Introduction: Beyond the IKEA Blueprint

• Traditional view: DNA as a “blueprint” (like IKEA instructions) for building organisms. Proteins are like builders and you get the thing.
• Reality: DNA provides a recipe for proteins (low-level parts), *not* a direct blueprint for the organism’s shape (morphology). It all works by complex systems.
• Analogy is describing metal, screws, or the parts list, but not a bookshelf (shape is emergent, it comes out from the bottom, from complexity and systems, there are no detailed instruction manuals).
• Analogy: Not instructions, but complex hardware (like a circuit that harnesses phsyical law) making softwate via dynamic bottom-up process of emmergence from many smaller parts with minimal rules/behaviors.
• In principle, with perfect simulation, we could derive shape from DNA + environment, but that’s not how biology *works*.

The Picasso Tadpole Experiment

• Experiment: Rearranged facial features of tadpoles (eyes, jaws, etc., in wrong places).
• Result: Features moved *correctly* to form a normal frog face. The end shape came and it worked.
• Implication: Genome doesn’t encode a hard-wired sequence of movements. Instead, it creates a system that *reduces error* towards a “target morphology” (the ‘remembered’ correct frog face).
• Analogy: Marble dropping (gravity), you dont need top-down direct instuction. You just drop. The complex marble sorter has bottom up dynamics to get it in the right place.
• The system “self-corrects,” It works continuously to correct a certain remembered pattern, error reduces.

Teleology and Goal-Directedness

• Teleophobia” in biology: Resistance to describing biological systems as having goals.
• Levin: Goal-directedness is *essential* for understanding how biological systems function and evolve.
• Not just day-to-day. It’s at every stage (including during Evolution itself, even when deveolping, or any change/process really).
• We should be studying the goals that emerge, even as a practical empirical issue!
• Friston’s Free Energy Principle: Organisms predict their environment *and themselves*, minimizing “surprise” (difference between prediction and reality).
• Multilevel (multiple tiers and hierarchies) Systems with goals! All reducing the delta and making prediction/goal.
• Intentional Stance (Dennett): Assigning “agency” is a *practical* tool for prediction and control.
• Principle of Least Action (Physics): Systems “minimize effort.” Analogous to biology: Find the most *efficient* level of description and intervention, not always the lowest.
• Example: No quantum, lowest possible, no one actually thinks like that. The system will act to go the lowest cost of path, it will do it, it must be at any of these levels, at the bottom! (bottom = particles or atons and then emergent systems from there)
• Robotics Analogy: Cuckoo Clocks must be *rewired*; advanced intelligences are easier to *persuade* with stimuli and experiences instead of adjusting its atom (and so they also lack feedback loops and are quite limited/not flexible!). This can inform bioengeneering!

Multi-Scale Systems and “Downward Causation”

• Cells follow local rules, BUT higher levels (tissue, organ) perform computations that “deform the option space” for cells.
• Analogy: Bending space. There is no “magic” at lower levels of computation but it can come together (complexity) to work at another layer, from higher layers, where another ’emergent’ level makes computations to do its thing too!
• Higher-level “goals” guide lower-level processes towards a global body plan (like water flowing downhill, “minimize effort/cost”).
• Swarm Intelligence: Group agent has a rough pattern-memeory that “remembers” the large pattern it’s creating.
• Planaria example (next section for details).

Planaria: Memory Beyond the Genome

• Planaria: Flatworms that regenerate any body part, do not age.
• Two-Headed Planaria: Bioelectric circuit stores “pattern memory.” This can be *reprogrammed* to create two-headed worms *without changing the genome*.
• Regenerate into perfect tiny worms, no more no less (can be even 255 parts, and still do this!)
• They’re IMMORTAL. So aging itself isnt inevitable and does not age by thermodynamics limits (we get new ones when they split in half, basically splitting the old “ancient” version).
• Somatic Inheritance: Mutations accumulate in planaria, but regeneration remains precise. Challenges “DNA-centric” view.
• Imperfect DNA, but very perfect bodies because of top down, multiscale layers with pattern correction.
• If we change the pattern, it will just generate its version, no matter if DNA changes or not, they will still form (if 2 heads it will). The software changed, the memory of bioelectrical circuit and the large memory.
• We can even ‘change heads’ by interupting the normal pattern with electricity.

