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The research conducted by Cambridge scientists involved applying physical constraints to an artificial system, designed to model a simplified version of the brain. This system used computational nodes to mimic neurons and was placed in a virtual space where communication between nodes was more challenging as they were further apart, similar to the organization of neurons in the human brain. The artificial system was given a task and learned to perform it correctly by adjusting the strength of connections between nodes, similar to how human brain cells adapt as we learn. As a result, the system developed hubs (highly connected nodes) and flexible coding schemes, resembling features of complex organisms’ brains. This research has implications for understanding differences in human brains and may benefit the development of more efficient AI systems, particularly in scenarios with physical constraints. It was funded by various organizations, including the Medical Research Council and Google DeepMind.

Dear Jascha Achterberg, Danyal Akarca, and Duncan Astle,

I am writing to express my keen interest in your recent study on how physical constraints can influence the development of artificial intelligence systems. Your findings are particularly intriguing in light of the similarities between your artificial system and the human brain.

I am particularly interested in your observation that the physical constraint of distance between nodes led your artificial system to develop hubs and flexible coding schemes. These are both features that are also seen in the human brain, and it is fascinating to see that they can emerge spontaneously in an artificial system.

I believe that your work has the potential to significantly advance our understanding of the human brain. By understanding how physical constraints shape the development of artificial intelligence systems, we may be able to gain new insights into how the human brain is organized and how it functions.

I would be grateful if you could provide me with more information about your study. Specifically, I would be interested to learn more about the following:

  • How did you measure the response profiles of individual nodes?
  • What other physical constraints did you consider?
  • What are the implications of your findings for our understanding of the human brain?

Thank you for your time and consideration.


Tim, MA.

Source: Cambridge Research News

FAQ: AI System Self-Organizes to Develop Features of Brains of Complex Organisms

Q1. What is the key takeaway from the study on AI system self-organization?

A1. The key takeaway from the study is that placing physical constraints on an AI system can force it to develop features similar to those found in the human brain. This suggests that the constraints imposed by the physical world may play a fundamental role in shaping the organization of the brain.

Q2. What specific features did the AI system develop?

A2. The AI system developed two key features:

  • Hubs: Highly connected nodes that act as conduits for passing information across the network.
  • Flexible coding: The ability of individual nodes to encode multiple properties of the task at hand.

Q3. How do these features compare to those found in the human brain?

A3. Hubs are a common feature of the human brain, and they are thought to play an important role in efficient information processing. Flexible coding is also seen in the human brain, and it is thought to allow us to adapt to new situations and learn new tasks.

Q4. What are the implications of these findings for understanding the human brain?

A4. The findings suggest that the physical constraints of the brain may play a more important role in its organization than previously thought. This could help us to understand how the brain works and how to develop more efficient AI systems.

Q5. What are the implications of these findings for designing future AI systems?

A5. The findings suggest that AI systems that are designed to solve problems similar to those faced by humans may need to be more brain-like in their organization. This could lead to the development of more efficient and versatile AI systems.