Scientists from the Korea Advanced Institute of Science and Technology (KAIST) have developed a new neuromorphic chip that can learn and correct its own mistakes. This development in the field of artificial intelligence (AI) opens the way to the creation of more efficient and autonomous devices that can operate without connecting to cloud servers. The study was published in Nature Electronics. The team’s development, led by professors Shinhyun Choi and Young-Gyu Yoon, is based on the use of a memristor, an element that imitates the work of synapses in the brain. The memristor has variable resistance, which allows it to simultaneously store and process data, significantly increasing the efficiency of the chip. The main problem with current neuromorphic devices is that they have imperfect characteristics that lead to errors. The new chip from KAIST is able to adapt and learn from these errors, improving its accuracy over time. This makes it ideal for use in video surveillance systems, medical devices, and other applications that require instant data processing. The test results showed that the chip achieves accuracy comparable to a perfect computer simulation, processing images in real time. The scientists emphasized that the key to success was the creation of not just a separate component, but an integrated system capable of solving complex problems in conditions of limited resources. Due to their unique properties, memristor platforms allow the creation of compact and energy-efficient devices capable of performing parallel calculations in analog format. This significantly increases the speed and security of operation, reducing dependence on external servers. As the researchers noted, the new technology changes the approach to implementing AI in everyday devices, providing the ability to process tasks directly on the spot, without the need to transmit data over the Internet. This solution promises to make artificial intelligence faster, smarter and more accessible to a wide range of users. The post New Brain-based chip self-learns and corrects errors appeared first on Anomalien.com.