Introduction: A New Framework for Understanding Life

• Levin’s approach integrates developmental biology, computer science, and philosophy to study agency, memory, and problem-solving in living systems. He proposes that it drives new discovery, in capabilities for building, communication, and unconvential “agent”-like constructs.
• Focuses on “navigating problem spaces” as a key characteristic of life, allowing us to understand unconventional agents (cells, swarms, AI, etc.) using this characteristic as an invariant to compare them.
• Primary example: collective intelligence of cells navigating anatomical “morphospace” (the space of possible body forms).
• Electrical networks act as a “protocognitive medium,” enabling cells to solve problems in anatomical space, impacting, bioengineering and, biomedicine.
• End-goal explores creation of synthetic living beings to understand the origins of novel goals.

Beyond Discrete Natural Kinds: A Continuum of Agency

• Challenges the traditional view of distinct biological “kinds” (like Adam naming animals), arguing for a continuum of forms and agency, from single cells to humans, citing Darwin (Evolution).
• Biotechnology and engineering further blur these lines, creating hybrids and chimeras, which requires new Frameworks to fit them in.
• Framework needed to think about diverse agents: primates, birds, colonial organisms, synthetic life, AI, and even potential exobiological entities, which frameworks from rosenbluether and bigalow are considered..
• Introduces “continuum of persuadability,” an engineering-focused approach: how systems change. Simple systems need rewiring; complex ones respond to experiences (dog training), reasons. Levin wants it not as just a philosophical concept but one used to directly impact research (where Modern bio, thinks “cells”, but should do testing).
• Our development. We originate as a single, quiescent cell (“just physics”) but gradually transform into beings with psychological characteristics, going to state he “hates that phrase”. There is No single “spot”, even the unified singular intelligence deart spoke of for humans is actually decentralized collections of cells..
• Therefore. We are not singular, centralized minds; but. “collective intelligences” built of “agential material” (cells with their own “agendas”). Examples of, cellular decision making. Single-celled organisms competently handle needs without brains/nervous systems, and Caterpillars radically change (metamorphosis) but retain some memories, and planaria regenerate and their “cut” tails grows an original, retaining learned info.
• Multi-Scale Competency Architecture: every biological level (cells, tissues, organs) has problem-solving capabilities in different “spaces” (transcriptional, physiological, anatomical). Human perception biased towards 3D; we might directly perceive complex physiological spaces if we had the right sensors, as cells do it internally.

Morphogenesis as Problem Solving: Turing’s Insight

• Highlights Alan Turing’s interest in both computation/intelligence and morphogenesis (the development of form). Turing recognized this as 1) using different substraces, and 2) origins in chemical system (he researched).
• Anatomical morphospace: the complex order of the body (organs, tissues) emerges from embryonic cells, yet the genome only encodes *protein* sequences, *not* a blueprint. Therefore Morphogenesis is a “software” problem. How cell groups collectively “decide” what to build. This information not written, but, emegeres, which is what Regenerative Medicing tries to understand.
• Goals: how cell groups “know” what to make and when to stop (regeneration). As Engineers: exploring possibilities, which may mean creating a novel form of similar cells.
• “Anatomical Compiler” (long-term goal): translate a desired shape into stimuli that guide cells to build it, with implication sin medicine and development/control, that wouldn’t need a printer to 3d assemble it.
• The lack of an Anatomical compiler is Because Molecular biology “stuck” at hardware level (DNA editing, protein engineering), neglecting “software of life” (cellular intelligence, problem-solving).
• Intelligence redefined (William James): reaching the same goal by different means, from Magnets unable to, and romeo and julia capable, of planning. A continuum. Requires *perturbation* (obstacles), not just observation.

