AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.
The humanoid robotics industry may be approaching its first real commercial inflection point – and the signal is not a new product launch or funding round, but a sharp and accelerating decline in prices. Recent disclosures from Unitree Robotics suggest the average price of its humanoid robots fell from approximately $85,000 in 2023 to about […]
Scientists in Japan say they have developed a new approach – dubbed ‘HEAPGrasp’ – that improves robots’ grasping success rate for transparent and shiny objects, beyond reducing handling time, using only RGB camera The fields of manufacturing, logistics, and even restaurants are increasingly moving toward automation, with robots being employed for a wide range of […]
Comau and Reis Robotics partner to deliver advanced automation systems across key industrial sectors
Comau and Reis Robotics have signed a cooperation agreement aimed at jointly developing and delivering advanced automation products and solutions for multiple industrial sectors through a coordinated technical and commercial approach. The agreement signed today by Comau and Reis Robotics, respectively Italian and German companies, testifies to the strength of the European technology framework in […]
Automation in law offices has shifted from a back-office convenience to a core part of how modern firms operate. What was once a profession defined by paper files and manual processes is now increasingly structured around systems – intake pipelines, workflow automation, and data tracking that bring speed and consistency to everyday operations. But the […]
A new tomato-picking robot is learning to think before it acts. Instead of simply identifying ripe fruit, it predicts how easy each tomato will be to harvest and adjusts its approach accordingly. This smarter strategy boosted success rates to 81%, with the robot even switching angles when needed. The breakthrough could pave the way for farms where robots and humans work side by side.
Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory physicists created a unifying mathematical framework that shows many AI techniques rely on the same core idea: compress data while preserving what’s most predictive. Their “control knob” approach helps researchers design better algorithms, use less data, and avoid wasted computing power. The team believes it could pave the way for more accurate, efficient, and environmentally friendly AI.
AI may learn better when it’s allowed to talk to itself. Researchers showed that internal “mumbling,” combined with short-term memory, helps AI adapt to new tasks, switch goals, and handle complex challenges more easily. This approach boosts learning efficiency while using far less training data. It could pave the way for more flexible, human-like AI systems.
New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced brain-like activity without any training at all. This challenges today’s data-hungry approach to AI development. The work suggests smarter design could dramatically speed up learning while slashing costs and energy use.
Aalto University researchers have developed a method to execute AI tensor operations using just one pass of light. By encoding data directly into light waves, they enable calculations to occur naturally and simultaneously. The approach works passively, without electronics, and could soon be integrated into photonic chips. If adopted, it promises dramatically faster and more energy-efficient AI systems.
Researchers have created a prediction method that comes startlingly close to real-world results. It works by aiming for strong alignment with actual values rather than simply reducing mistakes. Tests on medical and health data showed it often outperforms classic approaches. The discovery could reshape how scientists make reliable forecasts.