生物机器人能否从内部治愈我们？ | 迈克尔·莱文

要知道，我们总以为自己理解生命的本质。DNA、进化、自然选择，这些老生常谈。但如果我告诉你，此刻培养皿中你自身的气管细胞能自发组织成可修复脑组织的游泳机器人呢？如果青蛙皮肤细胞无需任何基因改造就能用现成零件自我复制呢？这并非科幻小说。

You know, we like to think we understand what makes something alive. DNA, evolution, natural selection, the usual suspects. But what if I told you that cells from your own trachea sitting in a petri dish right now could spontaneously organize into swimming robots that heal brain tissue? What if frog skin cells with no genetic modification whatsoever could build copies of themselves from spare parts lying around? This isn't science fiction.

这是塔夫茨大学迈克尔·莱文的研究成果，它正在彻底改写生物学规则。

This is the work of Michael Levin at Tufts University, and it's completely rewriting the rules of biology.

我们从成年患者身上提取气管上皮细胞，结果发现它们也能聚集成这些微型运动生物。我们称之为人形机器人。这些小家伙有9000种差异表达基因，能做出修复神经损伤等酷炫行为。而这只是冰山一角。

So we took cells from adult human patients, tracheal epithelial cells. Turns out that they too come together and form these little motile creatures. We call them anthrobots. Those guys have 9,000 differently expressed genes and they can do cool things like they can heal neural wounds. And this is just the tip of the iceberg.

迈克尔·莱文的研究挑战了我们对生命本质及生物特性起源的根本认知。作为塔夫茨大学杰出生物学家兼艾伦探索中心主任，他在生物电与再生生物学领域的开创性研究正重塑我们对生物系统处理信息与追求目标的理解。他研发的异种机器人——由青蛙细胞构成的生命体，以地球上前所未有的方式游动、协作与繁殖。这对意识、智能及生命本质有何启示？迈克·莱文教授，欢迎来到《探索不可能》播客。

Michael Levin's research challenges our fundamental understanding of what life is and where biological properties emerge from. Michael Levin is a distinguished biologist at Tufts University and director of the Allen Discovery Center whose groundbreaking research on bioelectricity and regenerative biology is re shaping our understanding of how biological systems process information and pursue goals. His xenobots, living robots built from frog cells, swim around, work together, and reproduce in ways that have never existed on Earth. What does this tell us about consciousness, intelligence, and the nature of life itself? Professor Mike Levin, welcome to the Into the Impossible podcast.

谢谢邀请。

Thank you for having me.

很高兴见到你。

It's great to see you.

我们问题太多了，时间肯定不够用。但我想先问个宏观问题：在自然界所有基本力中——强核力、弱核力、引力——为何偏偏是电力（而非磁力）扮演如此重要角色？毕竟根据麦克斯韦方程，电磁本是统一的。难道电力比——比如我桌上这些磁铁——更重要吗？

We have so many questions. We'll run out of time before we run out of questions, I'm sure. But I wanna first start with the big picture question. Why is electricity of all the fundamental forces of nature, nuclear forces strong and weak, gravitation, why is it that electricity and not say magnetism plays such an outsized role when we know that electricity and magnetism are unified via Maxwell's equation? So does electricity play a bigger role than, say, these magnets here that I have on my desk do?

确实如此，活体组织对磁场和电磁场非常敏感。超弱光子也很重要，所有这些因素都很关键。我完全不清楚生物学能否驾驭那些超出我们认知的强大或微弱力量。我真的不知道。

It is it is certainly the case that, living tissue is sensitive to magnetic fields, electromagnetism. It you know, ultra weak photons are important. All of these things are important. I I have no idea if, biology can harness the stronger than you know, or the weaker force. I I I just don't know.

也许吧。但确实如此。可能吧。我也不确定。但当前电力的独特之处在于以下这点。

Maybe. But that's yeah. Maybe. I I don't know. But but the special thing about electricity at this point is the following.

它是一种极其便利的媒介，可以充当我所说的'认知粘合剂'。宇宙中还有其他物质能实现这种功能。如果有外星生命，我相信它们会通过其他机制实现。但进化钟爱生物电的原因在于——

It is a really convenient modality to serve as what I call cognitive glue. There are other things that do it. There are other things that could do it elsewhere in the universe. I'm sure it's done by other mechanisms if there's life elsewhere. But but here's the thing that, evolution loves about bioelectricity.

这是一种非常便捷的方式，能用亚单位构建电网。对吧？通过这种方式将亚单位组合成更大整体，使整体具备目标、记忆、偏好，以及个体部件所没有的问题解决能力。可以说它实现了层级跃升，具体机制我们可以深入探讨。本质上，这和大脑中电流的作用如出一辙——让你超越神经元简单堆砌的存在。

It's a very convenient way to make electrical networks out of, low out of subunits. Right? So so so group subunits into bigger things in a way that allows a whole to have goals, memories, preferences, and, you know, basically problem solving behavior that the individual pieces don't have. It allows a raising of of levels, so to speak, and we can go into great detail how it does it. But basically, the exact same thing that electricity is doing in your brain, which makes you more than the sum of, than just a pile of neurons.

早在神经元出现之前，这种机制就已在多细胞生物体中运作。它是扩展物质认知视界的一种方式。我们接下来会...

It has been doing that for bodies, multicellular bodies, long before neurons ever came on the scene. It's a way to scale up the cognitive light cone of, of of of materials. We'll get

谈到认知视界，这对物理学家来说就像诱饵——难免会联想到爱因斯坦等等。但在深入之前，作为物理学家思考电力时，通常不会把它和柔软物质联系起来，除非有人把烤面包机扔进浴缸（这非常危险，显然我们不建议这么做）。请谈谈电力是如何产生的？它在什么尺度上显现？我们并没有在细胞上看到正负极或电极。

to the cognitive light cone because it's impossible for a physicist that's like, you know, bait for a physicist to evoke Einstein and all sorts of other things. But but before I get there, know, when I think about electricity, when I think about it as a physicist, I don't normally associate it with things that are squishy and unless, you know, somebody threw a toaster in the bathtub, and that would be quite dangerous. Obviously, we're not recommending that. But talk about how electricity even arises and and what scales does it manifest? I mean, we don't see little anodes and cathodes or, you know, positive and negative terminals on cells.

