Introduction

• Biological systems are self-constructing, multi-scale agents navigating diverse problem spaces, not just chemical reactions or pre-programmed computational models.
• Traditional binary distinctions (living vs. machine, natural vs. artificial) are blurring due to bioengineering and a continuum of life forms.
• Levin’s framework aims to recognize, create, and relate to diverse intelligences, regardless of origin (biological, AI, synthetic).
• A spectrum of “persuadability” exists – from hardware modification to training – for interacting with different agents, which needs experimental evaluation.

Biological Substrate as “Agential Material”

• Life scales cognition; it doesn’t arise magically. From a single cell (oocyte) to complex organisms, there’s a gradual increase in cognitive capabilities.
• We are all collective intelligences. Even a “unitary” organ like the pineal gland is made of many cells, each with internal complex processes.
• Single cells (e.g., *Lacrymaria*) demonstrate surprising problem-solving and learning abilities without nervous systems, highlighting inherent cellular competency.
• Biological systems exhibit a multi-scale competency architecture. Molecules, cells, tissues, organs, and swarms *all* solve problems in their respective spaces.
• Life navigates many “spaces” beyond physical 3D: gene expression, physiological states, anatomical “morphospace” (the space of possible body forms). Focus of this video is Morphospace.

Morphogenesis as Problem Solving

• Development (morphogenesis) isn’t just reliable; it’s actively *problem-solving*. Embryos adapt to perturbations (like being cut in half) to achieve correct forms.
• Regeneration (e.g., axolotls) demonstrates this adaptability. Cells regrow lost structures and “stop” when the correct anatomical pattern is achieved (anatomical homeostasis).
• Picasso frogs, with scrambled facial features, show that development isn’t hardwired. Organs move along *novel* paths to reach correct positions, exhibiting context-sensitive adaptation.
• Bioelectricity is a “cognitive glue” linking cells toward collective morphogenetic goals.

Bioelectric Communication and Control

• Number of individuals (embryos) in an early blastoderm can vary. It is not predetermined. Alignment and work (towards development) of individuals determines the final state, not pre-programmed count/data/instructions in the DNA.
• Life emphasizes “saliency” over information fidelity. Metamorphosis (caterpillar to butterfly) demonstrates remapping, not just storing, of information; memories are reinterpreted.
• “Bowtie” architectures, common in biological networks (chemical, biomechanical, bioelectric), force generalization and creative reinterpretation due to information bottlenecks.
• Creatures constantly reinterpret their current state and memory engrams due to the inherent unreliability of the biological medium (noise, plasticity). Past is available for reference, but there is heavy emphasis on new interpretations from present data.
• Development is a type of de novo, from new, every moment, of problem solving. Organisms reuse existing molecular tools in novel ways (cell communication vs. cytoskeletal bending in newt kidney tubules) to achieve desired anatomical outcomes.
• Cells within networks exhibit “pattern completion” abilities and have setpoints.
• Cancer is a failure of cells to adhere to the larger, collective goal, reverting to small individual goals. Disconnect from group electrical communication means their sense of “self” contracts to the size of just that cell.
• Every cell, not only neural, use electricity to communicate. Like nerons, they use voltage gradients, ion channels, and gap junctions, a principle exploited from very beginning (bacterial times).
• Bioelectric pre-patterns guide organ formation, a “electric face”, a map or pattern before anatomy appears. These can be manipulated to alter morphology.
• Modifying bioelectric patterns (by controlling ion channels/gap junctions, like neural synaptic plasticity) can induce organ growth (eyes in tadpole guts), respecify body plans (two-headed planaria), or change plarania’s head shapes into those that resemble that of different plarania species..
• Altered biolerical state can be perminant (plarania growing two heads for rest of it’s lives, generation after generation)
• Evolved structures can represent “latent spaces”, spaces that are accessabile, despite not being the default state, where they can go given the correct stimulation (in this case, bioelectrical, and chemical was mentioned with reference to gall wasps)

Emergent Capabilities and Future Directions

• Wasp galls exemplify a new capacity in a given organism. Acorns can be made to grow very very different strcutures from acorn with signals from gall wasps. This new shape would never normally happen from the original.
• Anthrobots (human tracheal cells forming novel structures) and xenobots (frog cells forming self-replicating organisms) demonstrate surprising, emergent capabilities *not* dictated by selection or human design.
• These emergent capabilities may arise from an “external component” a *latent space* of possibilities beyond specific genes or algorithms, which biology (and future bioengineers) can explore.
• Living systems are incredibly plastic. Any combination of evolved/engineered material and software can potentially form an “agent.”
• A field of “diverse intelligence” is crucial to understand, relate to, and ethically interact with these novel beings and their unconventional minds (“synth biosis”).
• Even sorting algorithms can have simple cognitivies behaviors. Life is more cognitive than it’s been preveious recognized or defined by.
• Goal: Develop principled frameworks for recognizing and interacting with diverse minds by overcoming our own evolutionary biases, using AI as translators.
• There exists company-interest connections.

