Introduction: The Journey from Physics to Mind

• All life begins as simple physics (e.g., an oocyte – egg cell), gradually becoming complex, even achieving metacognition. This transition across the “Cartesian cut” is a core question.
• Turing was also incredibly interested in biological development/creating shapes. Levin says that his work is linking/the-same-as intelligence and the bodes’ self-assembly.
• All intelligences are *collective* intelligences, made of parts (cells, etc.). Even the human brain is a vast collection of interacting components. Single-celled organisms (like Lacrymaria) show impressive competence at small-scale goals.

Multi-Scale Competency and Biological Plasticity

• Organisms have competence not just in 3D space, but also in other “spaces” like anatomical “morphospace” (the space of possible body shapes).
• Caterpillar to Butterfly Transformation: Highlights drastic body/brain reorganization while retaining *some* memories, raising fundamental questions about identity and cognitive continuity.
• Planarian Regeneration and Memory: Planaria can regenerate *any* body part, including the brain. Experiments show information storage *outside* the brain, and even transfer of this information to a *newly grown* brain.
• Frog Eye Relocation: Tadpole eyes can be moved to the tail, and the tadpole can *still see*. This demonstrates incredible plasticity and adaptation, challenging assumptions about fixed developmental programs. The optic nerve connects to the spinal cord and not the brain in these tadpoles.
• Multi-Scale Competency Architecture: Biological systems are nested hierarchies (like Russian dolls). Each layer solves problems in its own space (transcriptional, physiological, anatomical). Intelligence is the ability to reach a goal by *different* means (per William James), not just simple emergence.

Navigating Anatomical Morphospace

• Where do complex anatomies (like a human torso) come from? DNA provides instructions for *cellular hardware* (proteins), but not the *software* that organizes cells into complex structures.
• Picasso Tadpoles can organize into a “correct” face as a tadpole. When the cells that made up the Piccasso tadpole turn into a frog, the frog can now also find this new “correct” organization and will grow according to that new “correct” face.
• Salamander Kidney Tubules: Cells adjust their behavior to create a correctly-sized lumen (tube opening), even if cell size is artificially altered. *One* giant cell can bend to form the lumen, showing top-down causation: a large-scale anatomical goal drives the selection of *different* molecular mechanisms.
• The Brain as a Precedent: The brain maps high-level cognitive goals (in 3D space) onto molecular actions (muscle movement, etc.). Bioelectric networks *outside* the brain do something similar, controlling body configuration in *morphospace*. Evolution pivoted from spatial pattern control to temporal pattern control in neural processing.

Bioelectric Signals and Regenerative Medicine

• Bioelectric networks: predating and analogous to the use-case of neural networks. It controls configuration and acts as a body-configuration throughout the body in its morphospace. Evolution was using Bioelectric Networks, way-way before, it created/pivoted to neural-network-focused cognition.
• Tools to “Read” and “Write” Bioelectric Patterns: Inspired by neuroscience (optogenetics, active inference), these tools allow communication with cell collectives, influencing their “morphogenetic paths”.
• Frog Leg Regeneration: A *single-day* treatment can trigger leg regeneration in frogs (which normally don’t regenerate limbs). This involves convincing cells to embark on a “build a leg” trajectory in morphospace.
• Ectopic Eye Formation: Inducing a specific bioelectrical state can cause cells to build an eye *anywhere* on the body. This isn’t providing full eye-building instructions; it’s a “subroutine call” – “build an eye here.” The cells can even *recruit* neighboring cells to help.
• Electrical map: is how bioelectrical states, that have configurations that represent memories, can show planeria how many heads to grow and other anatomical guidance for new cellular developments. The Genome defines the *hardware*, however.
• Planarian Head Number Control: The bioelectric pattern determining head number can be *rewritten* using ion channel drugs (no external electric fields). This creates two-headed planaria, and this new “memory” (body plan) is *stable* through subsequent regenerations – a counterfactual, latent memory.