那么它是如何在机制层面实现自身的？你可以讲得很专业——毕竟我的听众是已知多元宇宙中最杰出且最具思辨能力的群体。请详细解释：为什么说细胞像电池或磁铁？这些极性、偶极子及相关特性是如何体现的？

So how does it instantiate itself in a mechanistic way? And you could be very technical. I mean, my audience is, you know, one of the most magnificent and and mindful in the known multiverse. Talk us through how is a how is a cell like a battery or a magnet with with these kind of polarities and and dipoles and everything associated with it?

是的。不，细胞确实含有带电粒子，通常是钾离子、氯化钠离子等类似物质。讨论这类问题的最佳方式是从神经科学的角度思考。我们知道，我们的认知是由单个神经元以及大脑中神经元群和其他细胞运作的生物电活动所支撑的。

Yeah. No. Cells absolutely have charged particles that that typically ions of potassium, sodium chloride, and things like this. And what happens is and and the best way to anchor these kinds of discussions is by thinking about what happens in neuroscience. So we understand that our cognition is underwritten by the bioelectricity that operates in individual neurons and then in groups of neurons and other cells in the brain.

具体来说，细胞拥有质膜——本质上是一种脂质膜，通常是良好的绝缘体。进化过程中产生了称为离子通道的特殊蛋白质。这些离子通道具有非常有趣的特性，可以选择性地让钾、氯、钠等带电粒子进出细胞，从而形成电压梯度。

So what happens is you have cells, and in their plasma membrane, which is basically this lipid kind of membrane, that's usually a pretty good insulator. What evolution has discovered are, special kinds of proteins called ion channels. And these ion channels have some really interesting properties where they let, certain charged, species like potassium, chloride, sodium, and so on. They either, preferentially let them in or out of the cell. And as a result, what you end up with is a voltage gradient.

以神经元为例，这种梯度本身可能在80毫伏左右。但由于膜厚度极小（具体数值我记不清了），这个微小距离上的实际电场强度极大。关键在于，调节电压的离子通道本身具有电压敏感性——本质上就是电压敏感的电流传导机制，即晶体管。有了这种机制，就能实现各种复杂运算。

Now the gradient itself might be on the scale of, let's say, 80 millivolts in neurons, something like that. But because the width across the membrane is actually extremely small, and I don't remember the exact numbers, but it's basically the actual field across that tiny distance is enormous. And what happens then is that you have these voltage gradients and things can happen. One is that the ion channels that regulate the voltage can be themselves voltage sensitive, which means that what you really have is a voltage sensitive current conductance, aka a transistor. And you can imagine that once you have that, you can do all sorts of cool computations.

此外，相邻细胞间还存在电突触，这些突触同样具有电压敏感性。实际上我们面对的是一个网络系统——通过电压状态传播的电路网络。事实上，人体所有细胞都会产生这类电压梯度，大多数细胞通过突触形成网络，利用电势差处理信息。这基本上就是大脑的运作原理。

And then the other thing that happens is that there are these, electrical synapses between adjacent cells, and those synapses are also can be voltage sensitive. So what you actually have is a network. You have an electrical network through which voltage states can propagate. So many cells, I mean, fact, all cells in the body generate these kind of voltage gradients and most of them have these synapses by which they form into networks that process information using the differences in electrical potential. It's exactly what happens in your brain basically.

关于大脑我们稍后再讨论。我儿子曾为与罗杰·彭罗斯的对话3D打印过大脑模型，虽然这次演示没成功...意识问题我们稍后探讨。如果要用电路来量化的话——说到电路，不妨引用您祖国俄罗斯（或者说苏联时期？您是在苏联出生的吧？）的基尔霍夫定律？

Yeah, so we'll get to the brain later. I've got a three d printed brain that my son made for a conversation with Roger Penrose at one point, failed here, but I couldn't get it to work. So we'll talk about consciousness later. But I guess if we had to quantify it as a circuit, know, and let's supply Kirchhoff's laws from your native Russia as I understand or I guess you were born in the Soviet Union. Is that true?

没错。基尔霍夫提出了著名的电路定律，阐述了电流、电压和电阻之间的关系。让我们详细分析一下。

True. True. Yeah. So Kirchhoff had these famous laws that governed how current voltage resistance are related in a circuit. Let's walk through it.

具体涉及哪些电势参数？您能否谈谈电压、电流、电阻、电导乃至磁场在理解生命体的生物化学和生物电活动中的作用？

What kind of, you know, potentials are we talking about? What kind of voltages, currents, resistances, conductances, and even magnetic fields if you have anything to say about how those might play into understanding the chem biochemistry and bioelectricity of life?

好的。对。所以细胞中典型的电压变化范围在几十毫伏量级。在神经细胞中，静息电位通常在负70毫伏左右，假设是负80毫伏，内部比外部更负。而在更古老的非神经生物电系统中，典型体细胞的电位范围大约从负10毫伏到负70、负80毫伏左右。

Sure. Yeah. So so so the typical voltage changes that you're talking about in cells are on the scale of tens of millivolts. So so what you will have are so so in the in the in the neural case, have cells that normally sit at about, I don't know, minus 70, let's say minus 80 with the inside being more negative than the outside. And then in the non neural bioelectricity, which is the far more ancient version, in typical somatic cells, you will be anywhere from, let's say, negative 10 millivolts up to, again, maybe minus 70, minus 80, something like that.

真正重要的是细胞与其相邻细胞之间的电位差。细胞实际追踪和解读的是空间分布模式。2000年我们率先开发了能在大脑外部读写这种电信息的技术工具——虽然神经科学领域早就开始研究这类现象了。

So it's the differences in that voltage between a cell and its neighbor that matter. So what cells actually track and interpret are the spatial patterns. Now, the differences across space, you have a whole bunch of cells in there. If you look and we developed back in 2000, developed the first tools to read and write this electrical information outside the brain. Neuroscience has been doing it for a long time.

