《智能的新纪元：解密人工智能与生物智能的深度融合》
The New Era of Intelligence: Decoding the Deep Integration of AI and Biological Intelligence
过去十年，人工智能领域发生了翻天覆地的变化，一种全新的智能形态在地球上悄然崛起。这种智能既展现出令人惊叹的能力，又会犯下人类难以想象的错误。它具有独特的能力和脆弱性，而我们对其工作原理的理解却几乎为零。这促使科学家们开始思考：我们是否需要一门全新的智能科学来理解和发展人工智能？
Over the past decade, the field of artificial intelligence has undergone revolutionary changes, with a new form of intelligence quietly emerging on Earth. This intelligence demonstrates remarkable capabilities while making errors unimaginable to humans. It possesses unique abilities and vulnerabilities, yet our understanding of its working principles remains nearly zero. This prompts scientists to consider: do we need a new science of intelligence to understand and develop AI?
要真正理解人工智能，我们需要将其置于生物智能的历史背景中。从5亿年前的脊椎动物共同祖先开始，经过漫长的进化，大脑逐渐发展成为一个惊人的器官。在从牛顿到爱因斯坦的500年间，人类大脑发展出了理解从夸克到宇宙学的深奥数学和物理学理论。而这一切都是在没有ChatGPT帮助的情况下完成的。
To truly understand artificial intelligence, we need to place it in the historical context of biological intelligence. Starting from the common ancestor of vertebrates 500 million years ago, through long evolution, the brain gradually developed into an amazing organ. In the 500 years from Newton to Einstein, the human brain developed sophisticated mathematics and physics theories to understand everything from quarks to cosmology. And all this was accomplished without the help of ChatGPT.
目前，人工智能面临着几个关键的挑战需要解决。首先是数据效率问题。当前的语言模型需要约一万亿个单词的训练数据，而人类一生中接触的单词量仅有1亿个左右。即使考虑到人类5亿年的进化历史，通过DNA传递的信息也只有约7亿字节。这意味着我们需要开发更高效的学习算法。
Currently, AI faces several key challenges that need to be addressed. First is the issue of data efficiency. Current language models require training data of about one trillion words, while humans encounter only about 100 million words in their lifetime. Even considering 500 million years of human evolution, the information passed through DNA is only about 700 megabytes. This suggests we need to develop more efficient learning algorithms.
其次是能源效率问题。人类大脑仅消耗20瓦的能量，而训练大型AI模型可能需要消耗1000万瓦的电力。这种差异源于数字计算本身的选择：每一次快速可靠的位翻转都需要消耗大量能量。相比之下，生物计算采用"及时计算正确答案"的策略，使用尽可能慢且不可靠的中间步骤，从而实现了能源效率的最大化。
Second is the issue of energy efficiency. The human brain consumes only 20 watts of power, while training large AI models may require 10 million watts. This difference stems from the choice of digital computation itself: each fast and reliable bit flip requires significant energy consumption. In contrast, biological computation adopts a "just-in-time correct answer" strategy, using intermediate steps that are as slow and unreliable as possible, thus maximizing energy efficiency.
更令人兴奋的是，科学家们正在探索将神经算法与量子硬件结合的可能性。通过用原子替代神经元，用光子替代突触，我们可以构建全新的量子神经形态计算系统，这将开启智能计算的新纪元。
More excitingly, scientists are exploring the possibility of combining neural algorithms with quantum hardware. By replacing neurons with atoms and synapses with photons, we can build entirely new quantum neuromorphic computing systems, which will usher in a new era of intelligent computing.
在可解释性方面，科学家们正在开发新的方法来理解人工智能模型的工作原理。通过构建数字孪生技术，我们可以更好地理解大脑的工作机制，并实现大脑与机器之间的双向通信。这种技术已经在小鼠实验中取得突破，科学家们可以通过控制仅20个神经元来影响小鼠的视觉感知。
In terms of explainability, scientists are developing new methods to understand how AI models work. Through digital twin technology, we can better understand how the brain works and achieve bidirectional communication between brains and machines. This technology has already achieved breakthroughs in mouse experiments, where scientists can influence mouse visual perception by controlling just 20 neurons.
智能科学的未来发展需要开放和长期的视角。学术界成为推动这一领域发展的理想场所，因为它不受季度收益报告的限制，不受企业法律部门的审查，能够进行更广泛的跨学科研究，并致力于与世界分享研究成果。如果说上个世纪人类最伟大的智力冒险是向外探索宇宙，那么这个世纪最伟大的智力冒险将是向内探索，既探索我们自己，也探索我们创造的人工智能，以发展出对智能的更深层科学理解。
The future development of intelligence science requires an open and long-term perspective. Academia becomes the ideal place to advance this field because it is free from quarterly earnings reports, corporate legal department censorship, can conduct broader interdisciplinary research, and is committed to sharing research results with the world. If humanity's greatest intellectual adventure in the last century was exploring the universe outward, then this century's greatest intellectual adventure will be exploring inward, both into ourselves and into the artificial intelligence we create, to develop a deeper scientific understanding of intelligence.

贴主:gaganow于2025_03_01 5:26:06编辑