Xenobots: Exploring Morphogenetic Goals

• Genome defines what the planarian’s new number of head would be *AFTER* a rewriting via influencing the electrical map.
• Xenobots: Created from dissociated frog skin cells, these self-assemble and exhibit *novel* behaviors (movement, navigation, collective action), including *kinematic self-replication* (building new xenobots from loose cells).
• There’s No “Xenobot” gene, rather, taking away the influence of neighboring cells helps uncover “Xenobots”. This behavior is described as “engineering by subtraction”: The normal, boring life the cells is dictated by its neighboring cells; isolating the cells reveal the default behaviour is being xenobots, and a completely new, never before seen, behaviour is observed.
• Engineered by Subtraction: The “default” behavior of the isolated skin cells is to become xenobots. This reveals hidden morphogenetic potential. No straightforward evolutionary explanation: The evolution pressure didn’t select xenobots, but it made the material/machines, so that if “correct” influences are given to cells (via subtraction), the materials will develop “correctly”.
• Kinematic self-replication: very minimum self-replication, as no real heredity between new ‘generations’, rather a rudimentary type of self-replicating robot, Von-Nuemann type of dream.

Implications and Conclusions

• Biology lacks firm expectations: “you don’t know… how many cells.. what size.. what genetics..” This lack of assumptions leads to evolved material working well, and “doing something adaptive in a wide range of circumstances.”
• Almost any combination of *evolved material*, *designed material*, and *software* can be some kind of *agent* (cyborgs, synthetic beings).
• Future Ethics: Traditional criteria for moral consideration (“What are you made of?” and “How did you get here?”) will be insufficient. We need new ethical frameworks for interacting with diverse forms of intelligence.
• The “spectrum of intelligence” is more interesting than a strict “living/non-living” distinction. Many systems will exhibit degrees of intelligence, blurring traditional boundaries.
• Self-replication, like other biological properties, exists on a *continuum*. Xenobots represent a *minimal* form of self-replication, requiring provided materials and lacking strong heredity.

引言：从物理到意识的旅程

• 所有生命都始于简单的物理学（例如，卵母细胞——卵细胞），逐渐变得复杂，甚至达到元认知。这种跨越“笛卡尔分界”的转变是一个核心问题。
• 图灵对生物发育/创造形状也非常感兴趣。莱文说，他的工作是将智力与身体的自组装联系起来/等同起来。
• 所有智能都是*集体*智能，由部分（细胞等）组成。即使是人类大脑也是一个巨大的相互作用组件的集合。单细胞生物（如泪腺虫）在小规模目标上表现出惊人的能力。

多尺度能力与生物可塑性

• 生物体不仅在三维空间中具有能力，而且在其他“空间”中也具有能力，例如解剖学上的“形态空间”（可能的身体形状空间）。
• 毛毛虫到蝴蝶的转变：突出了剧烈的身体/大脑重组，同时保留了*一些*记忆，提出了关于身份和认知连续性的基本问题。
• 涡虫的再生和记忆：涡虫可以再生*任何*身体部位，包括大脑。实验表明信息存储在*大脑之外*，甚至将这些信息转移到*新生的*大脑。
• 青蛙眼睛的重新定位：蝌蚪的眼睛可以移动到尾巴，蝌蚪*仍然可以看见*。这展示了惊人的可塑性和适应性，挑战了关于固定发育程序的假设。在这些蝌蚪中，视神经连接到脊髓而不是大脑。
• 多尺度能力架构：生物系统是嵌套的层次结构（像俄罗斯套娃）。每一层都在其自身的空间（转录、生理、解剖）中解决问题。智能是通过*不同*方式达到目标的能力（根据威廉·詹姆斯的说法），而不仅仅是简单的涌现。