我们研发了首个分子工具，能直接拍摄这些电压梯度在组织中的静态图像和动态视频，观察它们如何远距离传播。正如你提到的磁场问题——带电粒子的运动当然会产生磁场。

We developed the first molecular tools to literally take a picture and then a video of these voltage gradients and tissues. So you can see them propagating across distance. And, it is it is those differences that cells read. Now, as you as you mentioned, magnetic fields. So typically, of course, movement of charges absolutely makes magnetic fields.

对吧？大脑产生的磁场相当可观，因为电压脉冲变化极快（毫秒级）。这种dV/dt会形成很强的磁场，人们用各种设备都能检测到。而我们研究的非神经生物电变化非常缓慢，其感应磁场强度微乎其微。

Right? And the magnetic fields in the brain are quite sizable because the voltage spiking is so fast. So, you know, milliseconds, we're talking about milliseconds change. And so that generates a, you know, that DVDT generates a pretty, a pretty good magnetic field, which people of course read with, various devices. The non neural bioelectricity that we deal with changes very slowly and the magnetic fields that are induced there are extremely low.

虽然这么说可能会引发争议——目前没有证据表明生物体会响应这种极弱磁场。但必须承认，生物体确实能感知磁场（比如约0.5高斯的地磁场）。非神经生物电产生的磁场弱到难以置信，尚无证据表明其有生理作用，但电位变化确实至关重要。

I'm not going to say respond to those, but there isn't any evidence that I'm aware of right now that those extremely low level fields now having said that, I know I'm going get in trouble. People are going to yell at me because absolutely living things do care about magnetic fields. They sense the earth's geomagnetic field, which is about half a Gauss, something like that. So those things are absolutely important, but the magnetic fields induced by non neural bioelectricity are unbelievably weak and I don't know of any evidence that they play a role. The electric changes absolutely play a role.

是否存在某些物种的电生理特征中，生物电的重要性表现得不明显或缺失？

Are there species that don't have as manifest an importance of bioelectricity in their electrophysiology or lack thereof?

让我想想...比如秀丽隐杆线虫这种 nematode（线虫），它们确实有神经元和神经生物电。但目前尚未发现关于生物发育过程中生物电重要性的证据——当然这个领域正在研究，结论可能会改变。

Well, let's see. I mean, so so for example, something like C. Elegans, the nematode. I mean, have neurons, they definitely have neural bioelectricity, But I'm not aware of, any evidence yet. Now there are people working on this, and so this could totally change, but I'm not aware of, any stories about the importance of bio developmental bioelectricity in that model.

我的猜测是它会改变。我认为进化非常青睐这种现象。最初是在细菌生物膜时期发现的这些现象。UCSD的Groll和Swell有一篇很好的论文，展示了生物膜中类似大脑的信号传递。这就像是一种非常古老的现象。

And and my guess is it will change. Think I think evolution just loves it. It was it it first discovered these things around the time of bacterial biofilms. So there's a there's a very nice paper, by a Groll, Swell from UCSD who shows brain like signaling in biofilms. So it's like it's a very ancient phenomenon.

是的，我们有个叫Alison Miotry的研究员研究过大脑类器官，还把它们送上了太空。不过当我思考这个问题时——我得继续沉迷于电路物理，因为我热爱焊接这类操作（虽然在人体内会相当危险）。看着这些几十到几百毫伏的电压，再想想人类细胞其实与秀丽隐杆线虫没有本质区别，只是规模放大了万亿倍。这怎么可能从几乎检测不到电流的状态，发展到我们能轻松测量宏观电压的程度？

Yeah, we've had an Alison Miotry who's worked on brain organoids and launched them into space and a little bit about it. But when I look at the, know, I'm going to keep nerding out about the physics of circuits because I love to solder and do stuff, although that'd be quite dangerous in the human body. But, you know, looking at voltages at the, you know, tens to hundreds of millivolts, and then thinking about, you know, human cells as, you know, not that dissimilar from C. Elegans or something, you know, just scaled up trillions of times. I mean, how is it possible to go from something where, you know, there's essentially no voltage currents, you know, at a measurable level to actual macroscopic voltages that we can measure quite easily here?

我可以用Apple Watch测心率对吧？我知道这是事实。那么距离我们用手表测脑电图还要多久？有些冥想程序号称已经能做到这点了。

I can measure my my heart rate using my Apple Watch. Right? I mean, I I know, that that to be a fact. So and how long before we have electro you know, encephalograms on our on our wrists? I have some meditation program that supposedly does that already.

所以请带我们梳理下这种还原论或反还原论的思路——我们是如何从真核细胞（或原核细胞，我记得原核细胞更早出现？）里几乎什么都没有的状态，发展到具有复杂电生理特征的蠕虫生物，再进化到人类这种能产生毫安级电流和十分之一伏特电压的生物的？

So walk us through the the kind of reductionist or antireductionist. You know? How do we go from basically nothing in in, you know, eukaryotic cell or prokaryotic cells, I think, came first, right, to then these eukaryotic cells and and even complex worms and creatures and so forth that have very little electrophysiology to humans, which have huge amounts by comparison of, you know, milliamps and and and millivolts or, you know, in tenths of volts.

明确地说，我还没发现哪种生物（包括细菌和各种微生物）不具备这些现象。这些特性贯穿生命始终。我提到秀丽隐杆线虫时，只是指尚未发现该物种中生物电对形态发生的作用。

Yeah. Well, I mean, to be clear, I'm not aware of any creature, including bacteria and various microbes that don't have these phenomena. These go all the way down. When I mentioned C. Elegans, I simply meant that I wasn't aware of a morphogenetic role in that species for bioelectrics.

Kaushalan的研究将细菌作为神经元模型。想象生命最初阶段：某种膜结构首次尝试分隔内外环境。对，就是那种试图留住有益物质并阻挡外界危险的屏障。一旦实现这种分子隔离，由于内部物质的聚集，必然会产生电位失衡。

But mean, Kaushalan studies did where he talked about bacteria as a model neuron. Basically, if you think about the earliest steps towards life, imagine you've got some kind of membrane that is the first attempt to separate the inside from outside. Yeah. That you've got some kind of barrier that tries to keep the goodies in and the and the dangerous external world out. Well, as soon as you've done that, as soon as you've you've segregated molecules, chances are you're going to have an an electrical imbalance because you're keeping something inside.

