The ability to read, converse, process vast amounts of data, and provide business recommendations makes modern artificial intelligence seem more human-like than ever. However, AI still has several significant limitations.
Kyle Daruwalla, a NeuroAI Scholar at Cold Spring Harbor Laboratory (CSHL), points out that despite the impressive capabilities of current AI technologies like ChatGPT, they are still restricted in interacting with the physical world. Even tasks such as solving math problems and writing essays require billions of training examples to perform well.
Daruwalla is exploring unconventional methods to develop AI that can overcome these computational challenges, and he may have just identified a promising approach.
The key was moving data. Presently, a large portion of the energy used in modern computing is attributed to the transmission of data. Artificial neural networks, consisting of billions of connections, often require data to travel long distances. Therefore, Daruwalla sought inspiration from the human brain, known for being both highly computationally powerful and energy-efficient.
To read more, click here.