Bioelectricity and the “Anatomical Compiler”

• We can turn these “gates” off and on to build (by turning the ion channel circuits, protein batteries that store and create memories and decisions!) things! It works similar to a digital circuits “ram” but instead as biological organisms.
• The bioleectric information is similar to a memory circuit that needs voltage (RAM or the circuit, it needs current!), and the information is saved as electrical memories on top of DNA.
• There is hardware that encodes all things, turn it on, it will show what’s default, which gets tuned by evolutions to create all living creatures, but, with “goals”, its actually quite reporogrammable!
• Vision: “Anatomical Compiler”: Draw a desired organism; the system translates that into stimuli to guide cells to build it (not by 3D printing, but by rewriting cellular “goals”).
• Change the voltage of the bioelectric memory so they get changed! We’re still trying to understand how/when, but when there is stability.

Self, Boundaries, and Cancer

• Gap Junctions: Direct connections between cells (proto-synapses). Share information, erasing “ownership” (like a “mind meld”).
• Origin of “Self”: Merging of individual cell “minds” into a larger, compound self.
• Difficult to make ownership (there are signals shared, with no ‘ownership’ metadata that we often give value and importance to!), it’s not clear “who owns” these changes of values, making this a type of super telepathy. It merges.
• Analogy: A cell becoming large to create large cognition and larger (think about time and space now in larger terms, it grows).
• Cancer: Breakdown of gap junction communication, cells revert to a unicellular, “selfish” state (metastasis).
• Simulation analogy: We simulate “prisoner’s dilemma”. Cooperation becomes default, with no one able to cheat. However, the cell can become separate, and only thinks as singular cells (go, proliferate).
• Possible cancer treatment: Convincing cancer cells to rejoin the collective, restoring multicellular communication.
• Metastatic Malanoma: we prevented electrical communicational cells, it just reverted back and start doing cancerous/malfunctions of errors and non pattern correcting.
• We ought to reverse it by using things like ion-channel to correct/adjust voltage! Rejoin the “pattern”, with borg-style hivemind, we fix our cell.

Robotics, AI, and Future Implications

• Multiscale Intelligence: need an intelligence on par to humans, something a kin to a ‘human collective’.
• Collaboration, not competition/cheating. But make each individual count (keep its uniqueness in terms of skills). We need better control over things to know more about these different organisms.
• Why we use the worm? The body-blueprint isnt even in the body plan (how is this possible?). Where the body plan be in biology, where in it, what the ‘program’ is to work from? We use them becauase their genomes are MESSED, very messy! Yet it still produces consistent, almost exact copies that’s stable 100%.
• No matter how bad the mutations or messy its genes/chromosome/DNA become! So there must be something “other” than this for pattern corrections and morphogenisis and development.
• Multi-scale robotics: where robots arent fully following simple programming rules. This allows greater creativity, adaptation, resilience.
• Biological insights inform robotics: “Robots don’t get cancer” (because parts lack sub-goals, lacking a feedback system). Explore “info-taxes” (constant search for information).
• Synthetic Biology: Creating novel organisms by altering cell interactions and environments (“endless forms most beautiful”).
• Endless Forms: endless possibility and variety, and it may end up even outside “darwin’s wild imaginations” by being synthetic (we change them manually in the ‘goal’, not the “dna level). We can give normal cellls new changes/tasks!
• Exotic possibilities: can be part “evolved” (maybe not even organic/biochemical biology, it could use virutal-bio). It can range from bioengeneering-based robotics, household things with machien leanring and etc etc.
• New Types of Life: it blurs all words. It’ll blur all human distinctions of words with things like humanoids, human brains/biology.
• A cell on the tail, eye on tails, etc (tadpole), will give it “vision” with “normal” body structure, yet working in perfect order, showing it will do/function! Even by its genomic standard!
• If we design somtehing to act like an organisms. All words “break down” here, and we should update these labels in better definitions with new terms.
• Ethical Considerations: Re-evaluating definitions of “machine,” “organism,” “self.” What are our obligations to different types of agents?
• No good definitions of anything, yet it opens “moral, and philosophical question that is massive: what happens if my brain connects with computer and the computre connects with mine?”. Or other body parts being artificial, “how will this function”. Will it blur or not change us at all. How much it matters: a vacuum, or implant and the split/combination ratio.

