AI tagged posts

A team of roboticists at the Beijing Institute of Technology, working with a pair of colleagues from the Technical University of Munich, has created a new kind of rat robot—one that was designed to interact in social ways with real rats.

In their paper published in the journal Nature Machine Intelligence, the group describes how they used artificial intelligence to train their robot rat to behave like a real rat...

During your first driving class, the instructor probably sat next to you, offering immediate advice on every turn, stop and minor adjustment. If it was a parent, they might have even grabbed the wheel a few times and shouted “Brake!” Over time, those corrections and insights developed experience and intuition, turning you into an independent, capable driver.

Although advancements in artificial intelligence (AI) have made self-driving cars a reality, the teaching methods used to train them remain a far cry from even the most nervous side-seat driver. Rather than nuance and real-time instruction, AI learns primarily through massive datasets and extensive simulations, regardless of the application.

Now, researchers from Duke University and the Army Research Laboratory have develope...

Modern imaging systems, such as those used in smartphones, virtual reality (VR), and augmented reality (AR) devices, are constantly evolving to become more compact, efficient, and high-performing. Traditional optical systems rely on bulky glass lenses, which have limitations like chromatic aberrations, low efficiency at multiple wavelengths, and large physical sizes. These drawbacks present challenges when designing smaller, lighter systems that still produce high-quality images.

To overcome these issues, rese...

Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.

With this challenge in mind, a team from CSAIL has taken the lead in devising a framework that enables a more nuanced approach to balancing these qualities.

Instead of forcing a binary decision in labeling all data points as “good” or “bad,” their framework uses their Reject Option Classification (ROC) algorithm which assigns a third category of “rejected samples,” allowing it to identify instances where the model might be less certain or where predictions could potentially le...