Anatomical Homeostasis and Bioelectric Memory

• The observation that. Development is reliable (normal embryos become normal organisms), is a start. However, embryos cut into pieces (twins) still form complete organisms. This suggests: *regulative development*: reaching the same anatomical goal by different paths.
• Amphibians (e.g., salamanders) regenerate throughout life; even in humans/mammals it partially exists (livers, fingertips, deer antlers), so it shows this ability to “self repair”, is not “gone”.
• Example in Kidney tubules. Cells with *more* genetic material become *larger*, but tubule *number* adjusts to maintain lumen size, and even Single *gigantic* cells can bend around themselves. All for: Large Scale Goal, to build correctly, not just a single static solution.
• Challenges “feed-forward emergence”. There’s anatomical homeostasis *with feedback loops* (genetic and physical) guiding development back to “target morphology” and taking a differnet path as needed.
• Contrast: Typical feedback loops (temperature, pH) have *scalar* set points, while. Anatomical homeostasis needs a *shape descriptor* (more information-rich). and Challenges: discouragement of “goal-directed” thinking in biology (anthropomorphism).
• Claim: If anatomical homeostasis exists, we should be able to change the “set point” (desired shape) *without* rewiring the system at the molecular level. This involves a need to finding/encoding, decoding, and being able to rewrite setpoints, the memories, etc.
• Need: Cognitive “glue” to combine: the collective. Neuroscience examples where, neurons collectively for a lever association, despite the experience being spread between foot-touch and food reward in disparate neurons.
• Bioelectricity is the “glue” in nervous systems: ion channels create voltage gradients, forming networks for computation and memory. Not specific: Electrical properties, is ancient evolved *before* brains.
• Proposes: “decoding” body’s collective intelligence by reading/interpreting bioelectric information (“mind of the body”). An anology that Brain : Commands Muscle, and Electrical newtoks gives command.

Reading and Rewriting the “Mind of the Body”

• Voltage-reporting fluorescent dyes to visualize electrical communication between cells (“electric conversations” in embryos), along side quantitative Simulatirs.
• Example: “electric face” in frog embryos, a bioelectric *pre-pattern* predicting future organ placement. Pathological, Enogen inducing cells that detach Electrically, so no communication.
• *Rewriting* bioelectricity is, “intervening” directly, key: no applied fields/magnets/waves, modulating cells’ *native* ion channel “keyboard.”, By Modulating, optogentics and light, mutations of junctions. Not just a *readout*, but the *set point* for anatomical development.
• Claim tested by *changing* the electric face (frog embryos) creating *ectopic eyes* in gut regions by injecting mRNA for ion channels. Lessons:.
  • Bioelectricity is *instructive* (not just toxicity; triggers specific organs). Modularity.low-information signal (“make an eye here”) triggers complex processes; Competency. Only neurectoderm *thought* competent to make eyes (with pax6). Bioelectricity shows *all* cells can. “Bio-prompting” (analogy to AI), Scaling. injected cells *recruit* neighboring cells.
• These results are applied to Regenerative medicine applications. non-regenerating frog legs, a bioelectric “cocktail” triggers regeneration with long “delay,” therefore showing that early-communication set goal, rather than direct command to build.

Planaria: Bioelectric Patterns as Pattern Memories

• Planaria: robust regenerators, “immortal,” noisy genome. 1. Cut: How the fragments “knows” how many heads.
• Electrical circuit controls head number; targeting it creates two-headed worms, which has Bioelectric pre-pattern showing 1 head, “until you injure”.
• Critical. The *bioelectric pattern is *not* a *map* of the two-headed animal, but of the *normal* one. A *memory* that can be *edited*, storing 2 representations, despite one single, singular-headed animal. “Simple CounterFactual.”
• Recutting: shows permenant, forever, lasting change as: Memory (long-term, rewritable, conditional recall). Not: genome, with shows Normal Genome..
• Shapes not limited to “number of heads”: Controls head *shape*. Confusing the bioelectric network in a triangular-headed species can produce *different* head shapes, as: The cells can reaccess stored information of old shapes..
• Further exploration of “latent morphospace”: making planaria with different symmetries, hybrid forms, or “spiky” forms – all with normal cells and Genes.
• Implication of latent space: there’s a large room for error, for cells. Others exploit these cells: Wasp, leaf example, with leaf cell “hack,” for nests. No way to see it on genes, since most time: its flat.
• Challenge. Full “stack” understanding, connecting. Hardware. To: algorithms.
• Behavioral Science, where you train cells, is Complementary: to biomedical approach. The controversial idea, for this research is. “Somatic psychiatry:” communicate to, cells.

