An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
Fabricated as a single chip, the new implant is orders of magnitude faster and smaller than today's state-of-the-art brain-computer interfaces, offering an opportunity for more efficacious treatment of a number of neurological conditions.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
Fabricated as a single chip, the new implant is orders of magnitude faster and smaller than today's state-of-the-art brain-computer interfaces, offering an opportunity for more efficacious treatment of a number of neurological conditions.
EPFL researchers have discovered key 'units' in large AI models that seem to be important for language, mirroring the brain's language system. When these specific units were turned off, the models got much worse at language tasks.
Researchers from EPFL have developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips.
To strengthen the case for noninvasive BCIs, Carnegie Mellon researchers have demonstrated that through precision neuromodulation using focused ultrasound, the performance of a BCI could be improved for communication.
In this episode, we discuss how a team from Carnegie Mellon University spearheading non-invasive brain computer interface solutions has had a significant breakthrough in improving their accuracy.
Achieving a noteworthy milestone to advance noninvasive brain-controlled interfaces, researchers used AI technology to improve the decoding of human intention and control a continuously moving virtual object all by thinking about it, with unmatched performance.
Barani Raman, professor of biomedical engineering in the McKelvey School of Engineering, is leading a multidisciplinary team to study how the locust brain transforms sensory input into behavior with a four-year, $4.3 million grant from the National Science Foundation’s Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program.
As a result of the development of various machines and robots, we have become able to effortlessly handle jobs that humans could not perform with muscle strength and motor skills. Moreover, owing to advances in sensors, AI, and other information processing technologies, we have also become able to use perceptual abilities that exceed the five human senses.