Lifeboat Foundation

In conclusion, don’t accept what the AI tells you as absolute truth. Don’t make critical decisions based on its answers. And remember that you sometimes have to negotiate with the AI before it’s willing to give you helpful answers.

This test is yet another case where I’ve been able to turn to the AI and find an answer for a very me-specific question without coding in minutes.

If you have a question that requires a lot of text or numerical analysis, consider running it by ChatGPT or one of the other AIs. You might get a useful answer in minutes.

We are fast moving to level 3 of Artificial Intelligence — AI Agents. Does AI include sufficient reasoning for AI agents?

If you’re ever faced with trying to pick up a grain of rice with a pair of chopsticks, spare a thought for the scientists behind this latest innovation, which has been called “a medical breakthrough on the verge of happening.” They’ve painstakingly built a soft robot with the capacity to carry different types of drugs through the body. It’s the size of a grain of rice, and can be driven to various internal targets via magnetic fields.

Researchers in the School of Mechanical and Aerospace Engineering (MAE) at Nanyang Technological University, Singapore (NTU Singapore), have built on earlier work to create a grain-sized soft robot that can enter the body and be controlled by magnetic fields to travel to a specific target. Once there, it can quickly or slowly release the medication it has stored in its tiny frame.

In an interview to Moneycontrol, Meta’s Yann LeCun expressed optimism about the benefits of artificial intelligence and calls out any harm arising from it as ‘science fiction’ at this point.

But not for a wide audience.

Each retraining may cost millions of dollars in computation.

New research shows that AI models need to be completely retrained to learn new concepts — which is an expensive problem for AI companies.

Researchers Tomohito Amano and Shinji Tsuneyuki of the University of Tokyo with Tamio Yamazaki of CURIE (JSR-UTokyo Collaboration Hub) have developed a new machine learning model to predict the dielectric function of materials, rather than calculating from first-principles.

Researchers have developed a faster and more accurate method for acquiring and reconstructing high-quality 3D surface measurements. The approach could greatly improve the speed and accuracy of surface measurements used for industrial inspection, medical applications, robotic vision and more.

Utilizing a novel AI-driven method, researchers enhanced the precision of estimating critical cosmological parameters by analyzing galaxy distributions.

This breakthrough allows for more refined studies of dark matter and energy, with implications for resolving the Hubble tension and other cosmic mysteries.

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