导航解剖形态空间

• 复杂的解剖结构（如人体躯干）从何而来？ DNA 提供了*细胞硬件*（蛋白质）的指令，但没有提供将细胞组织成复杂结构的*软件*。
• 毕加索蝌蚪可以组织成一个“正确”的蝌蚪脸。当构成毕加索蝌蚪的细胞变成青蛙时，青蛙现在也可以找到这种新的“正确”组织，并将根据新的“正确”脸生长。
• 蝾螈肾小管：即使细胞大小被人为改变，细胞也会调整其行为以创建正确大小的管腔（管口）。*一个*巨大的细胞可以弯曲形成管腔，显示出从上到下的因果关系：一个大的解剖目标驱动选择*不同*的分子机制。
• 大脑作为先例：大脑将高级认知目标（在三维空间中）映射到分子行为（肌肉运动等）。大脑*外部*的生物电网络做类似的事情，控制*形态空间*中的身体构型。进化从神经处理中的空间模式控制转向时间模式控制。

生物电信号与再生医学

• 生物电网络：先于并类似于神经网络的用例。它控制构型，并在其形态空间中充当整个身体的身体构型。进化使用生物电网络，远远早于它创造/转向以神经网络为中心的认知。
• “读取”和“写入”生物电模式的工具：受神经科学（光遗传学、主动推理）的启发，这些工具允许与细胞群体进行通信，影响它们的“形态发生路径”。
• 青蛙腿的再生：*单日*治疗可以触发青蛙（通常不再生四肢）的腿部再生。这涉及说服细胞在形态空间中踏上“构建一条腿”的轨迹。
• 异位眼睛的形成：诱导特定的生物电状态会导致细胞在身体的*任何地方*构建一只眼睛。这并没有提供完整的造眼指令；这是一个“子程序调用”——“在这里造一只眼睛”。这些细胞甚至可以*招募*邻近的细胞来帮忙。
• 电图：是生物电状态（具有代表记忆的配置）如何向涡虫展示生长多少个头以及其他对新细胞发育的解剖指导。然而，基因组定义了*硬件*。
• 涡虫头部数量控制：可以使用离子通道药物（无需外部电场）*重写*决定头部数量的生物电模式。这创造了双头涡虫，这种新的“记忆”（身体计划）在随后的再生中是*稳定*的——一个反事实的、潜伏的记忆。

异种机器人：探索形态发生目标

• 基因组定义了涡虫在通过影响电图进行重写*之后*的新头部数量。
• 异种机器人：由分离的青蛙皮肤细胞创建，这些细胞会自组装并表现出*新*的行为（运动、导航、集体行动），包括*运动学自我复制*（从松散的细胞中构建新的异种机器人）。
• 没有“异种机器人”基因，而是去除邻近细胞的影响有助于发现“异种机器人”。这种行为被描述为“通过减法进行工程设计”：细胞正常的、无聊的生活是由其邻近的细胞决定的；隔离细胞揭示了默认行为是成为异种机器人，并且观察到了一种全新的、前所未见的行为。
• 通过减法进行工程设计：分离的皮肤细胞的“默认”行为是成为异种机器人。这揭示了隐藏的形态发生潜力。没有直接的进化解释：进化压力并没有选择异种机器人，而是制造了材料/机器，因此如果对细胞施加“正确”的影响（通过减法），材料将“正确”地发展。
• 运动学自我复制：非常小的自我复制，因为新的“世代”之间没有真正的遗传，而是一种基本的自我复制机器人类型，冯·诺依曼式的梦想。

影响和结论

• 生物学缺乏坚定的期望：“你不知道……多少细胞……什么大小……什么基因……”这种缺乏假设导致进化材料运行良好，并且“在各种情况下做一些适应性的事情”。
• 几乎任何*进化材料*、*设计材料*和*软件*的组合都可以是某种*主体*（半机械人、合成生物）。
• 未来伦理：传统的道德考虑标准（“你是由什么组成的？”和“你是如何来到这里的？”）将是不够的。我们需要新的伦理框架来与各种形式的智能互动。
• “智能光谱”比严格的“生物/非生物”区分更有趣。许多系统将表现出不同程度的智能，模糊了传统的界限。
• 与其他生物特性一样，自我复制也存在于一个*连续体*中。异种机器人代表了一种*最低限度*的自我复制形式，需要提供的材料并且缺乏强大的遗传性。