Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
The customer, an automotive manufacturer, needed an automation solution for the palletization and depalletization of large ABS parts. They decided on a solution where one vision-guided robot operates both processes.
In this episode, we talk all about connectomics - the study of animal brains - and how researchers at MIT have started leveraging AI to break through the primary bottleneck: brain image acquisition.
Unsupervised learning is a machine learning approach that finds patterns, structure, or useful representations in unlabeled data. It groups similar observations, compresses high-dimensional features, detects outliers, discovers item relationships, and supports modern representation learning.
Understanding industrial vision systems by examining their components, imaging fundamentals, AI integration since 2020, and how to choose the right solution for every application.
Explore the rise of intelligent EV hardware and how real-time processing, ML acceleration, and hardware virtualization are enabling safer, smarter, software-defined electric vehicles.
Modern robots know how to sense their environment and respond to language, but what they don’t know is often more important than what they do know. Teaching robots to ask for help is key to making them safer and more efficient.
The customer, an automotive manufacturer, needed an automation solution for the palletization and depalletization of large ABS parts. They decided on a solution where one vision-guided robot operates both processes.
In this episode, we talk all about connectomics - the study of animal brains - and how researchers at MIT have started leveraging AI to break through the primary bottleneck: brain image acquisition.
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
The butterfly effect—the idea that a butterfly flapping its wings in South America could change the course of a tornado in Texas—has been a popular way to illustrate the difficulty of making predictions about the behavior of chaotic systems like the weather. Because such systems are extremely sensitive to their initial conditions, even the tiniest of changes can send their behavior careening off into unexpected directions.
Training robust AI models can be challenging when using crowdsourced or publicly available datasets. The most useful neural networks are ones that replace expert knowledge to solve real problems. One example would be a visual inspection model for the factory floor.