当物质逆浓度梯度富集时就会产生电压差。这时物理法则会免费馈赠些奇妙现象——比如这种系统若被刺伤（假设膜被戳破），损伤处会立即产生修复电流试图平衡电位差。于是无需额外进化机制，系统就自动获得了损伤定位功能。

You're you're you're concentrating it against the gradient. You're gonna have a voltage imbalance. And when you do, then then some other very cool things happen as free gifts from physics. So for example, if you have a system like this and you poke it, let's say it gets injured, right, it runs into something that that that injures and pokes a hole through the memory, Immediately, that you're gonna have, an injury current that's gonna try to go through that location to try to equalize the voltage rating. And so for free now, without having to evolve any kind of additional mechanisms, you now have a vector to the damage.

你清楚地知道损伤的位置。电压下降导致去极化时，你立即意识到自己受伤了。我敢说这就是疼痛最初的物理关联。电场线走向会即刻标示出损伤部位——这些信息都是与生俱来的。

You know exactly where your damage was. And you you know that you've been injured because now your voltage is dropping, so you're depolarizing. I I would I would venture to guess that that's the first physical correlate of pain. And and and and you also know immediately where the damage is because because that's where the field lines are going. And so all of that you get you get for free.

这只是生物学利用物理学原理实现的众多‘免费午餐’中一个奇妙案例。

So so this is just, just one example of the many amazing, kind of free lunches that that that biology makes makes use of in physics.

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That's the transformation with Superhuman. It's the leading AI native email app, one that never misses proposals, never forgets referee replies, and always knows what matters most. From your family to your friends to your academic colleagues, students, more, take thousands of unread emails into automatic organizational bliss. Auto labels and auto archive clear the clutter in my inbox. Split inbox keeps urgent messages in focus.

Superhuman日历的即时会议和AI助手让日程安排如魔法般轻松。从错失机会到AI自动跟进，草稿生成比脉冲星闪烁还快，完美定时且符合你的行文风格。自动提醒和草稿功能让你永不掉链子，就算合作者像波函数坍缩般拖延回复。这就是我从焦虑崩溃到掌控自如的情绪转变。

At Superhuman Calendar, Instant Event, and Ask AI makes scheduling seem like magic. From missed opportunities to automatic follow ups with Right With AI, drafts materialize faster than a pulsar pulses, perfectly timed and put into your voice. Auto reminders and auto drafts mean you never drop the ball. Even if your collaborators take their sweet time collapsing their email reply wave functions. This is the emotional shift that I've experienced from stressed and overwhelmed to in control and mentally clear.

没有收件箱奇点，只有专注、清晰和终于能阅读那些你引用却未打开的论文的时间。挣脱邮箱的引力束缚，体验Superhuman的蜕变。点击下方链接可享一个月免费试用。说到医疗技术对物理学的借鉴，除颤器就是个例子——它能瞬间释放千伏电压30安电流，但仅持续毫秒级。据我理解，心脏依赖规律性而大脑需要不规则性，虽然我这里可能用错了术语。

No inbox singularities, just focus, clarity, and time back to finally read that paper you've been citing without actually opening. Escape the gravitational pull of email inbox overwhelm and experience superhuman transformation. Get started with one month free of Superhuman today using my link below. So when I look at the kinds of ways that that medical technology has been, you know, brought over from physics, I mean, one of those is, something like a defibrillator, which, you know, will provide, you know, on a very short time scale, still, you know, huge amounts, you know, 30 amps or something current at a thousand volts, but only for milliseconds of time. And as I've understood it, you know, the heart thrives on regularity and the brain sort of thrives on irregularity, although I'm kind of I'm certainly messing up, you know, the terminology here.

但我们能不能讨论下细胞是怎么知道的？我是说基因型的一部分，但它们怎么知道呢？对脑细胞来说，配备除颤器这类设备会非常有害，那基本上就像是被禁止的电休克疗法。而心肌细胞则会在节律失常时‘求之不得’。这些生物电特性是如何在细胞中具体化的？而不是简单地随机分布，让每个细胞都对电压、电流、磁场、频率等做出相同反应？这到底是怎么运作的？

But can we talk about like how does how do the cells know, you know, I guess part of the genotype, but how do they know, you know, for a brain cell it would be very damaging to have a defibrillator sort of device, I mean that would be maybe like electroshock therapy or something which is basically outlawed versus a heart cell which says you know bring it on when I'm not feeling rhythmic. How do these bioelectric properties get instantiated in the cells, you know, rather than just kind of being scattershot through in every cell basically reacting to voltages, currents, magnetic fields, frequencies, all the same? How does that work?

是的。实际情况是，这些细胞存在多层次的电路结构。单个细胞内部就存在由不同离子通道构成的回路——有的传递正电荷离子，有的传递负电荷离子，它们对电压的敏感度各不相同。你可以想象组装某种调节器，就像生物稳态机制那样，将电压维持在合适的范围内。

Yeah. Well, what happens is that, these these cells, there there are, multiple scales of, of of circuits here. So within a single cell, within any one cell, there is there is a circuit formed by the different ion channels that exist. So you've got ion channels, that pass different kinds of ions, so positive and negative charges, and they have different sensitivities to the voltage. In other words, you can very easily imagine putting together some kind of a regulator that's going to keep the voltage roughly in, you know, some kind of homeostat that's gonna keep the voltage in roughly the right category, range.

这是第一层机制。第二层在于这些细胞会形成网络——单细胞生物如阿米巴虫确实独立存在，但在多细胞生物体内，所有细胞都连接成网络。于是你现在拥有的是‘回路的回路’。

So so that's that's the first thing that happens. The second thing that happens is that now you have these, and they are arranged in a network because they're not sitting there by themselves. Well, in unicellular organisms, they are amoebas and things like that. But but in a multicellular body, all of these things are connected into a network. And so now you have a circuit of circuits.

