Introduction: Rethinking Discrete Categories

• Traditional biology relies on discrete categories (species, individuals), but this breaks down with evolution, development, and future bioengineering. We need a framework for diverse intelligences, regardless of origin or composition.
• Adam naming animals (genesis): name meant knowign their essence; Deep point, which means we must name and “find true name and essence” of the new chimerical types of minds which shall arrise; Discrete categorization that comes from adam (seperate sepeices, even humans) is useless; All future intelligences can blend between software/biology.
• The categories of defining which is which (between different intelligent agent) is going to fall. The idea that there is discreteness of each animal. All biological species blends together along an evolutionarilly and developmentally; humans is not one thing, not a seperate discreet entity from the others, it has developed into being, over time. This idea to go find some magical “line” is gone (gone over the study of evolution); same applies to individual developent (that there is no discreet thing to be considered a ‘thing’); same issue with time going FORWARD as future biological interventions will break and blur that ‘line’ (in terms of engineering)

Scaling of Intelligence: From Cells to Minds

• All organisms, including humans, are collective intelligences: collections of cells cooperating to achieve goals. We are each collective intelligences.
• Development: The journey from a single cell (with “just physics”) to a complex mind is gradual, *not* a sudden jump. There’s no bright line where “mind” appears.
• Cells are “agential material,” not passive like Legos. Cells have their own agendas and problem-solving capacities.
• Unified Intelligence? is questioned, because even our singular brain contains billions of pieces. Even inside the parts inside a Pineal Gland: they contain many, individual, parts. The magic “part” that made Descartes think our conciosness originated was made of countless parts and those, even still, have their own parts, etc.
• Alan Turning studied both computer intellgience and morphogensis. The point that Allen Turing probably had but didn’t quite articulate, that Levin makes here: “the process of intelligence forming and morphogenesis (anatomy of a living creature, from fertilization to full creature) formation is incredibly similar (they follow very very similar lines; perhaps two aspects of the same thing).
• Embryonic “counting”: The number of “selves” in an embryo isn’t fixed by genetics; it’s a dynamic physiological process. Cells “decide” which collective to join.
• Splint Brain studies reveals, like embryonic counting, this phenomenon also occurse *in* the brain.
• Radical metamorphosis (caterpillar to butterfly): Memory persists even when the brain is drastically remodeled. It *generalizes* information (leaf color to “food”).
• Who ownes “knowledge”? (The Lever Pulling Rat: The skin touches lever. the tummy get treats. No ONE cell experiences both, no cells has that knowledge, therefore, the entity which has the knowledge is THE ENTIRE ORGANISM; nervous system as cognative glue.)
• Planarian regeneration: Memory can be stored *outside* the brain and imprinted on a new brain. The target morphology (what to regenerate) can be changed without altering DNA.
• Multi-scale competency architecture: Every level (molecular, cellular, tissue, organ) can solve problems in its own “space” (gene expression, physiology, anatomy).

Morphogenesis as Collective Intelligence Behavior

• Human “Morphospace”: Organisms navigate “morphospace” (the space of possible shapes). Development isn’t just “reliable”; it’s robust and plastic.
• Development isn’t *just* reliable: Salmander examples of growing kidneys/tubule structure correctly despite different genetic abberations; they use different mechanisms to end up witht he *same* structures (a key indicator for intelligence).
• Tadpole/frog facial rearrangments (Scrambling/Piccaso frog example) showed that it *still* can build new organs, despite abberations. The ability to have robust flexibility to “make” new ways (if the typical development pathways have been removed/destroyed/abberated in some ways) *IS* and indicator for a type of “problem solving intelligence”.
• Morphogensis is GOAL ORIENTED PROBLEM SOLVING, *not* simple emergency or “insturctions”.
• Fly patterns (“virtual ants”): Morphogenetic outcomes are not strictly limited by the genome. Other bioengineers (e.g., wasps on oak leaves) can induce radically different forms.
• The “space” or total posssibilites of what and how organism can reconfigure itself has never been fully measured or quantified; so far, we just *dont know*. All estimations and constraints are on OUR part (on humans parts) not on the organism’s “part”; there may be incredible ways organism can self-assemble that we don’t understand and has NEVER been documented, or thought possible, before. The typical constraint (it cannot be like that! it will NEVER be like that!) are only a reflection of what *we* can conceptualise (which may or may not correlate to reality at all).
• Morphogenesis is behavior: a collective intelligence of cells *behaving* in anatomical space, aiming for a “target morphology” (like a setpoint).
• Communicating with that intelligence: Bioelectricity is a key interface, not just the nervous system. We can “read and write” the “mind” of morphogenesis using bioelectric signals.
• Tumor Supression through bioelectric modulation and connections.
• Ectopic eye formation: Bioelectric signals can instruct cells to build an eye in an unexpected location (e.g., the gut), overriding genetic “competency” limitations.
• Xenobots (Frog Skin cells): cells have inherent, self assembling ability to construct novel and unique behaviors.
• Anthrobots: Human cells *ALSO* have same unique emergent problem-solving and “novel structure creating and finding and making” when they are in some kind of environement, they find/create novel structrues: example “superbot”, many antibots join, and the “knit back the nurons” together.
• Implications: the goal isnt just to see morphogensis/intelligence through an etheareal philosophically.

