tornado detection
TorNet, a public artificial intelligence dataset, could help models reveal when and why tornadoes form, improving forecasters’ ability to issue warnings.
3 Questions: Enhancing last-mile logistics with machine learning
MIT Center for Transportation and Logistics Director Matthias Winkenbach uses AI to make vehicle routing more efficient and adaptable for unexpected events.
AI generates high-quality images 30 times faster in a single step
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
How symmetry can come to the aid of machine learning
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
Q&A: A blueprint for sustainable innovation
Atacama Biomaterials, co-founded by Paloma Gonzalez-Rojas SM ’15, PhD ’21, combines architecture, machine learning, and chemical engineering to create eco-friendly materials.
New hope for early pancreatic cancer intervention via AI-based risk prediction
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
What to do about AI in health?
Although artificial intelligence in health has shown great promise, pressure is mounting for regulators around the world to act, as AI tools demonstrate potentially harmful outcomes.