The cortex is filled with excitatory local synapses, but we know little about their role in brain function. New experimental tools, along with ideas from artificial intelligence, are poised to change that.

In a new study published in PNAS Nexus, scientists have demonstrated that artificial intelligence can predict different types of human intelligence by analyzing connections in the brain. Using neuroimaging data from hundreds of healthy adults, they found that predictions were most accurate for general intelligence, followed by crystallized intelligence, and then fluid intelligence. The findings shed light on the distributed and dynamic nature of intelligence, demonstrating that it arises from the global interplay of brain networks rather than isolated regions.

While prior research has established that intelligence is not localized to a single brain region but rather involves distributed networks, many studies have relied on traditional methods that focus on isolated brain features. These approaches have offered limited insights into how intelligence arises from the interplay of brain structure and function. By employing machine learning to analyze brain connectivity, the researchers aimed to overcome these limitations.

A key focus of the study was the distinction between three major forms of intelligence: general, fluid, and crystallized. General intelligence, often referred to as “g,” is a broad measure of cognitive ability that encompasses reasoning, problem-solving, and learning across a variety of contexts. It serves as an overarching factor, capturing shared elements between specific cognitive skills.

The electronics industry is approaching a limit to the number of transistors that can be packed onto the surface of a computer chip. So, chip manufacturers are looking to build up rather than out.

Instead of squeezing ever-smaller transistors onto a single surface, the industry is aiming to stack multiple surfaces of transistors and semiconducting elements—akin to turning a ranch house into a high-rise. Such multilayered chips could handle exponentially more data and carry out many more complex functions than today’s electronics.

A significant hurdle, however, is the platform on which chips are built. Today, bulky silicon wafers serve as the main scaffold on which high-quality, single-crystalline semiconducting elements are grown. Any stackable chip would have to include thick silicon “flooring” as part of each layer, slowing down any communication between functional semiconducting layers.

OpenAI has run into problem after problem on its new artificial-intelligence project, code-named Orion.

Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass human creativity.