Researchers have developed a hydrogel that can learn to play the game Pong, demonstrating that even simple materials can exhibit adaptive behaviors akin to those seen in living systems.
The study, led by Dr. Yoshikatsu Hayashi from the University of Reading, also revealed that similar hydrogels could mimic cardiac tissue, potentially offering new avenues for studying heart arrhythmias and reducing animal testing in medical research.
“Hydrogel Brain” Learns To Play Pong
AI is shaking up industries — and software engineering is no exception.
In a leaked recording of a June fireside chat obtained by Business Insider, Amazon Web Services CEO Matt Garman reportedly told employees that AI is changing what being a software engineer means —and essentially changes the job description.
“If you go forward 24 months from now, or some amount of time — I can’t exactly predict where it is — it’s possible that most developers are not coding,” Garman said, adding later that the developer role would look different next year compared to 2020.
New technique lets robots detect force and interpret touch without extra sensors.
Researchers used internal sensors and machine learning to give robots an innate sense of touch, bypassing expensive external sensors.
A new study involving an AI scientist that can run its own experiments is both fascinating and terrifying, changing science forever.
Robotic automation has become a game-changer in addressing labor shortages. While traditional rigid grippers have effectively automated various routine tasks, boosting efficiency and productivity in industries that deal with objects of well-defined specifications, they fall short in sectors like the food industry, where delicate objects of varying sizes and shapes need to be handled. In these cases, a more specialized type of gripper is required.
“Bioinspired soft robotics seeks to develop technologies that draw inspiration from nature and leverage advanced materials and fabrication processes,” said Dr. Pablo Valdivia y Alvarado, Associate Professor at the Singapore University of Technology and Design (SUTD).
Soft grippers inspired by the natural dexterity and control of human hands are particularly well-suited to the food industry. They can adapt to objects of varying sizes and shapes while distributing forces more evenly, making them ideal for handling delicate items.
According to a paper published by Nature Computational Science on Friday, the researchers developed a model that bridges the gap between big, externally complex AI networks and the small, internally complex workings of the brain.
Industry experts said the team’s findings could mark a pivotal shift in AI development, prompting further exploration of computing solutions that are not dependent on silicon chips.
Designed to mimic human decision-making and physical interaction, the Astribot S1 robot can handle tasks that would traditionally require human dexterity and judgment.
Launched by Stardust Intelligence, a Chinese company, the robot has a human-like upper body structure mounted on a wheeled base.
During its first technical demonstration, the robot was seen folding clothes, sorting items, flipping pans while cooking, vacuuming, and cup stacking, attracting widespread attention in the industry.
A recent study by UC San Diego researchers brings fresh insight into the ever-evolving capabilities of AI. The authors looked at the degree to which several prominent AI models, GPT-4, GPT-3.5, and the classic ELIZA could convincingly mimic human conversation, an application of the so-called Turing test for identifying when a computer program has reached human-level intelligence.
The results were telling: In a five-minute text-based conversation, GPT-4 was mistakenly identified as human 54 percent of the time, contrasted with ELIZA’s 22 percent. These findings not only highlight the strides AI has made but also underscore the nuanced challenges of distinguishing human intelligence from algorithmic mimicry.
The important twist in the UC San Diego study is that it clearly identifies what constitutes true human-level intelligence. It isn’t mastery of advanced calculus or another challenging technical field. Instead, what stands out about the most advanced models is their social-emotional persuasiveness. For an AI to catch (or fool a human) it has to be able to effectively imitate the subtleties of human conversation. When judging whether their interlocutor was an AI or a human, participants tended to focus on whether responses were overly formal, contained excessively correct grammar, or repetitive sentence structures, or exhibited an unnatural tone. Participants flagged stilted or inconsistent personalities or senses of humor as non-human.
There’s a lot of chatter about AI supposedly passing the Turing Test. Don’t fall for it. Here’s the truth, including a close look at the Reverse Turing Test.
Scientists are rethinking how to implement automation for biologists to reduce costs, simplify adoption, and increase reproducibility.