对吧？这样就从组织层面推进到器官层面，形成‘回路的回路的回路’。每个层级都有其鲁棒性特性，各自的敏感度和阈值，当特殊事件发生时就会触发对称性破缺，启动各种放大过程。

Right? And and and so now you've got to the tissue level and then the organ level and and so on. And so you've got circuits of circuits of circuits. Each level here has its own robustness properties. Each level has its own sensitivities and thresholds such that when something interesting happens, it does kick off, it does do symmetry breaking and kick off various kinds of, amplification processes.

而且存在不同类型的计算：有些细胞利用电压计算，有些以细胞群、组织或器官为单位计算。最酷的是我们现在能模拟所有这些——我的同事Alexis Pytak开发的Betsy系统（生物电组织模拟环境）就是首个此类平台。

And, and you've got different kinds of computation. So there are cells that are using these voltages to compute. There are cell groups and tissues and organs and so on. So all of these things and we now the cool thing is that we now have simulators of all of this. So the first one was my my colleague Alexis Pytak, created this thing called Betsy, the, Bioelectric, Tissue Simulation Environment.

你可以布置一堆细胞，设定要表达的通道类型，然后运行模拟观察结果。还能进行各种酷实验，对吧？

And you can you can you can lay out a bunch of cells and you can say, here are the channels that are gonna be expressed. Now go run. Tell me tell me what's gonna happen. And then you can do all kinds of cool experiments. Right?

比如你可以测试：当像涡虫实验那样切除部分组织后，剩余部分的电压模式会怎样变化？如果直接杀死部分细胞会发生什么？你能观察到系统重新调整尺度，有些细胞还具有惊人的记忆特性——它们会记住电压变化并保持状态，就像电子存储器那样。

So so you can say, okay. Does it do pattern completion the way that we see in flatworms where you cut off a piece of it and you say, happens to the to the rest of the voltage pattern if we just cut you know, we just killed a bunch of cells? What happened? And and you can watch these things rescale and, you know, some of them have really interesting memory properties such that when you change the voltage, they remember it and they keep it. And so like a like a like a like an electric memory.

是的，我们现在已经拥有了这类模拟器，这实际上是一个非常活跃的研究领域，旨在预测和干预。可以说，如果我想要电压达到某个特定值，我需要采取什么措施？这就是

So, yeah, so we we have simulators now for all this stuff, and this is a very active, area of development actually to be able to predict and infer intervention. So you could say, you know, if I wanted the voltage to be this or that, what would I need to do? And so that's the

计算方面的问题。是否需要植入设备，还是需要外部刺激响应？这些是如何实现的？

computational side. Would there be any implantables or would that involve external stimuli in response or how is that brought into effect?

对，对。好问题。目前这项技术其实并不涉及电极。

Yeah. Yeah. Great. Great question. The technology for this actually right now is really not about electrodes.

电极确实擅长两件事。非常擅长。这就是植入式设备的理念。它们非常擅长刺激神经元。在外周神经系统和迷走神经等领域已有一些出色的研究，能够通过这种方式控制神经系统。

Electrodes are really good at two things. They're really good. So this is the implantables kind of idea. They're they're really good at spiking neurons. So there's some there's some, beautiful work on, the peripheral nervous system and the and the vagus nerve and all this kind of thing, you you know, being able to control the nervous system that way.

它们擅长这个，也擅长建立静电场，细胞会将其作为导向立方体。细胞喜欢在电场中爬行，能感知电场并有偏好。一种会趋向阳极，另一种则会趋向阴极。

So so they're good at that. And they're also good at establishing a standing electric field that would be used by cells as a guidance cube. Cells love to crawl in electric fields. They can sense electric fields and they have preference. One will go anode, the other a different cell type will go cathode.

如果你想促使细胞迁移——比如我们常做的伤口再生——像Min Zhao这样的研究者就做过惊人工作，研究皮肤破损时形成的天然电场。当上皮组织受损时会产生电场。就像我之前说的，电场线会立即指向损伤处。所有迁移细胞都会立即沿着电场线移动，最终到达损伤部位。植入式电极非常擅长这类应用。

And if you wanted to make cells migrate, as we often do, for example, for regeneration towards wounds, Somebody like Min Zhao has done some amazing work on the natural electric fields that are formed when you puncture the skin. When you have various epithelial damage, there's an electric field. This is like the story I was telling before where the the the the field lines are an immediate guide to the damage. So all these migratory cells, immediately start following the field lines, they and they and they hit the they hit the side of damage. So those kinds of things, the the implantable electrodes are really good at.

但它们不擅长——至少目前如此，也许未来有人能找到方法——它们不擅长建立空间上的差分电压电位模式，而这正是我们需要的。为了实现器官形成、肢体再生、肿瘤重编程、修复先天缺陷等目标，需要在空间上建立复杂的电压模式。目前我不知道，也没见过有人能用电极很好地实现这一点。

What they're not good at, at least right now, maybe someday somebody will figure out a way to do it. What they're not good at is at setting up spatial patterns of differential voltage potential, which is what we need here. In order to do the things that we do, induce, organ formation, limb regeneration, tumor reprogramming, repairing birth defects, all these different kinds of things. In order to do them, you need to set up complex patterns of different voltages across space. And I don't know of any way, and I haven't seen anybody develop any way to really do that well with electrodes.

那么我们究竟如何实现这一切呢？我们的做法是利用靶向特定离子通道的化合物——离子通道药物。我们拥有一个计算平台，可以展示当前模式，并提出期望模式。平台会建议需要开启或关闭哪些通道和泵来实现目标转变。

So how so how do we do all this stuff? So what we do is we use, ion channel drugs that are chemical compounds that target, specific, kinds of ion channels. And so we have a computational platform where we can say, this is the pattern we have now. I'd like it to be this instead. Tell me what channels and pumps I would need to open and close in order for them to do that, and the model should give you a suggestion.

然后我们查阅资料库——根据我上次统计，约20%的药物属于离子通道药物。这简直令人惊叹，你拥有一个庞大的电药物库资源。这些药物基本都可重新定向使用，比如原本用于治疗神经症状的药物。

And then we go to the shelf and something like 20%, the last time I looked at it, so 20% of all drugs are ion channel drugs. So you have this incredible yeah, mean, it's amazing. You have this incredible, of pharmacopeia of electroceutables. Basically drugs that can be repurposed. You know, they may be used for neuro neurological symptoms.