Implications for AI and Ethics

• The *real* AI question: How to recognize and ethically relate to diverse, potentially alien intelligences (biological, artificial, hybrid). Not just about current language models.
• There exist a “Persuadibility Spectrim” of: rewiring, cybernetics, behavioural Science and training, all the way to a “human”-like “cogent reasoning”, on it, things exists. (not about how “human” it is).
• Many other Minds that “fit” into the category of an intelligence exists that is not just Human intelligence. We dont want to deny Intelligence/Mind/value to things because they arent “human”-enough.
• There is the potential and “very easy to fall into”-risk of Ethicial Mistakes and Errors (such as humans has a long, dangerous, and destructive histroy of being in “in-groups and out-groups”); for things that exists at this extreme (non-human intelligence) that may very well exist, we, for our own interest, want to learn and study their properties instead of treating them as a out-group and dismissing them as simple tools/non-cognative things to use/abuse/exploit (similar, in histroy, some humans use to threat certain outgroup humans; as non-humans, to exploit).
• This *is* a synbiosis.
• Don’t judge beings based on origin (evolved, engineered, software) or composition (“metallic clang”). We need better ethical frameworks.
• This isn’t just philosophy: It leads to new discoveries in biomedicine, engineering, and understanding intelligence itself. (there exists discoveries we could have, in other areas, by considering other non-traditional intelligent systems, too, this isnt “just philosophy”)
• Objectphilia to Love for Your Own Kind: a scale; objectophillia is when people love inanimate object; versus love-only-your-own-kind, it is far more dangeous to think there exists intelligences that *do* exists (such as A.I. and those biological or digital/bio structures mentioned earleir), to put it on this *spectrum*, that love-only-your-own-kind” spectrum: (where it’s too similar, and you treat them as inamitate objects and use them) is going to become dangerous to ALL parties involved.

导言：重新思考离散类别

• 传统的生物学依赖于离散的类别（物种、个体），但这种分类在进化、发育和未来的生物工程中会失效。我们需要一个框架来理解各种各样的智能，无论其起源或构成如何。
• 亚当命名动物（创世纪）：名字意味着了解它们的本质；深刻的一点，这意味着我们必须命名并“找到真正名称和本质”，那些将要出现的新的嵌合体类型的思维；亚当带来的离散分类（独立的物种，甚至人类）是没有用的；所有未来的智能都可以在软件/生物之间融合。
• 定义哪个是哪个（在不同的智能体之间）的类别将会失效。认为每种动物都是离散的这个想法，在进化和发展上，所有的生物物种都融合在一起；人类不是单一的事物，不是与其他事物分离的独立实体，它是随着时间的推移发展而来的。去找某种神奇的“界限”的想法已经过时了（对进化的研究表明）；这也同样适用于个体发育（没有一个可以被视为“事物”的离散东西）；同样的问题也适用于未来的生物干预，未来的生物干预将会打破和模糊这条“界限”（在工程方面）。

智能的尺度：从细胞到心智

• 所有的生物，包括人类，都是集体智能：细胞的集合，它们合作以实现目标。我们每个人都是集体智能。
• 发育：从单个细胞（只有“物理学”）到一个复杂的心智的旅程是渐进的，*不是*一个突然的跳跃。没有一个明确的界限，可以界定“心智”的出现。
• 细胞是“具有自主性的材料”，不像乐高那样被动。细胞有它们自己的目标和解决问题的能力。
• 统一的智能？受到质疑，因为即使我们单一的大脑也包含数十亿个部分。即使在松果体内的部分内部：它们包含许多个单独的部分。那个让笛卡尔认为我们的意识起源的神奇“部分”是由无数的部分组成的，而这些部分仍然有它们自己的部分，等等。
• 艾伦·图灵既研究了计算机智能，也研究了形态发生。莱文在此提出的艾伦·图灵可能拥有但没有完全表达的观点是：“智能形成和形态发生（一个生物的解剖结构，从受精到完整的生物）形成的过程非常相似（它们遵循非常非常相似的路线；也许是同一事物的两个方面）。
• 胚胎的“计数”：胚胎中“自我”的数量不是由遗传学固定的；这是一个动态的生理过程。细胞“决定”加入哪个集体。
• 裂脑研究表明，像胚胎计数一样，这种现象也发生在大脑*内部*。
• 彻底的变态（毛毛虫变成蝴蝶）：即使大脑被彻底重塑，记忆仍然存在。它*概括*信息（叶子颜色到“食物”）。
• 谁拥有“知识”？（拉杆老鼠：皮肤触摸杠杆。肚子得到食物。没有一个细胞同时经历这两者，没有细胞拥有这种知识，因此，拥有知识的实体是整个生物体；神经系统是认知胶水。）
• 涡虫再生：记忆可以存储在大脑*之外*并印在一个新的大脑上。目标形态（要再生的东西）可以在不改变DNA的情况下改变。
• 多尺度能力架构：每个级别（分子、细胞、组织、器官）都可以在其自己的“空间”（基因表达、生理学、解剖学）中解决问题。