Xenobots and the Origins of Goals

• Changing Size and the origins: Goals of biological systems usually attributed to *evolution*. “Cognitive light cone” (analogous to spacetime diagrams). Size of biggest goal, a system. Ticks, bacteria: small. Dogs: bigger. Humans: very large. Different types of organisms.
• Agents: We are *composed* of agents (cells, organs) with *different* sized cognitive cones, cooperating *and* competing, which changed. Evolutionary failure Mode = “Cancer,” where cancer cells detach from communication, “and have Smaller, selfs”. Not selfish.
• Implication to the change: that Cancer does not necessarily needs death. Xenobots: made from *frog skin cells* (*not* embryonic stem cells). Ask, “what if they had no borders”.
• Xenobot Results. Spontaneous formation and self-organize, they swim by hairs, *novel* behaviors, navigate, spontaneous actions, signal, kinematic self-replication: assemble “children”. Never happened, *before*, which means evolution never required it to happen before, as new trait. Therefore this suggest, evolution, doesnt *just* produce “things”.
• Interspecies compatability/compatibility of all these types: Living beings can interoperable with other types of living constructs to: create, agential, types of: agents. This means. Ethical questions on “agents”.

Questions, with Answer Highlights (simplified and paraphrased as short as possible):

• Plants?. Absolutely: they have Intelligence and fit in.
• AI/Robotics and bio cells?. Yes: exciting, feedback and use for synergy, using better alogirthms for biology to be capable of using them, due to it: helping machines, to understand biology..
• Source of collective Intelligence?. We know parts: Need connections, Memory-wiping. We *don’t* yet predict *specific goals* of collectives.
• Tumors injectiion showed clutser, malignacies don’t. *Induced*, by engonogen. Disconnect. They convincetheir neighbots. *Then*, reconnect.
• How long for liver replacement?. Can’t give. In a Lifetime (Frog-> Mice,).
• Macroscopic Control (exerted on frog)? we, can, change stuff “permenantly”. *Goal*: is to science.
• Consider part? Dead/Other. If other, use them, yes: include..
• Tutura?, what is?. “living fossil” rapid DNA evolution. Interesting, no prior knowledge of creature before Q+A.

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简介：理解生命的新框架

• Levin 的研究方法将发育生物学、计算机科学和哲学相结合，探讨生命系统中的能动性、记忆以及问题解决能力。他认为，这种方法能推动在构建、沟通以及非常规“类主体”结构方面的全新发现。
• 他强调把“在问题空间中导航”视作生命的关键特征，因而可以用这个特征来比较和理解非常规主体（例如细胞、群体、人工智能等）。
• 主要示例：细胞在解剖“形态空间”（可能身体形态的空间）中导航时所展现的集体智能。
• 电网络充当一种“原认知媒介”，使细胞能在解剖空间中解决问题，并对生物工程和生物医学领域产生影响。
• 最终目标包括探索合成生命体的创造，以了解新目标的起源。

超越离散的自然种类：能动性的连续体

• 对传统的生物“种类”观念（如亚当为动物命名）提出质疑，主张从单个细胞到人类的各种生命形态和能动性呈现连续体，借鉴达尔文的进化理论。
• 生物技术和工程进一步模糊了这些界限，创造了各种嵌合体和混合体，需要新的框架去容纳这些形态。
• 必须构建一种可思考多种不同主体的框架：从灵长类、鸟类、群体性生物、合成生命体、人工智能，甚至可能的外星生物。引用 Rosenbluether 和 Bigalow 的理论。
• 提出“可说服性连续体”这一面向工程的方法：关注系统是如何改变的。简单系统需要“重新布线”，复杂系统则会根据经验（如训练狗）或理由来作出反应。Levin 希望这不仅是哲学概念，也能在实际研究中使用（现代生物学倾向将“细胞”视作对象，但应进一步进行测试）。
• 人的发育过程表明：我们起初是一个“只是物理”的安静单细胞，随后逐渐发展出心理特征；并没有一个明确的时间点在此发生飞跃。即使对人来说，看似统一的单一“心智”其实是去中心化的细胞集合。
• 因此，我们并非单一、集中的思维实体，而是由具有各自“目的”的细胞构成的“集体智能”。例如单细胞生物无需神经系统即可胜任各自需求；毛毛虫经历彻底变形（化蛹成蝶）但保留了一些记忆；三角涡虫（planaria）可以再生，它们被切断的尾巴能长回原体并保留学习过的信息。
• 多尺度能力结构：在不同层级（细胞、组织、器官）中，生物都能在各自“空间”（转录、生理、解剖）里解决问题。人类的感知偏向三维，如果能拥有更合适的感官，也许就能直接感知细胞在内部处理复杂生理空间的方式。