它们可能用于内耳疾病等，但若拥有能指示通道开关的平台，就能将其重新定向用于其他用途。

They may be used for inner ear or whatever. You can repurpose them for other things if you've got the platform that tells you which channels do I want to open and close.

若你记得《侏罗纪公园》这部经典电影，杰夫·高布伦说过'生命总会找到出路'，生命总是正确的。但反例是中微子具有左旋特性——有假说认为宇宙射线相互作用可能导致了初始不对称性，至少我个人尚未完全理解。不过这种对称性破缺现象确实存在，就像我们能制造糖分子，据许多研究者所言，甚至能创造人类的镜像版本。

If you remember Jurassic Park, that great movie, know, Jeff Goldblum says something like that, life finds a way. Life is always right. So I I just remember it like that. But the, you know, a counterexample is neutrinos are left handed, and there's been some conjectures that only left handed neutrinos, you know, kind of stemming from cosmic ray interactions could have caused the initial asymmetry, still not entirely understood by me at least. But, but talk about this breaking of symmetry and and kind of the the way that it kind of grows out again from objects we know we can make sugars and we can make a whole mirror version of a human being, according to many people that I've talked to.

这将非常惊人。但手性如何介入其中？是否可能存在与我所在的物理世界（比如磁场）的关联？据我们所知，弱核力就能在某种程度上区分磁场。是否有方法追溯胚胎发育中手性的起源？或许与左右旋极化辐射有关——比如环形极化天线。这种形态不对称性和手性是否可能源于某种物理机制？

So that would be quite striking. But but how does handedness come into this? And is there potentially, again, a link between the physical world that I inhabit of, say, magnetic fields, which which are also, you know, distinguishable at some level using the weak nuclear force alone as we far as we know. Is there any way to to retrace the origin of handedness and embryonic development to handedness, you know, perhaps, due to, polarized radiation where we have antennas that are circularly polarized left to right. For example, is there anything to this notion that the origin of of morphological asymmetry and chirality could come from, you know, some some physical mechanism.

这话题太棒了！左右对称性曾是我的研究方向——我博士论文就专注于此，直到2016年我都在持续研究这个问题。

Yeah. Yeah. This is this is great. I love this is about favorite topic, and and I haven't done work on left right in in quite a bit quite a bit of time, but but I used to. This was my my PhD was in this, and I worked for roughly until 2016, pretty much on this problem.

这是个迷人的课题。首先让我阐述基本概念：大多数动物（实际上远超动物界）包括植物等生物都存在稳定的不对称性。它们本质上具有双侧对称的体型结构，虽然粗略来看镜像两侧相同，但...

It's a fascinating problem. Let me just kind of set up what we're talking about here first, then I'll talk about this, what relationship it might have to symmetry breaking in the universe. The basic fact is that most animals, and in fact it goes well beyond animals, are plants that do it, are all kinds of weird creatures that have consistent asymmetries. They have basically a fundamentally bilateral symmetrical body plan. So you can draw kind of a mid plane and you can say, okay, you know, to first approximation, the reflections are the same.

是的，它们左右两侧看起来相同，但你会发现存在一致的差异。正如你指出的，在人类身体中，心脏、肝脏、胆囊等内脏器官都存在不对称性。大脑两半球也不相同。各种有趣现象层出不穷，比如某些会影响对称结构（如肩膀和髋部）的综合征，往往更倾向于发生在某一侧。

Yeah, they're the same left and right. Except that you find that there are consistent differences. And so in humans, as you pointed out, the heart, the liver, the gallbladder, there's all sorts of the stomach, there's all sorts of organs that are asymmetric. The brain hemispheres are not the same. All kinds of interesting phenomena such as certain syndromes that affect non asymmetric structures like shoulders and hips and things like this sometimes occur more prevalent on one side.

还有许多更明显的生物例子。螃蟹和龙虾通常有一只螯与另一只截然不同。类似的有趣案例比比皆是。我们多年前就发现，黏菌在生长过程中需要转向时，会优先选择某个特定方向。就连黏菌也具备左右对称的感知能力。

There are lots of other creatures that are more obvious. Crabs and lobsters often will have one claw that's quite different from from from another. Many interesting examples like this. You know, there are we showed years ago that slime molds, when they're growing out and they want to turn, they preferentially turn in one direction versus another. Even slime molds have a sense of left right symmetry.

最奇妙的是这种不对称并非随机产生。设计一个随机选择某侧的机制很容易，但要实现方向固定的稳定不对称性——即对称性破缺方向相对于另外两个轴向固定——则困难得多。我以前常对学生说：想象你在遥远星系发现了外星生命，你们之间只有电话联系。

The cool thing about it is that it isn't random. It's easy enough to come up with a mechanism that will pick one side at random. What's much harder is to have consistent asymmetry where the direction of symmetry breaking is fixed relative to the other two axes. I used to, back when I talked about this a lot to students, I would say, imagine that you discovered some aliens far far away. All you had was a telephone connection.

你无法传递实物，只能通过语音交流。当你们互相学习语言、掌握了所有其他词汇后，现在需要定义左右的概念。于是你说：好，假设我站在这里，我的感觉器官都朝向前方。

You couldn't you couldn't give them objects. All you could do is talk via voice. And so you're learning each other's language, you've got all the other words down, and it's time to decide what left and right means. So you say, okay. So so so I'm standing here, and, let's say that, you know, my my my sense organs are are pointing forward.

这就确定了第一个轴向。而我的双脚指向行星重心方向，这给出了第二个轴向。那么我的左手是哪只？这时你就卡住了。

So that's the first axis. And, my feet are pointing towards the center of gravity of the planet. So that gives you the second axis. Now my left hand is the one that what? And now you're stuck.

对吧？问题突然变得极其棘手。除非...我们知道可以用右手定则，或者通过磁场做些什么，或者假设他们和我们处在同类型宇宙中，拥有相同的中微子等等。但在宏观尺度上，这确实非常困难。

Right? Now it gets really hard. Unless right? So as you well know, we could do the right hand rule and we could do something with magnetic fields and we could do the right hand rule, Or or maybe we you know, maybe they live in the same kind of universe as us, and they have the same, you know, neutrinos and and all of that. But otherwise, at a macroscopic scale, it's really hard.

