From Analog to AI: The Journey of Digital Evolution

Digital World |

Across the vast expanse of time, the birth and development of intelligence is one of the most mysterious and fascinating phenomena, a glittering thread running through billions of years of life's evolution. From early philosophers contemplating the nature of wisdom to today's scientists using sophisticated instruments and complex algorithms to decipher the secrets of intelligence, humanity's exploration of the evolution of intelligence has never ceased.

Looking back in history, ancient Greek philosophers Plato and Aristotle began to consider human cognition and thought, and their ideas have become the source of inspiration for intelligence research. Modern-day, the convergence of multiple disciplines, including neuroscience, biology, and computer science, has provided more powerful tools for uncovering the truth about the evolution of intelligence. The evolution of intelligence not only impacts our understanding of ourselves but also has profound implications for future technological development and social progress. It holds the key to unlocking the mysteries of life and a map to the future world.

AI: From Virtual to Physical
AI, a virtual entity typically running on silicon chips, now faces new challenges in its own evolution. How would giving AI a physical form affect its intelligence? AI is gradually evolving from virtual to physical form. In a Stanford University experiment, a diverse range of intelligent agents evolved using a DataContract-RL environment. How can this breakthrough be leveraged to further advance AI intelligence?
DERL and Unimals
Led by Fei-Fei Li, a Stanford University research team constructed a computer simulation environment called DERL (Deep Evolutionary Reinforcement Learning), a kind of "playground" for intelligent agents. Within this DERL environment, agents called Unimals underwent mutation and selection similar to biological evolution. This experiment further revealed that the physical form of these virtual agents profoundly impacted their ability to learn new tasks.
The Impact of Physical Form on AI Intelligence
These findings raise an important question: Physically formed AI exhibits more efficient learning and evolution, especially when tackling complex and diverse tasks. Agents that continuously learn and evolve in more challenging environments exhibit faster evolution and better performance than those that evolve in simpler environments.
Evolutionary Algorithms and Complex Tasks
In this study, we found that the most successful Unimal morphology demonstrated a higher speed in task mastery compared to previous generations, despite no significant difference in their initial intelligence levels. This finding further confirms the central role of embodiment in intelligent evolution. However, for certain complex and diverse tasks, such as climbing through nuclear reactors to extract waste, earthquake relief work, guiding nanorobots through the human body, or even performing household chores like washing dishes and folding clothes, humans may find it difficult to directly design suitable robot bodies. Finding the optimal robot morphology through evolutionary algorithms to cope with various complex tasks demonstrates AI's adaptability in different environments.

Artificial Intelligence: A New Journey in Intelligent Evolution
  • Neural Networks: Digital Architecture for Simulating Intelligence
In the wave of modern technology, artificial intelligence has emerged as a new field of intelligent evolution. Neural networks in the field of artificial intelligence represent a great attempt by scientists to simulate the structure and function of the human brain. Convolutional Neural Networks (CNNs) mimic the hierarchical structure of the human visual cortex, building powerful image recognition capabilities. Like the human eye, it can accurately identify objects in images; whether it's facial recognition, license plate recognition, or medical image analysis, CNNs demonstrate outstanding performance. The Transformer architecture abstracts the brain's default mode network, achieving a major breakthrough in natural language processing. Language models, represented by the GPT series, can understand and generate human language, enabling intelligent dialogue, text generation, machine translation, and other functions. Machine learning algorithms continuously learn and optimize from massive amounts of data, much like organisms evolving in their natural environment. By adjusting parameters and improving algorithms, they continuously enhance their intelligence, enabling machines to perform increasingly complex tasks. Artificial intelligence, as a representative of silicon-based intelligence, relies on silicon-based chips and circuits to simulate human intelligence through code and algorithms. Unlike carbon-based intelligence, which has evolved over billions of years, silicon-based intelligence has developed rapidly in just a few decades thanks to human technology, evolving from simple computational programs to today's complex intelligent systems, demonstrating a new path of intelligent evolution.
  • Reward Function: Algorithmic Mechanisms that Mimic Intelligent Decision-Making
Artificial intelligence systems, represented by AlphaGo, mimic the process of human intelligent decision-making through sophisticated reward functions. In Go, AlphaGo's Monte Carlo tree search and deep reinforcement learning algorithms are like the decision-making mechanisms in the human brain. The reward function, much like the reward mechanism of the human dopamine system, provides positive feedback when AlphaGo makes a good move and approaches victory, encouraging it to continue optimizing its strategy; conversely, it provides negative feedback, prompting it to adjust its strategy.
Through continuous self-play and learning, AlphaGo was able to make optimal decisions in complex Go games, ultimately defeating top human players. This process demonstrates the enormous potential of artificial intelligence in simulating human intelligent decision-making, providing a new perspective on understanding intelligent decision-making processes and showing us the possibility of machine intelligence surpassing humans in specific fields. Silicon-based intelligence, through unique algorithms and models, simulates the decision-making process of the human carbon-based brain. Although its underlying mechanisms are completely different from carbon-based intelligence, it has already shown comparable capabilities to carbon-based intelligence in certain tasks, even surpassing humans in computational speed and data processing capacity.

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