artificial intelligence tagged posts

Computer chips that combine the use of light and electricity are shown to increase computational performance, while reducing energy consumption, compared with conventional electronic chips. The photonic computing chips, described in two papers in Nature this week, might address the growing computing demands driven by advancing artificial intelligence technology.

The growth in the complexity of artificial intelligence and deep learning models is pushing conventional electronic computing to its limits and has ever-increasing energy demands. Photonic computing, which uses photons rather than electrons, presents a potential solution to these challenges.

Multiplication and accumulation—central computational operations for artificial intelligence—can be performed faster and more effic...

Artificial intelligence (AI) makes important decisions that affect our everyday lives. These decisions are implemented by firms and institutions in the name of efficiency. They can help determine who gets into college, who lands a job, who receives medical treatment and who qualifies for government assistance.

As AI takes on these roles, there is a growing risk of unfair decisions—or the perception of them by those people affected. For example, in college admissions or hiring, these automated decisions can unintentionally favor certain groups of people or those with certain backgrounds, while equally qualified but underrepresented applicants get overlooked.

Or, when used by governments in benefit systems, AI may allocate resources in ways that w...

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...