实际上...遗传学本身并不区分左右。我们其实并不...这正是我博士论文的研究方向——发现一组在身体左侧与右侧表达差异的基因通路。后来我在博士后阶段及后续数年里，一直在追溯这个现象的起源，稍后我会告诉你答案。这确实是个极其基础、迷人又充满挑战的难题。

There really there really isn't I mean, genetics doesn't distinguish left from left from right. And so you're you're gonna express, we actually don't. And that's what I did for my PhD is discover, a pathway of genes that are expressed on the left side differently than than they're expressed on the right side. And, and then I spent my postdoc and and some years after that chasing that back to see what the origin is, and I'll tell you what that is. But but it's a really it's a really fundamentally a very fascinating and and difficult problem.

将二十年的工作浓缩成几句话，我可以告诉你，归根结底是一个微小结构的手性问题。它是细胞内细胞骨架的一部分，实际上遵循右手定则。换句话说，它具有手性。细胞...早期胚胎...极早期胚胎具有另外两条轴线。因此它使结构朝一个方向锚定。

And compressing, you know, twenty years of work, into a couple sentences, I will tell you that basically what it boils down to is the chirality of a particular little structure. It's part of the cytoskeleton inside of cells, and it literally does the right hand rule thing. In other words, it has a chirality. The the cell has the cell has two the the early embryo the very early embryo has has two other axes. So so it anchors this in one direction.

它使结构朝另一个方向锚定。然后它有个微小特征会实际指向右侧。这个特征会形成一系列细胞骨架轨道，马达蛋白将沿着这些轨道运输特定物质。它们运输的物质中包括离子通道。正如我们在2002年展示的，这确实形成了左右两侧之间的电压梯度。

It anchors this in the other direction. And then it's got a little little feature that points in in the actually rightward. And that nucleates a bunch of cytoskeletal, tracks along which motor proteins will ride and they take certain cargo. The cargo they take among other things is ion channels. And it sets up literally when we can see, and we showed this in 2002, there is a voltage gradient between the left and the right sides.

这种梯度之所以能稳定存在，是因为这个微小的成核分子使两侧离子通道分布不同。正如你提到的物理学中的对称性破缺...虽然我不是专家，但我曾阅读过相关文献，CPT破坏现象确实令人惊叹。我一直认为这是最神奇的事情。

And it arises there consistently because, this little little nucleating molecule allows the ion channels to to be different on one side than the other. And so, you know, your your point about the the the the the symmetry breaking of in in in physics and so on. I mean, I'm certainly not an expert on it, but I used to read read all this stuff and just this amazing, you know, this the CPT violation. Right? At the at the at the basis of it, I always thought was was the most amazing thing.

这里存在两种可能性，我们尚不确定哪种正确。一种可能是由于宇称不守恒，某些分子对映体的稳定性存在差异——虽然差异微小，但足以被进化选择。这或许能解释为何所有生命都呈现相同手性，这与弱核力中电子发射方式导致的分子稳定性有关。

And, you know, does that actually so so there's two possibilities, and I I don't think we know which one is is right. One possibility, and people have published papers on this, that because of the parity violation, some enantiomers of certain molecules are more stable than others. And the idea is that, not by much, but enough, that evolution might have picked up on this and that the reason that we are all chiral in the same direction is because literally, you know, because of the way that, you know, I think it was that the electrons get ejected in weak nuclear force, you are actually more stable. These molecules are more stable in one direction. That's that's one possibility.

另一种可能是冻结的偶然性——本质上没有重大差异，但首个成功的生命共同祖先恰好选择了某个方向，之后就难以改变。进化过程中充满这种一旦确立就难以改变的特性。我无法确定哪种解释正确，但思考这种机制是否贯穿生命本质确实引人入胜。

The other possibility is that it's a frozen accident. That that basically that basically there is no major difference, but the first successful, universal ancestor, to life just happened to have it going one way, and it was too hard to change it after. You know, evolution's full of these things that once you set it up, you can't change it. So I can't tell you I can't tell you which one of those is is is the case, but but I think it's fascinating to to think about whether it goes all the way down.

我想回到物理学话题，同时提醒观众我们稍后会讨论异种机器人（xenobots）——不是排外情绪，也不是战士公主西娜，马上就会谈到迈克尔这个精彩的合成词。但在那之前，我要问你一个薛定谔提出的简单问题：迈克尔，生命是什么？

I wanna stick to physics, and I want to also let the audience know that soon we'll get to xenobots, not not xenophobia, not Xena the warrior princess, but we'll get to Michael's wonderful portmanteau xenobots in just a minute. But before we get there, I wanna ask you a very simple question to you, is one posed by none other than Erwin Schrodinger himself, Michael, and I think you know what I'm about to say. Michael, tell us what is life?

哈，简单的问题总是最难。有趣的是，我们刚发表了一篇论文：我精选了约70位思想家进行问卷调查，请他们用最多三句话给出定义。

Yeah. Yeah. The simple the simple questions, of course. So so interestingly enough, we just we just put out a paper where we, what I did was I polled about 70, various thinkers that I chose on this question. I I gave them three set up to three sentences, and everybody gave their definitions.

借助一些AI工具，我们实际上为所有定义创建了一个概念空间，用以观察人们对此的思考结构。不用说，目前并没有真正的共识。我要说两点：第一点对生物学家来说可能有些奇怪，但我还是要说——我不认为这是个特别有趣的类别。

And then we had, with the help of some AI tools, we actually created a conceptual space for all the definitions to kind of look at the structure of how people think about this. Needless to say, there is no real agreement. I'm going say two things. One, which is weird for biologists to say, but I'm going to say it anyway. I don't think it's a particularly interesting category.

换句话说，我认为真正有趣的类别是认知光谱，我认为它一直向下延伸。而且我认为，生命是认知的一个子集。我不认为纠结于困难的边缘案例、试图制定非黑即白的生命定义有什么意义——我讨厌这种二元定义。

In other words, I think what's a very interesting category is the spectrum of cognition, I also think goes all the way down. And I think, life is a subset of cognition. I don't think it's important to try to, wrestle over difficult corner cases and try to come up with definitions of life that try to make rulings. Yes or no. I don't like these binary definitions.

