Interestingly, a study by the University of Cambridge found that an artificial intelligence system organizes by itself to adapt the human brain’s characteristics when it is equipped with a simple physical constant. Upon applying the simple physical constraint to the AI System, the system the AI adapts some characteristics of the human brain. Since the University of Cambridge revealed this development, it has been making headlines leaving people curious about it. Therefore, we have come up with this column to make you informed about it. Let’s delve deep into the details and unfold more information about it. Kindly drag down the page and learn more.
Reportedly, the researchers at the University of Cambridge put physical constraints on an AI system which was similar to how animals and human brains have to operate and develop with both biological and physical constructs. Upon applying physical constraints to it, the AI system developed some characteristics of the brains of humans to perform tasks. The University of Cambridge worked to develop a simplified version of the brain. In a bid to do so, they applied physical constraints to it before giving tasks. Shift to the next section and read more details.
In a study published today in a journal, Nature Machine Intelligence, Danyal Akarca, and Jascha Achterberg from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBSU) at the University of Cambridge worked with their colleagues to make a simplified version of the brain. Danyal Akarca and Jascha Achterberg used computational nodes instead of real neurons or brain cells because neurons and nodes perform the same functions. Initially, the system did not know how to perform the task and kept making mistakes. However, the researchers kept giving it directions until it gradually learned to get better at the task. Continue reading this article.
Thereafter, the AI system repeated the task multiple times until it learned how to perform the task perfectly. Achterberg said, “We see a lot of potential in using our insights to create AI models that are made simpler in their internal structure while preserving their capabilities so that they run more efficiently on computer chips. We also think our results can help to better distribute large AI models across multiple chips within large-scale compute clusters,” Stay tuned to this website for more details and further updates.