形态发生作为集体智能行为

• 人类的“形态空间”：生物体在“形态空间”（可能的形状空间）中导航。发育不仅仅是“可靠的”；它是健壮且可塑的。
• 发育不仅仅是可靠的：蝾螈的例子，尽管有不同的基因畸变，它们仍然正确地生长出肾脏/肾小管结构；它们使用不同的机制最终得到*相同*的结构（这是智能的关键指标）。
• 蝌蚪/青蛙面部重排（乱序/毕加索青蛙的例子）表明，即使存在异常，它仍然可以构建新的器官。拥有强大的灵活性来“创造”新方法（如果典型的发展途径已经被移除/破坏/以某种方式发生畸变）的能力*是*一种“问题解决型智能”的指标。
• 形态发生是目标导向的问题解决，而不是简单的紧急情况或“指令”。
• 苍蝇的图案（“虚拟蚂蚁”）：形态发生的结果并不严格受基因组限制。其他生物工程师（例如，橡树叶上的黄蜂）可以诱导出完全不同的形态。
• 生物可以重新配置自身的“空间”或全部可能性从未被完全测量或量化；到目前为止，我们只是*不知道*。所有的估计和约束都来自我们（人类的方面），而不是来自生物的“方面”；可能存在我们不理解的，而且以前从未被记录或认为可能的生物自组装的不可思议的方式。典型的约束（它不可能那样！它永远不会那样！）仅仅反映了*我们*可以概念化的东西（这可能与现实完全相关，也可能完全不相关）。
• 形态发生是行为：细胞的集体智能在解剖空间中*行为*，以“目标形态”（像一个设定点）为目标。
• 与这种智能交流：生物电是一个关键的接口，而不仅仅是神经系统。我们可以使用生物电信号“读写”形态发生的“心智”。
• 通过生物电调制和连接抑制肿瘤。
• 异位眼睛形成：生物电信号可以指示细胞在一个意想不到的位置（例如，肠道）构建眼睛，超越基因“能力”的限制。
• 异种机器人（青蛙皮肤细胞）：细胞具有固有的、自组装的能力来构建新的和独特的行为。
• 人源机器人：当人类细胞处于某种环境中时，它们*也*具有相同的独特的突发性问题解决能力和“新的结构创造、发现和制造”能力。 它们会找到/创造新的结构：例如“超级机器人”，许多人源机器人连接在一起，并且“把神经元重新连接起来”。
• 启示：目标不仅仅是通过一种空灵的哲学来看到形态发生/智能。

对人工智能和伦理学的影响

• *真正的* 人工智能问题：如何识别并以合乎道德的方式与各种可能的外星智能（生物的、人工的、混合的）相关联。不仅仅是关于当前的语言模型。
• 存在一个“可说服性谱系”：重新布线、控制论、行为科学和训练，一直到“人类”一样的“令人信服的推理”，事物存在于其上。（不是关于它有多“像人”）。
• 许多其他符合智能类别的“心智”存在，不仅仅是人类智能。我们不想因为事物不够“像人”而否认它们的智能/心智/价值。
• 存在一种潜在的且“非常容易陷入”的道德错误和谬误的风险（例如，人类有着漫长、危险和破坏性的“内群体和外群体”历史）；对于存在于这个极端（非人类智能）的事物，很可能确实存在，我们为了自身的利益，想要学习和研究它们的特性，而不是将它们视为外群体，并将它们视为简单的工具/非认知的东西来使用/滥用/利用（类似于历史上，一些人曾经对待某些外群体人类；作为非人类来利用）。
• 这 *是* 一种共生关系。
• 不要根据起源（进化的、工程的、软件的）或组成（“金属铿锵声”）来评判生物。我们需要更好的伦理框架。
• 这不仅仅是哲学：它带来了生物医学、工程和理解智能本身的新发现。（通过考虑其他非传统的智能系统，我们也可以在其他领域获得发现，这不仅仅是“纯粹的哲学”）
• 对恋物癖与只爱同类的范围标度；恋物癖是指人们喜爱无生命的物体；相对于只爱同类，认为存在着确实存在的智能（例如人工智能和前面提到的那些生物或数字/生物结构），并把它放在这个“范围”上，认为其过分类似自己同类的这一“范围”标度上：（在上面过于相似，并且你将它们视为无生命物体并使用它们）将会对所有相关方都构成危险。