In today’s world, autonomous machines play a major role in our lives, yet it is still difficult to establish trust between humans and machines. Aside from concerns about unexpected disruptions, robots do not yet communicate exactly the way humans do. Researchers have revealed that independent computer systems are able to increase trust in robots, increase collaboration, and streamline task execution.

Humans tend to rely more on robots that provide self-assessment while performing their tasks, according to the study. Communication is essential for establishing trust in a human working environment. Humans and autonomous machines may have a gap of understanding, which can result in a robot performing an action incorrectly, and even misuse or exploitation of the robot’s capabilities.

In a study conducted by researchers from Draper and the University of Colorado Boulder in the USA, researchers examined how autonomous robots using probability models are capable of calculating and expressing self-assessment skills, forming a kind of machine-self-confidence. The models were developed to predict their behavior and provide a perspective on their mission before the event occurs.

Scientists identified and observed cells in the human brain that act as filing systems for everything we observe. Could we use this for AI? property= description.

The multi-use rover can autonomously swap payloads, mobilize astronauts, and more.

A Microsoft Research team has introduced a “simple yet effective” method that dramatically improves stability in transformer models with just a few lines of code change.

Large-scale transformers have achieved state-of-the-art performance on a wide range of natural language processing (NLP) tasks, and in recent years have also demonstrated their impressive few-shot and zero-shot learning capabilities, making them a popular architectural choice for machine learning researchers. However, despite soaring parameter counts that now reach billions and even trillions, the layer depth of transformers remains restricted by problems with training instability.

In their new paper DeepNet: Scaling Transformers to 1,000 Layers, the Microsoft team proposes DeepNorm, a novel normalization function that improves the stability of transformers to enable scaling that is an order of magnitude deeper (more than 1,000 layers) than previous deep transformers.