导言：超越宜家蓝图

• 传统观点：DNA 就像构建生物体的“蓝图”（类似于宜家说明书）。蛋白质就像建造者，然后你就得到了成品。
• 现实：DNA 提供了蛋白质（低级部件）的配方，*而不是*生物体形状（形态）的直接蓝图。一切都通过复杂的系统运作。
• 类比：描述金属、螺丝或零件清单，但不是书架（形状是涌现的，它从底部、从复杂性和系统中产生，没有详细的说明手册）。
• 类比：不是指令，而是复杂的硬件（如利用物理定律的电路），通过许多具有最小规则/行为的小部件自下而上的动态涌现过程来制造软件。
• 原则上，通过完美的模拟，我们可以从 DNA + 环境推导出形状，但那不是生物学*运作*的方式。

毕加索蝌蚪实验

• 实验：重新排列蝌蚪的面部特征（眼睛、下巴等，位置错误）。
• 结果：特征*正确地*移动，形成正常的青蛙脸。最终的形状出现了，而且很有效。
• 启示：基因组不编码硬编码的运动序列。相反，它创造了一个*减少误差*的系统，朝着“目标形态”（“记住的”正确的青蛙脸）发展。
• 类比：弹珠掉落（重力），你不需要自上而下的直接指令。你只需让它掉落。复杂的弹珠分拣机具有自下而上的动力学，使其到达正确的位置。
• 系统“自我校正”，它持续工作以纠正某个记住的模式，减少误差。

目的论和目标导向性

• 生物学中的“目的恐惧症”：抵制将生物系统描述为具有目标。
• 莱文：目标导向性对于理解生物系统如何运作和进化*至关重要*。
  • 不仅仅是日常。它存在于每个阶段（包括进化本身，甚至在发育期间，或任何变化/过程）。
  • 即使作为一个实际的经验问题，我们也应该研究出现的这些目标！
• 弗里斯顿的自由能原理：生物体预测它们的环境*和它们自己*，最小化“惊讶”（预测和现实之间的差异）。
• 多层次（多个层级和等级）具有目标的系统！所有这些都在减少差距并进行预测/目标。
• 意向立场（丹尼特）：分配“自主性”是预测和控制的*实用*工具。
• 最小作用量原理（物理学）：系统“最小化努力”。类似于生物学：找到最*有效*的描述和干预水平，不一定总是最低的。
• 示例：没有量子，最低可能，没有人真的那样思考。系统将采取最低成本的路径，它会这样做，它必须在这些层级中的任何一个，在底部！（底部=粒子或原子，然后从那里产生涌现系统）
• 机器人学类比：布谷鸟钟必须*重新接线*；更高级的智能更容易通过刺激和经验来*说服*，而不是调整它的原子（因此它们也缺乏反馈回路并且非常有限/不灵活！）。这可以为生物工程提供信息！

多尺度系统和“向下因果关系”

• 细胞遵循局部规则，但更高层次（组织、器官）执行计算，这些计算“扭曲”细胞的选择空间。
• 类比：弯曲空间。在较低层次的计算中没有“魔法”，但它可以聚集在一起（复杂性）在另一个层次上工作，从更高的层次，在那里另一个“涌现”层次进行计算来做它自己的事情！
• 更高层次的“目标”引导较低层次的过程朝着全局的身体计划发展（就像水流向下游，“最小化努力/成本”）。
• 群体智能：群体智能体有一个粗略的模式记忆，它“记住”它正在创建的大模式。
• 涡虫示例（下一节详细介绍）。

涡虫：超越基因组的记忆

• 涡虫：可以再生任何身体部位的扁虫，不会衰老。
• 双头涡虫：生物电回路存储“模式记忆”。这可以被*重新编程*以创建双头蠕虫，*而无需改变基因组*。
• 再生为完美的微小蠕虫，不多不少（甚至可以是 255 个部分，仍然可以做到这一点！）
• 它们是不朽的。因此，衰老本身并不是不可避免的，也不会因热力学限制而衰老（当它们分裂成两半时，我们会得到新的，基本上是分裂旧的“古代”版本）。
• 体细胞遗传：突变在涡虫中积累，但再生仍然精确。挑战“以 DNA 为中心”的观点。
• 不完美的DNA，但非常完美的身体，因为自上而下、多尺度层具有模式校正。
• 如果我们改变模式，它将只生成它的版本，无论 DNA 是否改变，它们仍然会形成（如果 2 个头，它会）。软件改变了，生物电回路的记忆和大记忆。
• 我们甚至可以通过用电中断正常模式来“改变头部”。

生物电与“解剖编译器”