将形态发生视作问题解决：图灵的洞见

• 强调艾伦·图灵（Alan Turing）对计算/智能和形态发生（形态发展）的共同兴趣。图灵意识到可在不同基质中研究此现象，并从化学系统的角度切入。
• 解剖形态空间：器官和组织的复杂有序来自胚胎细胞，而基因组仅编码蛋白序列而并非蓝图，所以形态发生更像是一个“软件”层面的问题——细胞群如何集体“决定”构建什么结构。再生医学便在探究这种涌现机制。
• 研究目标：弄清细胞群如何“知道”要形成什么，以及何时停止（如再生）。工程视角：可能通过相同的细胞创造新形态。
• “解剖编译器”（长期目标）：将想要的形状翻译成对细胞的刺激，让它们构建所需的组织，而无需 3D 打印或其它装配。
• 缺乏“解剖编译器”的原因在于分子生物学仍停留在硬件层面（DNA 编辑、蛋白工程），忽略了生命“软件”层面（细胞智能与问题解决）。
• 威廉·詹姆斯（William James）的智能重定义：能通过不同手段达到同一目标。磁铁无法改变行为，但罗密欧和朱丽叶可以计划。智能呈现连续体，需要看其在受到阻碍时如何应对，而不仅是观察。

解剖稳态与生物电记忆

• 胚胎发育通常十分稳定（正常胚胎会发育成正常生物），但切割后的胚胎片段依旧能形成完整个体，体现出“调节式发育”：以多种途径实现同一解剖目标。
• 两栖类（如蝾螈）终生具备再生能力；人类和哺乳动物也部分保留了这一特性（如肝脏、指尖、鹿角的再生），说明自我修复的能力并未彻底丧失。
• 肾脏小管的例子：具有更多遗传物质的细胞会变大，但小管数量会相应调整以维持管腔大小，甚至单个巨细胞也能自我折叠，以完成大尺度目标（维持器官结构）。
• 这挑战了“自下而上的一次性涌现”。形态稳态包含基因与物理的反馈回路，引导发育回归“目标形态”，并在需要时通过不同路径达到该形态。
• 与典型的标量反馈回路（如温度、pH 值）不同，解剖稳态需要一个关于形状的更丰富信息描述。然而，生物学界常常对“目标导向”这类思路持抗拒态度（担心拟人化）。
• 若解剖稳态确实存在，我们应能在不重新布线分子层面的情况下改变其“设定点”（目标形态）。这需要能找到并读写对应的编码或记忆。
• 我们需要某种“认知黏合剂”让集体生物体结合在一起。类比神经科学：神经元分散在身体各处，却能形成统一的学习或记忆。
• 生物电就是神经系统中的“黏合剂”：离子通道营造电压梯度，形成可进行计算和记忆的网络。这种特性并不限于神经元，它是古老的进化成果，早在人类大脑出现之前就已存在。
• Levin 提议通过“解读”生物电信息（“身体的思维”）来了解机体的集体智能。类比大脑指挥肌肉，生物电网络指挥发育或再生。

读取与重写“身体的思维”

• 利用荧光染料来报告电压，可视化细胞间的电信号交流（“胚胎中的电对话”），并搭配定量模拟工具。
• 示例：“电面孔”在青蛙胚胎中作为生物电预模式，预示未来器官的位置；若出现病理状态（如致癌基因），细胞会在电平面上脱离通信。
• *重写*生物电：通过直接干预细胞的本底离子通道（“键盘”），而非施加外部场/磁/波。比如使用光遗传学或突变调控离子通道，不仅可读取，还能重设形态发展的“设定值”。
• 用修改电面孔的方法（在青蛙胚胎注入离子通道 mRNA）成功在肠道等部位诱导出眼睛。说明：
  • 生物电具有*指导性*（不仅是毒性或干扰，而能触发特定器官形成）。
  • 具有模块性：低信息量信号（“在这里生成眼睛”）可激发复杂过程。
  • 细胞的能力：此前认为只有神经外胚层才“有资格”生成眼睛（需要 pax6 基因等），但生物电修饰后，所有细胞都能。可视为“生物提示”（类似 AI 提示）。
  • 可扩展性：被注入的细胞会“招募”邻近细胞。
• 这些发现应用于再生医学。例如在不具备再生能力的青蛙腿上使用生物电“配方”触发再生，虽然中间延时很长，也表明了早期信号可以确立“目标”，而非逐步手把手指挥构建。

