Leadership quality has a significant impact on firm productivity and can even affect national prosperity, but measuring individual leadership skills is difficult. Existing methods require observing prospective leaders working with multiple randomly assigned groups, an undertaking that can be both logistically complex and expensive.
Australian scientists, including at the Charles Perkins Centre, University of Sydney, have successfully developed a research system that uses ‘biological artificial intelligence’ to design and evolve molecules with new or improved functions directly in mammal cells. The researchers said this system provides a powerful new tool that will help scientists develop more specific and effective research tools or gene therapies.
Named PROTEUS (PROTein Evolution Using Selection) the system harnesses ‘directed evolution’, a lab technique that mimics the natural power of evolution. However, rather than taking years or decades, this method accelerates cycles of evolution and natural selection, allowing them to create molecules with new functions in weeks.
This could have a direct impact on finding new, more effective medicines. For example, this system can be applied to improve gene editing technology like CRISPR to improve its effectiveness.
Artificial intelligence systems like ChatGPT are notorious for being power-hungry. To tackle this challenge, a team from the Center for Optics, Photonics and Lasers (COPL) has come up with an optical chip that can transfer massive amounts of data at ultra-high speed. As thin as a strand of hair, this technology offers unrivaled energy efficiency.
Published in Nature Photonics, the innovation harnesses the power of light to transmit information. Unlike traditional systems that rely solely on light intensity, this chip also uses the phase of light, in other words, its shift.
By adding a new dimension to the signal, the system reaches unprecedented performance levels, all while maintaining a tiny size. “We’re jumping from 56 gigabits per second to 1,000 gigabits per second,” says Ph.D. student Alireza Geravand, the first author of the study.
In many countries, there is an academic cheating crisis with students misusing artificial intelligence tools like ChatGPT to write essays, dissertations and other assignments. According to new research, certain personality traits make some students more likely to pass off AI-generated work as their own.
In a study published in BMC Psychology, Jinyi Song of South Korea’s Chodang University and Shuyan Liu of Baekseok University surveyed 504 Chinese art students. They found that students who scored highly for dark personality traits like narcissism, machiavellianism and psychopathy (collectively known as “the Dark Triad”) were more likely to rely on AI tools like ChatGPT and Midjourney to do their work.
Although previous studies have revealed a link between dark personality traits and academic dishonesty, most research has focused on general student populations, not on specific groups such as art students.
“We are excited to be joining Google DeepMind along with some of the Windsurf team,” Mohan and Chen said in a statement. “We are proud of what Windsurf has built over the last four years and are excited to see it move forward with their world class team and kick-start the next phase.”
Google didn’t share how much it was paying to bring on the team. OpenAI was previously reported to be buying Windsurf for $3 billion.
IN A NUTSHELL 🌐 Realbotix’s humanoid robot can fluently speak 15 languages and access 147 additional languages via cloud support. 🏨 The robot aims to enhance communication in healthcare and hospitality by eliminating language barriers and improving user experiences. 📈 The global market for humanoid robots is projected to grow from $2.93 billion in 2025
Artificial intelligence (AI) is infamous for its resource-heavy training, but a new study may have found a solution in a novel communications system, called ZEN, that markedly improves the way large language models (LLMs) train.
The research team at Rice University was helmed by doctoral graduate Zhuang Wang and computer science professor T.S. Eugene Ng with contributions from two other computer science faculty members: assistant professor Yuke Wang and professor Anshumali Shrivastava. Stevens University’s Zhaozhuo Xu and Jingyi Xi of Zhejiang University also contributed to the project.