为何“千脑理论”可能是真正通用人工智能的关键

来源：科技世代千高原

近年来，人工智能 (AI) 取得了令人瞩目的进步。语言模型能够生成文本，图像识别系统能够创建逼真的视觉效果，机器能够以惊人的速度掌握模式识别任务。然而，尽管取得了这些进步，人工智能仍未达到人类智能真正卓越的水平：持续的终身学习以及从经验中归纳总结的能力。这被称为通用人工智能 (AGI)。

我的核心假设是，真正的通用人工智能 (AGI) 只有在人工智能能够持续学习、灵活地实时调整其理解，而非仅仅依赖于大规模的一次性训练时才能实现。人类天生就会持续学习，根据与周围环境的每一次新互动来更新知识和理解。而目前的人工智能系统通常不具备这种能力。

计算机科学家、神经科学家和工程师杰夫·霍金斯提出的“千脑”理论为实现这种持续学习提供了宝贵的见解。霍金斯认为，大脑是由众多小型、分散的单元（称为皮质柱）组成的网络。每个皮质柱通过进行预测、将其与实际感官输入进行比较并根据差异不断更新，独立地创建自己的“微型现实模型”。这种分散式结构允许进行稳健、适应性强且持续的学习，从而有效地避免了集中式神经网络方法中经常遇到的灾难性遗忘。

为什么当前的人工智能系统存在缺陷

如今，大多数人工智能系统严重依赖于大量的初始训练（预训练），之后则保持稳定。与人类不同，这些系统无法持续适应新情况或实时吸收新信息。因此，人工智能系统通常难以将知识灵活地应用于不可预见的任务或情境。

我的观点是，AGI 的实现只有通过创造能够在整个运行生命周期内持续学习、调整和保留知识的 AI 才能实现。受“千脑理论”启发，实施一种去中心化的模块化方法或许有助于解决这些问题，因为它可以让 AI 动态地整合新的经验，同时保留先前学到的知识。

为什么参考框架对于真正的认知至关重要

仅靠持续学习是不够的。它需要一个关键要素：能够整合感官输入的稳定参考框架。对人类而言，主要的参考框架是我们的身体。以识别咖啡杯为例：仅凭视觉识别是不够的。只有当我们亲手触摸它，感受它的形状和重量时，我们才能真正理解并形成连贯的内部表征。每一种感官输入——视觉、触觉和运动——都位于我们身体形态所提供的共享、稳定的环境中。

人工智能要想发展出同样复杂的认知能力，还必须运用清晰一致的参考框架。这些参考框架至关重要，因为它们使人工智能能够将不同的感官输入整合成连贯的心理表征，类似于人类通过身体解读感官数据的方式。这种方法与世界模型的概念密切相关，人工智能首先需要深入理解并内化各种对象和概念的特征和关系。只有创建了这种稳定、集成的模型，人工智能才能有效地应对全新的、前所未有的问题。

运动技能和触觉等复杂感官能够显著受益于真实的物理交互或高度逼真的虚拟模拟，它们能够提供纯虚拟输入无法完全复制的关键情境。因此，这意味着，如果我们想要在人工智能中实现真正类似人类的认知，就离不开机器人技术；通过机器人系统或高度先进的模拟技术，将实体化是迈向真正理解和通用智能的关键一步。

混合架构方法

另一个悬而未决的问题是，单靠去中心化架构是否能够完全实现持续学习，或者将去中心化和中心化元素相结合的混合架构是否更有效。受“千脑理论”的启发，我们可以想象无数个人工智能模块，类似于大脑皮层柱，独立学习并建模其局部感官输入。同时，一个总体中央系统会将这些局部模型整合成一个统一的理解，在全球范围内协调响应和决策。

这种混合方法可以在局部灵活性和全局一致性之间提供必要的平衡，为人工智能提供持续学习所需的稳健性，而不会忘记过去的经验。

结论与展望

实现通用人工智能可能需要从根本上转向受人脑过程启发的去中心化、持续学习模型。稳定一致的参考框架，结合平衡去中心化局部学习和集中式全局协调的混合架构，为实现通用人工智能 (AGI) 提供了充满希望的途径。在这些原则的指导下，未来的发展或许最终能够弥合当前的差距，使人工智能能够真正像人类一样思考和学习。

如今，人工智能系统已经达到了成熟的水平，足以在组织内部广泛应用——这不仅可以提高效率，还可以扩展现有的商业模式，甚至创造前所未有的全新机遇，带来巨大的附加值。事实上，如果真正的通用人工智能（AGI）需要更长的时间才能出现，这对大多数公司来说可能是有利的，因为它可能会迅速颠覆现有的商业模式。

在此之前，我建议各组织积极利用当前的人工智能技术，尤其是基于代理的系统，来实现复杂工作流程的自动化，并确保竞争优势。理想情况下，他们应该以创新的方式优化和发展其商业模式，以至于即使有了通用人工智能 (AGI)，复制这些模式也会变得困难或缺乏经济吸引力。

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Artificial intelligence (AI) has made remarkable strides in recent years. Language models generate texts, image recognition systems create photorealistic visuals, and machines master pattern recognition tasks at impressive speeds. However, despite these advancements, AI has yet to achieve what makes human intelligence truly remarkable: continuous, lifelong learning and the ability to generalize from experience. This is known as artificial general intelligence (AGI).