涡虫：作为模式记忆的生物电模式

• 三角涡虫：再生能力极强，被称为“不死生物”，基因组也存在噪音。问题在于切割后各部分如何“知道”要长多少个头。
• 有研究显示头部数量受电路控制；若靶向这个电路，就能生成双头涡虫。原本的生物电预模式显示的是“一个头”，直到外伤时才启动。
• 关键：*生物电模式并不直接显示“双头”的方案，而是一个*正常*个体的模式。只有被改写后才储存了双头的方案，表现出“一种简单的反事实”。
• 反复切割后依旧保持这种双头特性，证明这是一种长久、可重写、可在特定条件下被再次调用的记忆，而非基因层面的变化（基因仍然是正常的）。
• 改变的并非仅仅是“头的数量”，还包括头部形状。通过扰乱生物电网络，能让原本呈三角形头部的物种形成其他头型；此过程可视为细胞调取了“曾经的形态信息”。
• 更进一步地挖掘“潜在形态空间”：可以造出具有不同对称轴、混合特征、甚至“尖刺”形状的涡虫——而它们的细胞和基因均完全正常。
• “潜在形态空间”暗示了细胞在正常情况下无法轻易呈现的大量潜在形态。自然界中有寄生蜂“劫持”植物细胞为其筑巢，也是类似的“黑客”行为——基因上并无记录，但在某些情况下就会出现。
• 难点在于理解从硬件到算法的完整层次结构。
• 行为学研究（如“训练”细胞）可与传统生物医学方法互补。Levin 将其称为“身体的精神病学”（somatic psychiatry），即与细胞进行对话。

Xenobots 与目标的起源

• 关于生物系统目标的来源：通常归功于演化过程。Levin 提出“认知光锥”概念，类似时空图，用于描述一个系统可处理的最大目标范围：如细菌或蜱虫范围更小，狗更大，人类更大。
• 个体由不同层级、不同认知光锥大小的单元（细胞、器官）构成，既有合作，也有竞争。癌症可被视为进化失败的模式：癌细胞脱离通信，认知范围缩小为“自身”。
• 此转变带来的启示：癌症并不一定是宿命性的死亡结局。
• Xenobots（“青蛙机器人”）由青蛙皮肤细胞（不是胚胎干细胞）制造，探讨如果细胞不受组织边界束缚会如何表现。
• Xenobots 的研究结果：这些细胞会自发形成结构，通过表面纤毛游动，展现出全新的行为模式，包括导航、随机动作、信号交流，以及“运动式自我复制”（收集并组装“子代”）。这是在此前的进化过程中从未被要求过的特性，却自然出现，说明进化的潜力远超我们对已有形态的认知。
• 不同物种间可能存在更深层的兼容性，能够组建全新的“主体”形态，这引发对“代理人”概念的伦理思考。

问答（简要精炼版）：

• 植物？——是的，它们具有智能并可纳入此框架。
• 人工智能/机器人与生物细胞？——可以结合，产生正向反馈；我们可用更优算法帮助生物研究，也能用生物启示改进机器。
• 集体智能的来源？——我们只了解部分：需要有连接、有记忆的持久性。目前尚不能准确预测集体会产生什么具体目标。
• 曾对肿瘤注射——结果显示，恶性肿瘤常见于断联失调；有时能被“说服”重新连接。
• 多久能替换肝脏？——无法给出明确期限，但研究正逐步推进，如（青蛙→小鼠）。
• 宏观层面干预（例如在青蛙身上）？——是的，我们可以永久性地改变某些特征，但最终目标在于科学研究。
• 若涉及死亡或其他？——视具体情况，也可利用额外材料及其他方式。
• 对披针形喙头龟（Tuatara）的看法？——它是“活化石”，DNA 演化速度快。Q&A 并未透露更多细节。