How Artificial General Intelligence (AGI) could create machines that evolve like life.

Humans have ascended to our current level of intelligence over many centuries, evolving to stay alive, reproduce and thrive. Evolution has shaped survival strategies for nearly all living creatures, and this same principle is now being applied to Artificial General Intelligence (AGI). 

In computer science, AGI is an intelligent system with comprehensive knowledge and cognitive computing capabilities. Scientists envision AGI having the mental capacities of a human—the abilities to learn, speak, plan, and perform tasks across a range of activities—combined with speed, memory, reliability and the scalability of high-performance computing. Still in the very early stages of development, the objective of AGI is to mimic generalized human cognitive abilities in software to perform any task that a human could do. 

AGI’s digital DNA 

AGI isn’t just learning, it’s also evolving. Thanks to Evolutionary Algorithms (EAs) inspired by the principles of biological evolution and Darwin’s theory of natural selection, there is a powerful way to take on complex problems in AGI. EAs are crucial in the development of AGI as they offer a framework for artificial intelligence (AI) to learn and adapt, replicating how biological systems in nature evolve to solve problems.  

The principle of “survival of the fittest” occurs in EAs’ selection process where the best solutions have a better chance of surviving and reproducing. In each generation of the algorithm, the best solutions are selected and undergo crossover. Just like how natural genes mix, AGI could recombine learned models and behaviors to produce new hybrid solutions. Some of these selected solutions will mutate to produce new traits, structures, or behaviors that were not present in previous iterations of the code—and these are vital for adaptation and diversification. The whole process repeats across multiple generations until the algorithm converges on an optimal solution. AI scientists call this Emergence; over time, behaviors not explicitly programmed will produce behaviors that are adaptive, sometimes unexpected and highly efficient.  

In essence, evolutionary algorithms strive to imitate how species evolve in nature to survive—but with AGI, computer scientists envision it solving business-oriented problems like minimizing cost or maximizing efficiency. 

Industries Evolving with AGI 

AGI in business holds tremendous promise, especially in sectors like Gaming, Robotics and Materials Science. 

Gaming 

AGI is poised to play a key role in game design with the potential to create more immersive and interactive experiences. By collecting player interaction data, AGI could help refine and greatly improve player experiences. For instance, games could evolve in real time based on collective player decisions, mirroring co-evolution in ecosystems. Non-player characters could adapt their tactics within gameplay, too, creating new interactions. Like real life, the wondrous worlds within games could evolve, helping them become fresh and new whenever players immerse themselves.  

Video games offering these kinds of experiences are already in market but could greatly improve with AGI. Games like SimCity are called Open Simulation games as they enable players to explore and interact with a virtual world without a defined endpoint. AGI promises to shift open simulation games beyond established scenarios to create responsive and personalized experiences where the world, its inhabitants, and the story evolve organically based on player encounters. 

Robotics 

Morphological evolution refers to changes in the form and structure of organisms over time, often driven by evolutionary processes. This principle of nature is being mimicked in robot design; it involves altering a robot’s physical structures over iterations to optimize its performance and adaptability in its surroundings. Using EAs to simulate natural selection, AGI could help evolve robot designs, leading to changes in their structure, including body shape, sensors, actuators, and materials.  

Swarm Robotics is another approach to robot design that takes its cue from nature. Swarm Robotics involves the design, construction and deployment of large groups of robots that coordinate and collaborate to solve a problem or perform a task—patterned after self-organizing systems, like social insects, schools of fish or flocks of birds. Swarm Robotics extracts cognitive engineering principles from collaborative systems in nature to provide multi-robot systems with similar abilities. This helps to build systems that are stronger, error-proof, and more flexible than single robots, enabling them to better adapt their behavior to changes in their surroundings. Swarm Robotics could really excel in its scalability and its ability to solve problems through decentralized control, making it highly applicable to robot design. 

AI-powered robotics solutions are starting to emerge in the marketplace and AGI could play an integral role in its development. For instance, there is now a prosthesis that provides a more natural walking experience for amputees. Developed at MIT, bionic leg wearers can control their prosthetic feet using their thoughts to walk more naturally, traverse stairs and move around barriers.  

Materials Science 

By understanding how certain materials behave, scientists can create new and improved materials for all kinds of things, from jets to phones to medicine. And AGI promises to accelerate this process. EAs would be utilized to iteratively generate and evaluate potential materials, mimicking the process of evolution. Following the theory of natural selection, the best traits—like strength, flexibility and conductivity—that emerge after testing would become the new solutions. What scientists ultimately hope to develop are materials that can self-heal or adapt. Self-healing and adaptive materials would mimic biological regeneration or environmental responsiveness. They would be engineered to change their properties or repair themselves in response to external stimuli like temperature, light, pressure, and electric fields or magnetic fields.  

AI has been put to the test in discovering new battery materials but could greatly improve with AGI. A major tech company and a national lab whittled down millions of possible electrolyte materials into 18 viable candidates in less than a week; using a traditional process that normally would have taken 20 years. While finding the potential battery materials was impressive, the real breakthrough was the incredible speed of the discovery. 

Machine Evolution: Ethics and Philosophy 

Evolution doesn’t always produce “good” results. It is driven by natural selection acting on existing genetic variation. Trade-offs are common; improving one trait often emerges at the expense of another.  

Because AI algorithms are trained on data, they often reflect existing biases in the wider society as well as those of the designers. And since the AI algorithms are shaped by this data, they could easily perpetuate or amplify existing biases. As AGI advances, there should be processes in its design and execution that incorporate technical diligence, fairness, and equity. The goal is to have algorithms designed responsibly and to avoid the unfortunate consequences of heightened systemic discrimination and unethical applications. 

There is another dilemma. If AGI’s capabilities expand to become sentient, will it be classified as a new species? Some believe that if AGI can learn, adapt, and set its own goals, it could be called a new form of life, albeit one without a biological body. The idea certainly prompts humanity to question what defines life and evolution. It is therefore crucial that the decisions made today regarding AGI development and regulation closely consider humanity’s future and ensure that AGI reflects human values and benefits the whole of society. 

The New Evolutionary Frontier  

Humanity has evolved and AGI has the opportunity, as well. By following the evolutionary principles in its development, AGI can grow to function like humans: learning, problem-solving, reasoning and collaborating. It represents a monumental shift in the way humans interact with technology and each other.   

In summary, AGI could evolve to become the super brain right out of science fiction, performing a wide range of cognitive tasks as well as or better than humans in ways that reflect human goals, values, and safe decision-making. AGI is poised to play a key role in gaming development, robotics, and materials science with more applications on the horizon. And while this technology is potentially transformative, its development and deployment raise fundamental scientific, ethical, and societal questions with no easy answers. 

As machines evolve more like life, are we ready to evolve with them? 

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