导言

• 生物系统是自构建的、多尺度的智能体，在各种问题空间中穿梭，而不仅仅是化学反应或预编程的计算模型。
• 由于生物工程和生命形式的连续性，传统的二元区分（生命与机器，自然与人工）正在变得模糊。
• 莱文的框架旨在识别、创建和关联各种智能，无论其起源如何（生物的、人工智能的、合成的）。
• 存在一个“可说服性”的频谱——从硬件修改到训练——用于与不同的智能体交互，这需要实验评估。

生物基质作为“自主材料”

• 生命扩展认知；它不是凭空出现的。从单个细胞（卵母细胞）到复杂的生物体，认知能力逐渐增加。
• 我们都是集体智慧。即使是像松果体这样的“单一”器官也是由许多细胞组成的，每个细胞都有复杂的内部过程。
• 单细胞（例如，*泪腺虫*）在没有神经系统的情况下表现出令人惊讶的解决问题和学习能力，突出了细胞固有的能力。
• 生物系统表现出多尺度能力架构。分子、细胞、组织、器官和群体*都*在各自的空间中解决问题。
• 生命在物理3D之外的许多“空间”中航行：基因表达、生理状态、解剖“形态空间”（可能的身体形式的空间）。本视频的重点是形态空间。

形态发生作为问题解决

• 发育（形态发生）不仅仅是可靠的；它是积极地*解决问题*。胚胎适应扰动（如被切成两半）以实现正确的形式。
• 再生（例如，蝾螈）证明了这种适应性。细胞再生失去的结构，并在实现正确的解剖模式时“停止”（解剖稳态）。
• 毕加索青蛙，具有杂乱的面部特征，表明发育不是硬连接的。器官沿着*新*的路径移动以到达正确的位置，表现出上下文敏感的适应。
• 生物电是一种“认知胶水”，将细胞连接起来以实现集体的形态发生目标。

生物电通讯与控制

• 早期胚盘中的个体（胚胎）数量可能会有所不同。它不是预先确定的。个体的对齐和工作（朝着发育）决定最终状态，而不是DNA中预编程的计数/数据/指令。
• 生命强调“显著性”而不是信息保真度。变态（毛毛虫到蝴蝶）展示了信息的重新映射，而不仅仅是存储；记忆被重新解释。
• “蝴蝶结”架构，在生物网络（化学、生物力学、生物电）中很常见，由于信息瓶颈，强制泛化和创造性的重新解释。
• 由于生物介质固有的不可靠性（噪声、可塑性），生物不断地重新解释其当前状态和记忆印记。过去可供参考，但非常强调来自当前数据的新解释。
• 发育是一种从新的每一刻开始的解决问题的类型。生物体以新的方式重用现有的分子工具（新蝾螈肾小管中的细胞通讯与细胞骨架弯曲）以实现所需的解剖结果。
• 网络内的细胞表现出“模式补全”能力并具有设定点。
• 癌症是细胞未能坚持更大的集体目标的失败，恢复到小的个人目标。与群体电通讯断开意味着他们的“自我”意识缩小到仅仅那个细胞的大小。
• 每个细胞，不仅仅是神经元，都使用电来通讯。像神经元一样，它们使用电压梯度、离子通道和间隙连接，这是从一开始（细菌时代）就利用的原理。
• 生物电预模式指导器官形成，“电面”，解剖结构出现之前的地图或模式。这些可以被操纵以改变形态。
• 修改生物电模式（通过控制离子通道/间隙连接，如神经突触可塑性）可以诱导器官生长（蝌蚪肠道中的眼睛），重新指定身体计划（双头涡虫），或将涡虫的头部形状改变为类似于不同涡虫物种的头部形状。
• 改变的生物电状态可以是永久的（涡虫在余生中生长出两个头，一代又一代）。
• 进化结构可以代表“潜空间”，即可访问的空间，尽管不是默认状态，它们可以在给定正确刺激（在这种情况下，是生物电，并且提到了化学与瘿蜂的关系）的情况下到达。

涌现能力和未来方向

• 瘿蜂瘿体现了给定生物体的新能力。橡子可以被制成与来自瘿蜂的信号一起生长的非常不同的结构，这些结构不同于正常的橡子。这种新形状通常永远不会从原始橡子中发生。
• 人源类器官（人支气管细胞形成新结构）和异种机器人（青蛙细胞形成自复制生物体）表现出令人惊讶的、涌现的能力，*不是*由选择或人类设计决定的。
• 这些涌现的能力可能来自于一个“外部组件”，一个超越特定基因或算法的可能性的*潜空间*，生物学（和未来的生物工程师）可以探索这些空间。
• 生命系统具有惊人的可塑性。进化/工程材料和软件的任何组合都可能形成一个“智能体”。
• 一个“多样化智能”领域对于理解、关联和与这些新型生物体及其非常规思维（“合成共生”）进行道德互动至关重要。
• 即使是排序算法也可以具有简单的认知行为。生命比以前公认或定义的更具认知性。
• 目标：通过克服我们自身的进化偏见，使用人工智能作为翻译器，开发识别和与各种思维互动的原则性框架。
• 存在公司利益关联。