My central hypothesis is that true AGI can only be achieved when AI learns continuously, flexibly adapting its understanding in real time rather than relying solely on large-scale, one-time training sessions. Humans naturally engage in constant learning, updating their knowledge and understanding based on every new interaction with their surroundings. Current AI systems typically do not possess this capability.

The "Thousand Brains" Theory proposed by Jeff Hawkins—computer scientist, neuroscientist and engineer—provides valuable insights into achieving this kind of continuous learning. According to Hawkins, the brain operates as a network of numerous small, decentralized units called cortical columns. Each column independently creates its own "miniature model" of reality by making predictions, comparing them to actual sensory inputs and continually updating based on discrepancies. The decentralized structure allows robust, adaptable and continuous learning, effectively preventing the catastrophic forgetting frequently encountered by centralized neural network approaches.

Why Current AI Systems Fall Short

Today, most AI systems rely heavily on extensive initial training (pre-training) and remain static afterward. Unlike humans, these systems do not adapt continuously to new situations or incorporate new information in real time. Consequently, AI systems often struggle to apply knowledge flexibly to unforeseen tasks or contexts.

My argument is that AGI can only be achieved by creating AI that can continuously learn, adjust and retain knowledge throughout its operational lifetime. Implementing a decentralized, modular approach inspired by the Thousand Brains Theory might help solve these issues by allowing AI to dynamically integrate new experiences while preserving previously learned knowledge.

Why Reference Frames Are Essential For True Cognition

Continuous learning alone is insufficient. It requires a crucial component: stable reference frames that integrate sensory inputs. For humans, the primary reference frame is our body. Consider recognizing a coffee cup: Visually identifying it alone is incomplete. Only when we physically touch it, feeling its shape and weight, can we truly understand and form a coherent internal representation. Each sensory input—visual, tactile and motor—is positioned within a shared, stable context provided by our physical form.

For AI to develop similarly sophisticated cognitive abilities, it must also employ clear and consistent reference frames. These reference frames are essential because they enable AI to integrate diverse sensory inputs into coherent mental representations, similar to how humans interpret sensory data through their bodies. This approach is closely linked to the concept of world models, where an AI first needs to deeply understand and internalize the characteristics and relationships of various objects and concepts. Only after creating such stable, integrated models can AI effectively tackle completely novel, previously unseen problems.

Complex senses like motor skills and haptics significantly benefit from actual physical interaction or highly realistic virtual simulations, providing critical context that purely virtual inputs may not fully replicate. Consequently, this implies we cannot bypass robotics if we aim to achieve truly human-like cognition in AI; physical embodiment, through robotic systems or highly advanced simulations, is an essential step toward developing genuine understanding and general intelligence.

A Hybrid Architectural Approach

Another open question is whether decentralized architectures alone can fully realize continuous learning or if a hybrid structure, combining decentralized and centralized elements, might be more effective. Drawing inspiration from the Thousand Brains Theory, one can imagine numerous AI modules, analogous to cortical columns, independently learning and modeling their local sensory inputs. Simultaneously, an overarching central system would consolidate these localized models into a cohesive understanding, coordinating responses and decisions on a global scale.

This hybrid approach could offer the necessary balance between local flexibility and global coherence, providing AI with the robustness required to continuously learn without forgetting past experiences.

Conclusion And Outlook

Realizing artificial general intelligence will likely demand a fundamental shift toward decentralized, continuous learning models inspired by human brain processes. Stable and coherent reference frames, combined with hybrid architectures balancing decentralized local learning and centralized global coordination, offer promising pathways toward AGI. Future developments guided by these principles might ultimately bridge the current gap, enabling AI to genuinely think and learn like a human.

Today's AI systems have already reached a maturity level sufficient for broad adoption within organizations—not just to increase efficiency but also to expand existing business models or even create entirely new opportunities that were previously unattainable, delivering tremendous added value. In fact, it could be beneficial for most companies if true AGI takes more time to emerge, as it might rapidly disrupt established business models.

Until then, I suggest organizations proactively leverage current AI technologies, particularly agent-based systems, to automate complex workflows and secure competitive advantages. Ideally, they should optimize and evolve their business models in such innovative ways that replicating them, even with AGI, becomes challenging or economically unattractive.

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