stub Physical Constraints Drive Evolution of Brain-Like AI - Unite.AI
Connect with us

Artificial Intelligence

Physical Constraints Drive Evolution of Brain-Like AI

Updated on

In a groundbreaking study, Cambridge scientists have taken a novel approach to artificial intelligence, demonstrating how physical constraints can profoundly influence the development of an AI system.

This research, reminiscent of the developmental and operational constraints of the human brain, offers new insights into the evolution of complex neural systems. By integrating these constraints, the AI not only mirrors aspects of human intelligence but also unravels the intricate balance between resource expenditure and information processing efficiency.

The Concept of Physical Constraints in AI

The human brain, an epitome of natural neural networks, evolves and operates within a myriad of physical and biological constraints. These limitations are not hindrances but are instrumental in shaping its structure and function. In the words of Jascha Achterberg, a Gates Scholar from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBSU) at the University of Cambridge, “Not only is the brain great at solving complex problems, it does so while using very little energy. In our new work, we show that considering the brain's problem-solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do.”

The Experiment and Its Significance

The Cambridge team embarked on an ambitious project to create an artificial system that models a highly simplified version of the brain. This system was distinct in its application of ‘physical' constraints, much like those in the human brain.

Each computational node within the system was assigned a specific location in a virtual space, emulating the spatial organization of neurons. The greater the distance between two nodes, the more challenging their communication, mirroring the neuronal organization in human brains.

This virtual brain was then tasked with navigating a maze, a simplified version of the maze navigation tasks often given to animals in brain studies. The importance of this task lies in its requirement for the system to integrate multiple pieces of information—such as the start and end locations, and the intermediate steps—to find the shortest route. This task not only tests the system's problem-solving abilities but also allows for the observation of how different nodes and clusters become critical at various stages of the task.

Learning and Adaptation in the AI System

The journey of the artificial system from novice to expert in maze navigation is a testament to the adaptability of AI. Initially, the system, akin to a human learning a new skill, struggled with the task, making numerous errors. However, through a process of trial and error and subsequent feedback, the system gradually refined its approach.

Crucially, this learning occurred through alterations in the strength of connections between its computational nodes, mirroring the synaptic plasticity observed in human brains. What's particularly fascinating is how the physical constraints influenced this learning process. The difficulty in establishing connections between distant nodes meant the system had to find more efficient, localized solutions, thus imitating the energy and resource efficiency seen in biological brains.

Emerging Characteristics in the Artificial System

As the system evolved, it began to exhibit characteristics startlingly similar to those of the human brain. One such development was the formation of hubs – highly connected nodes acting as information conduits across the network, akin to neural hubs in the human brain.

More intriguing, however, was the shift in how individual nodes processed information. Instead of a rigid coding where each node was responsible for a specific aspect of the maze, the nodes adopted a flexible coding scheme. This meant that a single node could represent multiple aspects of the maze at different times, a feature reminiscent of the adaptive nature of neurons in complex organisms.

Professor Duncan Astle from Cambridge’s Department of Psychiatry highlighted this aspect, stating, “This simple constraint – it's harder to wire nodes that are far apart – forces artificial systems to produce some quite complicated characteristics. Interestingly, they are characteristics shared by biological systems like the human brain.”

Broader Implications

The implications of this research extend far beyond the realms of artificial intelligence and into the understanding of human cognition itself. By replicating the constraints of the human brain in an AI system, researchers can gain invaluable insights into how these constraints shape brain organization and contribute to individual cognitive differences.

This approach provides a unique window into the complexities of the brain, particularly in understanding conditions that affect cognitive and mental health. Professor John Duncan from the MRC CBSU adds, “These artificial brains give us a way to understand the rich and bewildering data we see when the activity of real neurons is recorded in real brains.”

Future of AI Design

This groundbreaking research has significant implications for the future design of AI systems. The study vividly illustrates how incorporating biological principles, particularly those related to physical constraints, can lead to more efficient and adaptive artificial neural networks.

Dr. Danyal Akarca from the MRC CBSU underscores this, stating, “AI researchers are constantly trying to work out how to make complex, neural systems that can encode and perform in a flexible way that is efficient. To achieve this, we think that neurobiology will give us a lot of inspiration.”

Jascha Achterberg further elaborates on the potential of these findings for building AI systems that closely mimic human problem-solving abilities. He suggests that AI systems tackling challenges similar to those faced by humans will likely evolve structures resembling the human brain, particularly when operating within physical constraints like energy limitations. “Brains of robots that are deployed in the real physical world,” Achterberg explains, “are probably going to look more like our brains because they might face the same challenges as us.”

The research conducted by the Cambridge team marks a significant step in understanding the parallels between human neural systems and artificial intelligence. By imposing physical constraints on an AI system, they have not only replicated key characteristics of the human brain but also opened new avenues for designing more efficient and adaptable AI.

Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.