artificial intelligence tagged posts

Increasingly complex applications such as artificial intelligence require ever more powerful and power-hungry computers to run. Optical computing is a proposed solution to increase speed and power efficiency but has yet to be realized due to constraints and drawbacks. A new design architecture, called diffraction casting, seeks to address these shortcomings. It introduces some concepts to the field of optical computing that might make it more appealing for implementation in next-generation computing devices.

Whether it’s the smartphone in y...

The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.

Training data for robot control and planning algorithms, on the other hand, remains far less abundant, in part because acquiring it is not as straightforward...

With the rapid advancement of artificial intelligence, unmanned systems such as autonomous driving and embodied intelligence are continuously being promoted and applied in real-world scenarios, leading to a new wave of technological revolution and industrial transformation. Visual perception, a core means of information acquisition, plays a crucial role in these intelligent systems. However, achieving efficient, precise, and robust visual perception in dynamic, diverse, and unpredictable environments remains an open challenge.

In open-world scenarios, intelligent systems must not only process vast amounts of data but also handle various extreme events, such as sudden dangers, drastic light changes at tunnel entrances, and strong flash interference at night in driving scenarios.

Many artificial intelligence (AI) systems have already learned how to deceive humans, even systems that have been trained to be helpful and honest. In a review article published in the journal Patterns on May 10, researchers describe the risks of deception by AI systems and call for governments to develop strong regulations to address this issue as soon as possible.

“AI developers do not have a confident understanding of what causes undesirable AI behaviors like deception,” says first author Peter S. Park, an AI existential safety postdoctoral fellow at MIT. “But generally speaking, we think AI deception arises because a deception-based strategy turned out to be the best way to perform well at the given AI’s training task. Deception helps them achieve their goals.”

Park and coll...