• 我们可以关闭和打开这些“门”来构建（通过转动离子通道电路，储存和创造记忆和决定的蛋白质电池！）东西！它的工作方式类似于数字电路的“ram”，但作为生物体。
• 生物电信息类似于需要电压的存储电路（RAM 或电路，它需要电流！），并且该信息作为 DNA 之上的电记忆被保存。
• 有一种硬件可以编码所有事物，打开它，它会显示默认值，进化会对其进行调整以创造所有生物，但是，有了“目标”，它实际上是可以重新编程的！
• 愿景：“解剖编译器”：绘制一个想要的生物体；该系统将其转化为刺激以引导细胞构建它（不是通过 3D 打印，而是通过重写细胞“目标”）。
• 改变生物电记忆的电压，让它们改变！我们仍在努力了解如何/何时，但是当有稳定性时。

自我、边界和癌症

• 间隙连接：细胞之间的直接连接（原突触）。共享信息，擦除“所有权”（就像“心灵融合”）。
• “自我”的起源：个体细胞“思维”合并成一个更大的、复合的自我。
• 难以确定所有权（有共享的信号，没有我们经常赋予价值和重要性的“所有权”元数据！），不清楚“谁拥有”这些值的变化，使其成为一种超级心灵感应。它合并。
• 类比：一个细胞变大以创造更大的认知和更大的（现在从更大的角度考虑时间和空间，它增长）。
• 癌症：间隙连接通信中断，细胞恢复到单细胞、“自私”状态（转移）。
• 模拟类比：我们模拟“囚徒困境”。合作成为默认，没有人能够作弊。但是，细胞可以变得分离，并且只作为单个细胞思考（去，增殖）。
• 可能的癌症治疗：说服癌细胞重新加入集体，恢复多细胞通讯。
• 转移性黑色素瘤：我们阻止了电通讯细胞，它只是恢复过来并开始癌变/错误和非模式校正的故障。
• 我们应该通过使用离子通道之类的东西来纠正/调整电压来逆转它！重新加入“模式”，通过蜂巢思维，我们修复我们的细胞。

机器人、人工智能和未来的影响

• 多尺度智能：需要与人类相当的智能，类似于“人类集体”的东西。
• 合作，而不是竞争/作弊。但要让每个个体都有价值（保持其在技能方面的独特性）。我们需要更好地控制事物，以更多地了解这些不同的生物体。
• 为什么我们使用蠕虫？身体蓝图甚至不在身体计划中（这怎么可能？）。身体计划在生物学中会在哪里，在哪里，工作的“程序”是什么？我们使用它们是因为它们的基因组很乱，非常混乱！然而，它仍然产生一致的、几乎精确的副本，100% 稳定。
• 无论突变有多糟糕，或者它的基因/染色体/DNA 变得多么混乱！因此，除了这个之外，必须还有一些“其他”东西来进行模式校正、形态发生和发育。
• 多尺度机器人技术：机器人不完全遵循简单的编程规则。这允许更大的创造力、适应性、弹性。
• 生物学见解为机器人学提供信息：“机器人不会得癌症”（因为部件缺乏子目标，缺乏反馈系统）。探索“信息税”（不断搜索信息）。
• 合成生物学：通过改变细胞相互作用和环境来创造新的生物体（“无穷无尽的最美丽的形式”）。
• 无限形式：无限的可能性和多样性，它最终甚至可能超出“达尔文的疯狂想象”，通过合成（我们在“目标”中手动更改它们，而不是“dna 级别”）。我们可以给正常细胞新的变化/任务！
• 奇异的可能性：可以是部分“进化”的（甚至可能不是有机/生物化学的，它可以使用虚拟生物）。它的范围可以从基于生物工程的机器人、具有机器学习的家用物品等等。
• 新型生命：它模糊了所有词语。它将模糊所有人类对诸如人形、人脑/生物等事物的区别。
• 尾巴上的细胞，尾巴上的眼睛，等等（蝌蚪），将赋予它“视觉”，具有“正常”的身体结构，但工作秩序完美，表明它会做/起作用！即使按其基因组标准！
• 如果我们设计出某种行为像生物体的东西。所有单词在这里“崩溃”，我们应该用更好的定义和新术语更新这些标签。
• 伦理考虑：重新评估“机器”、“生物体”、“自我”的定义。我们对不同类型的行为者有什么义务？
• 没有任何东西的好的定义，但它打开了“巨大的道德和哲学问题： 如果我的大脑与计算机连接并且计算机与我的连接会发生什么？”。或者其他身体部位是人造的，“这将如何运作”。它会模糊还是根本不会改变我们。它有多重要：真空，或植入物和分裂/组合比率。