Lifeboat Foundation

The optimal choice of hypothesis is the weakest not the shortest.

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The processors of most computers work in series, performing one instruction at a time. This limits their ability to perform certain types of tasks in a reasonable period. An approach based on arrays of simultaneously interacting molecular switches could enable previously intractable computational problems to be solved.

Black holes are some of the most powerful objects in the universe — and humans could devise ways to harness that power as an energy source, a new theoretical study claims.

Researchers have experimentally demonstrated how to harness a property called negative capacitance for a new type of transistor that could reduce power consumption, validating a theory proposed in 2008 by a team at Purdue University.

The researchers used an extremely thin, or 2-D, layer of the semiconductor molybdenum disulfide to make a channel adjacent to a critical part of transistors called the gate. Then they used a “ferroelectric material” called hafnium zirconium oxide to create a key component in the newly designed gate called a negative capacitor.

Capacitance, or the storage of electrical charge, normally has a positive value. However, using the ferroelectric material in a transistor’s gate allows for negative capacitance, which could result in far lower power consumption to operate a transistor. Such an innovation could bring more efficient devices that run longer on a battery charge.

A computer chip processes and stores information using two different devices. If engineers could combine these devices into one or put them next to each other, then there would be more space on a chip, making it faster and more powerful.

Purdue University engineers have developed a way that the millions of tiny switches used to process information—called transistors—could also store that information as one device.

The method, detailed in a paper published in Nature Electronics, accomplishes this by solving another problem: combining a transistor with higher-performing memory technology than is used in most computers, called ferroelectric RAM.

One of the most actively debated questions about human and nonhuman culture is this: Under what circumstances might we expect culture, in particular the ability to learn from one another, to be favored by natural selection?

Researchers at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, have developed a simulation model of the evolution of social learning. They showed that the interplay between learning, memory and forgetting broadens the conditions under which we expect to see social learning to evolve.

Social learning is typically thought to be most beneficial when the environments in which individuals live change quite slowly—they can safely learn tried and tested information from one another and it does not go out of date quickly. Innovating brand-new information, on the other hand, is thought to be useful in dynamic and rapidly changing environments.

To effectively assist humans in real-world settings, robots should be able to learn new skills and adapt their actions based on what users require them to do at different times. One way to achieve this would be to design computational approaches that allow robots to learn from human demonstrations, for instance observing videos of a person washing dishes and learning to repeat the same sequence of actions.

Researchers at University of British Columbia, Carnegie Mellon University, Monash University and University of Victoria recently set out to gather more reliable data to train robots via demonstrations. Their paper, posted to the arXiv preprint server, shows that the data they gathered can significantly improve the efficiency with which robots learn from the demonstrations of human users.

“Robots can build cars, gather the items for shopping orders in busy warehouses, vacuum floors, and keep the hospital shelves stocked with supplies,” Maram Sakr, one of the researchers who carried out the study, told Tech Xplore. “Traditional robot programming systems require an expert programmer to develop a robot controller that is capable of such tasks while responding to any situation the robot may face.”

Large language models (LLMs) are advanced deep learning algorithms that can process written or spoken prompts and generate texts in response to these prompts. These models have recently become increasingly popular and are now helping many users to create summaries of long documents, gain inspiration for brand names, find quick answers to simple queries, and generate various other types of texts.

Researchers at the University of Georgia and Mayo Clinic recently set out to assess the biological knowledge and reasoning skills of different LLMs. Their paper, pre-published on the arXiv server, suggests that OpenAI’s model GPT-4 performs better than the other predominant LLMs on the market on reasoning biology problems.

“Our recent publication is a testament to the significant impact of AI on biological research,” Zhengliang Liu, co-author of the recent paper, told Tech Xplore. “This study was born out of the rapid adoption and evolution of LLMs, especially following the notable introduction of ChatGPT in November 2022. These advancements, perceived as critical steps towards Artificial General Intelligence (AGI), marked a shift from traditional biotechnological approaches to an AI-focused methodology in the realm of biology.”

New research into large language models shows that they repeat conspiracy theories, harmful stereotypes, and other forms of misinformation.

In a recent study, researchers at the University of Waterloo systematically tested an early version of ChatGPT’s understanding of statements in six categories: facts, conspiracies, controversies, misconceptions, stereotypes, and fiction. This was part of Waterloo researchers’ efforts to investigate human-technology interactions and explore how to mitigate risks.

They discovered that GPT-3 frequently made mistakes, contradicted itself within the course of a single answer, and repeated harmful misinformation. The study, “Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording,” was published in Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing.