Introduction: Challenging Biological Assumptions

• Individual cells don’t “know” their position (e.g., on a nose), but cellular collectives do. These networks possess computational properties and a form of collective intelligence.
• This collective intelligence solves problems, including storing a representation of the intended structure (morphogenesis) and minimizing the difference between the current state and the target.

Genome as Hardware, Bioelectricity as Software

• The genome specifies the “hardware” (proteins), but subsequent events are like “software.” This “software” is reprogrammable and handles representation. The Genome alone DOES NOT specify the body-plan, the “Software 2.0”, aka, the bioelectrical plan and processes determine that.
• Development usually produces a “default” form (e.g., human shape), but this isn’t the fixed endpoint. It can be modified, bioelectricity can change without changing dna.
• Biology utilizes a robust architecture unlike computer science, since its not made to stay working reliably. Its meant to “just keep going, man, go and persist”. Hardware is unreliable and there are various issues which cannot be fully “cleaned up”, it *must* go forward with errors.
• Evolution doesn’t solve specific problems. Instead, it creates problem-solving agents that operate in various spaces (e.g., anatomical space), not by planning them perfectly, but through persistence.
• The default “just keeps going man” biological architecture can make up and correct to achieve what would *seemingly* be highly-unlikly.

Planaria and Reprogramming Morphology

• Nature shows how things *can* deviate and adapt. There are examples, where a wasp will do non-genetic DNA changes via other means on Oak leaves that produces a vastly, crazy, new look to an Oak, so, the typical thinking of Oak always have certain genetic coding and such is wrong, because the Wasps change them via other means, which Levin researches with bioelectricity.
• Planaria (flatworms) regenerate any body part. Each piece “knows” the correct worm structure.
• A bioelectric circuit stores this “pattern memory” (like an API for cells). This pattern can be altered (e.g., to create two-headed worms) without genetic change.
• This altered pattern is persistent. Two-headed worms continue to regenerate as two-headed without further intervention and the bi-electricty in each of the now, 2-heads, “pattern memory”.
• Scientists can visualize these bioelectric patterns, directly observing the “memories” of the collective intelligence (like reading a brain, but different).
• The pattern in a planaria shows “two-headed-ness”, is stored not necessarily locally, but globally throughout the worm. This “counterfactual memory” which can change in future time based on the future events which changes it.

Memory Transfer and Caterpillar-Butterfly Metamorphosis

• “Memory” is not solely a brain concept. There’s evidence of memory transfer between organisms (e.g., trained *Aplysia* RNA injected into naive hosts). It is transferable!
• Planaria retain learned information even after regenerating a new head/brain, suggesting memory storage beyond the brain, and transferal, and reimprint, of that memory.
• Caterpillar-butterfly metamorphosis (not Levin’s direct research) shows memory persistence through drastic physical changes.
• The *interesting* part isn’t just memory *persistence*, but the *remapping* of that memory. A caterpillar’s memory of crawling to food is useless to a flying butterfly, requiring generalization (leaves -> food) and remapping to new effectors.

Implications for Medicine and Beyond

• Understanding and manipulating these bioelectric networks has implications for regenerative medicine (e.g., birth defects, limb regeneration, tumor normalization).
• Instead of micromanaging at the molecular level (like current molecular medicine), the goal is to “persuade” cells to achieve a desired outcome (like training a rat).
• This involves setting “top-level parameters” and letting the inherent agency and problem-solving capacity of lower-level systems (cells, tissues) cascade down. Adult frog can grow their legs with 24 hour “jumpstart”. The idea isn’t micromanage but get things going by perusasion and competency of “down stream levels”.
• It’s akin to “bending the energy landscape” to guide lower levels while utilizing their inherent competencies, like creating a downhill such that water “just flows”.

Intelligence and Agency in Biological Systems

• Intelligence, is simply navigating.
• Molecular networks also demonstrate learning, implying the same capabilities but at a “lesser degree”. The more complex and large the structure, the more its navigation, persistence and cognitive capabilities is.
• Defining intelligence: Problem-solving capacity, navigating a problem space to achieve goals, despite new perturbations (“same goal by different means” – William James). This isn’t about consciousness or self-awareness.
• Intelligence is *not* a philosophical claim but an *empirical*, testable one. Hypothesize about a system’s goals and competencies, then test with perturbations.
• Making an intelligence claim is also a test of the *observer’s* understanding, since they might miss other capabilities. Don’t assume lack of observed intelligence means it’s not present.
• Operational” machine (ai) “Intelligence” is present *now*.
• Even simple systems (gene networks, sorting algorithms) exhibit unexpected problem-solving abilities, implying a need for humility about assuming we know a system’s capabilities just because we built it or know its parts. Bubble sort algorithm even “surprised” us of certain hidden features we didnt even forsee, which we, humans created it from a relatively small program size.

Implications for Artificial Intelligence

• AI can possess operational intelligence without necessarily having consciousness or self-awareness. This simplifies discussions around AI and intelligence.
• We must *not* fall back on, typical, easy ways, out when facing tough problems with Intelligence, or AI-Intelligence. Such easy ways would include saying, “It’s Just Physics/Machine/Linear Algorithmn. It is likely biological principles, like emergent agency are key to intelligence in nature, and are the cause.
• Moving away from a completely deterministic approach in building AI. Hierarchical systems with agentive components are less predictable but potentially more powerful. We may uninetionally create these new complexities, and they may do things in unintended way.s
• There’s a concern about *unintentionally* creating agentive, sentient systems in AI. Levin stopped writing a paper detailing the biological features crucial for true agency to avoid accelerating this. “we should be responsible for creating/understanding new types of intelligenct, especially with bioelectric manipulations/understandings”.

Scientific Renaissance and Future of Being Human

• Levin and others see hints of a scientific renaissance, a questioning of established assumptions across multiple disciplines (neuroscience, psychology, even physics).
• There is crisis in “meaning” where things thought as important are shown not to be. Example given by levin: free will.
• The far future: “Freedom of embodiment” where limitations of a given body at birth (e.g., health issues, lifespan) are considered absurd and unacceptable.
• This could be seen as people being stuck in what seems like the “stone age” before the “freedom” of having bodies (bodyplans, morphology) to meet our current *and* changing goals.

导言：挑战生物学假设

• 单个细胞并不知道自己的位置（例如，在鼻子上），但细胞集体却知道。这些网络拥有计算特性和一种集体智慧。
• 这种集体智慧解决问题，包括存储预期结构的表示（形态发生），并最小化当前状态和目标之间的差异。

基因组作为硬件，生物电作为软件

• 基因组指定“硬件”（蛋白质），但后续事件就像“软件”。这个“软件”是可重新编程的并处理表征。仅基因组并不能指定身体计划，“软件2.0”，也就是生物电计划和过程，决定了这一点。
• 发育通常会产生一个“默认”形式（例如，人类的形状），但这并不是固定的终点。它可以被修改，生物电可以在不改变DNA的情况下改变。
• 生物学利用了一种与计算机科学不同的强大架构，因为它不是为了保持可靠地工作而设计的。它意在“坚持下去，伙计，继续并持续存在”。 硬件是不可靠的，并且存在各种无法完全“清理”的问题，它 *必须* 带着错误前进。
• 进化并不解决具体问题。相反，它创造了在各种空间（例如，解剖空间）中运作的解决问题的智能体，不是通过完美地计划它们，而是通过持久性。
• 默认的“坚持下去，伙计”的生物架构可以弥补和纠正，以实现看起来极不可能实现的目标。

涡虫与形态重编程

• 大自然展示了事物如何 *能够* 偏离和适应。 有一些例子，黄蜂会通过其他方式在橡树叶上进行非基因DNA改变，从而产生一种全新的、疯狂的橡树外观，因此，橡树总是有某些基因编码等的典型思维是错误的，因为黄蜂通过其他方式改变它们，莱文通过生物电对此进行研究。
• 涡虫（扁虫）再生任何身体部位。每一块都“知道”正确的蠕虫结构。
• 生物电回路存储这种“模式记忆”（就像细胞的API）。 这种模式可以在不进行基因改变的情况下被改变（例如，创建双头蠕虫）。
• 这种改变的模式是持久的。 双头蠕虫在没有进一步干预的情况下继续再生为双头，并且现在，两个头中的每一个都有生物电的，“模式记忆”。
• 科学家们可以可视化这些生物电模式，直接观察集体智慧的“记忆”（就像读取大脑，但有所不同）。
• 涡虫中的模式显示“双头性”，不一定存储在局部，而是存储在整个蠕虫的全局。 这种“反事实记忆”可以根据未来改变它的未来事件而改变。

记忆转移与毛毛虫-蝴蝶变态

• “记忆”不仅仅是一个大脑的概念。有证据表明记忆在生物体之间转移（例如，训练过的 *海兔* RNA 被注射到幼稚的宿主体内）。 它是可转移的！
• 即使在再生了新的头部/大脑后，涡虫也保留了学习到的信息，这表明记忆存储在大脑之外，以及该记忆的转移和重新印记。
• 毛毛虫-蝴蝶变态（不是莱文的直接研究）显示了通过剧烈身体变化的记忆持久性。
• *有趣的*部分不仅仅是记忆 *持久性*，而是该记忆的 *重新映射*。 毛毛虫爬向食物的记忆对飞行的蝴蝶来说是无用的，需要泛化（叶子 -> 食物）并重新映射到新的效应器。

对医学及其他领域的影响

• 理解和操纵这些生物电网络对再生医学具有影响（例如，出生缺陷、肢体再生、肿瘤正常化）。
• 目标不是在分子水平上进行微观管理（就像当前的分子医学），而是“说服”细胞实现期望的结果（就像训练老鼠）。
• 这涉及设置“顶级参数”，并让较低级别系统（细胞、组织）的固有自主性和解决问题的能力向下级联。 成年青蛙可以在24小时的“启动”下长出腿。 这个想法不是微观管理，而是通过说服和“下游级别”的能力来让事情开始。
• 这类似于“弯曲能量景观”来引导较低级别，同时利用它们的固有能力，就像创建一个下坡，使水“自然流动”。

生物系统中的智能与自主性

• 智能，只是导航。
• 分子网络也展示了学习能力，这意味着相同的能力，但程度“较低”。结构越复杂和庞大，其导航、持久性和认知能力就越强。
• 定义智能：解决问题的能力，在问题空间中导航以实现目标，尽管有新的扰动（“通过不同方式实现相同目标”——威廉·詹姆斯）。 这与意识或自我意识无关。
• 智能 *不是* 一个哲学主张，而是一个 *经验性的*、可测试的主张。 对系统的目标和能力进行假设，然后用扰动进行测试。
• 提出智能主张也是对 *观察者* 理解的考验，因为他们可能会错过其他能力。 不要假设观察到的智能缺失就意味着它不存在。
• “操作性”机器（人工智能）“智能”*现在* 就存在。
• 即使是简单的系统（基因网络、排序算法）也表现出意想不到的解决问题的能力，这意味着我们需要谦虚地假设我们知道一个系统的能力，仅仅因为我们构建了它或知道它的组成部分。冒泡排序算法甚至“惊讶”了我们某些隐藏的特征，我们甚至没有预见到，而我们人类是从一个相对较小的程序规模创建它的。

对人工智能的影响

• 人工智能可以拥有操作智能，而不一定具有意识或自我意识。 这简化了围绕人工智能和智能的讨论。
• 在面对智能或人工智能智能的难题时，我们 *绝不能* 重新回到典型的、简单的方式。 这种简单的方法包括说，“这只是物理/机器/线性算法。”
• 很可能像涌现自主性这样的生物学原理是自然界智能的关键，也是原因所在。
• 远离在构建人工智能时采用完全确定性的方法。 具有自主组件的分层系统不太可预测，但可能更强大。 我们可能会无意中创造出这些新的复杂性，它们可能会以意想不到的方式做事。
• 人们担心在人工智能中 *无意中* 创建自主的、有感知能力的系统。 莱文停止撰写一篇详细描述对于真正自主性至关重要的生物学特征的论文，以避免加速这一过程。“我们应该负责任地创造/理解新型智能，尤其是通过生物电操纵/理解”。

科学复兴与人类的未来

• 莱文和其他人看到了科学复兴的迹象，对多个学科（神经科学、心理学，甚至物理学）中既定假设的质疑。
• “意义”存在危机，被认为重要的事物被证明并非如此。莱文给出的例子：自由意志。
• 遥远的未来：“身体自由”，出生时给定身体的限制（例如，健康问题、寿命）被认为是荒谬和不可接受的。
• 这可以被视为人们被困在似乎是“石器时代”，在“自由”拥有身体（身体计划、形态）以满足我们当前 *和* 不断变化的目标之前。