我不认为这些定义能促进研究。但如果我们想指出生命的特殊性，我认为人类观察者倾向于将那些擅长扩展其认知光锥的事物称为生命。让我给出定义：认知光锥是指一个系统在特定问题空间中所能追求的最大目标范围。

I don't I don't think they facilitate I don't think they facilitate research in any way. You know? However, if we if we wanted to say what it is that's that that is special about life, I think that we we human observers tend to call life those things that are very good at scaling their cognitive light cone. In other words, what you have are so let me just give a definition. So the cognitive light cone is the size in some particular problem space of the biggest goal that a system can pursue.

它不是效应器的触及范围，也不是传感器的感知范围，而是你能追求的目标状态的大小。例如细菌只关心极小区域内营养物质的浓度——它们只有微弱的预测能力和记忆功能。

It's not the reach of its effectors. It's not the reach of its sensors. It is the size of the goal state that you can pursue. So for example, if you're a bacterium, all you really care about managing is the concentration of nutrients and some other things in a very small area. You have a little bit of prediction going forward.

它们只管理着时空中的微小区域，对其他地方发生的事毫不在意。如果你告诉我你只关心20微米区域内的糖浓度，我会说你很可能是个细菌。

You have a little bit of memory going backwards, but that's it. That little area of space time that you are managing. You could care less what happens anywhere else. You're managing a tiny little area of space time in terms of making efforts to make it be one way versus a different way that entropy would have you go. So if you tell me that all I care about is sugar concentration within this, you know, the 20 micron region, I'm going say you're probably a bacteria.

但如果你关心的是百年后全球金融市场的走势，并为此积极努力，我就会说你很可能是人类。当然还存在许多中间状态——比如狗永远不可能像人类那样关心三周后邻镇发生的事。

If you tell me that that, you know, you're interested in, you're actively working on goals of, what the financial markets are going to look like all over the earth a hundred years from now, I'm gonna say you're probably a human. And there are many in between cases. So for example, if you've got a dog, the dog is never going to, to my knowledge, is never going to be able to care about in the sense of pursuing goals. What's gonna happen three weeks from now, you know, three towns over? Right?

这完全超出了它的认知光锥范围。虽然比细菌强得多，但它就是不会在意——据我所知，你无法改变这点。这些都是需要实验验证的经验性问题。

That's just outside of its cognitive glyco. And it's certainly bigger than than the bacterium, but it just isn't gonna care. And there's nothing you can do to make it care again, as far as I know. These are all empirical things. You have to do experiments to find out.

我认为发生的是非常微小的东西，虽然肯定是细胞层面的，但我认为甚至比细胞更微观。当这些微小物质以特定方式组合时，认知糖原就会上升。举个简单例子：单个细胞有微小的认知标识，它们关注pH值、饥饿程度、电压状态这类局部微小指标，而细胞群体则关注宏大的构建工程。

And so and so what I think happens is that very, tiny things, and it's certainly cells, but I actually think it goes even below the cellular level. There are certain configurations of those things that when they get together, the cognitive glycone goes up. So so I'll just give you a a very simple example. Individual cells have little tiny cognitive icons and they care about things like, their pH level, their hunger level, their voltage state, you know, these kinds of local little tiny things. But groups of cells care about grandiose construction projects.

比如蝾螈肢体被截肢后，细胞会立即察觉它们在解剖空间偏离了正确状态。它们会全力重建肢体，完成后就停止活动。最神奇的是这个稳态纠错系统——当问题解决且误差恢复到可接受水平时，它们就会停止行动。

For example, you've got a salamander limb, You amputate the limb. Immediately, the cells notice that they've been deviated from the correct, state in in in the anatomical space. They work really hard. They they build the limb, and then they stop. That's the most amazing thing is when it's a homeostatic error reduction system, when they've solved their problem and they've reached they've they've reduced the error back to acceptable levels, then they stop activity.

因此细胞群体能追求宏大目标。单个细胞不知道手指是什么或该有几根手指，但集体绝对知道。实验证明：若试图让它们偏离目标，它们会用巧妙方法回归。这某种程度上就是它的定义。那么究竟发生了什么？

So the collection of cells are able to pursue a very large goal. No individual cell knows what a finger is or how many fingers you're supposed to have, but the collective absolutely knows. And you know that by experiment, because if you try to deviate them from their goal, they will do ingenious things to get back there. That's kind of the definition of it. And so I think and so so what has happened?

这正是我们的研究方向：电网如何扩展认知糖原。通过记忆匿名化、压力共享等方式构建的电网，不仅会扩大认知糖原，还能投射到其他空间。单个细胞能触及代谢空间、基因表达空间和生理空间，而细胞群体能触及解剖形态空间。若拥有大脑和肌肉，就能进入三维空间。

And this is what we study. We study how electrical networks scale the cognitive glycol. So what happens is that specifically by memory anonymization, stress sharing, some other things, you make these electrical networks, not only does your cognitive glycol get bigger, but it also projects into other spaces. Individual cells have access to metabolic space, gene expression space, physiological space, but groups of cells have access to anatomical morphospace. If you happen to have a brain and some muscles, now you have access to three-dimensional space.

若掌握语言，就能进入语言空间——天知道还有什么空间。你懂的。当我们看到这种具有多尺度架构，且组件的认知光锥能扩展投射到新问题空间的事物时，我们就称之为生命。此外还有关于解读记忆等话题。本质上，这就是我们所说的生命含义。

If you have access to language, then you're into linguistic space and God knows what else. You get the idea. So I think when we see things like that, that have a multiscale architecture where the cognitive light cone of the parts becomes expanded and projected into new problem spaces, we say that that's that's life. And then there are some other things that we could get into about interpreting your own memories and some other things. Fundamentally, that's what I think is what that's what we mean when we say life.

确实，从按下录音键那刻起，显然我们需要更大的平台——希望未来能有更多这样的交流机会。接下来这个轻松些的问题是：我在教学中发现，就像宇宙学研究宇宙演化时，起源虽是其部分，但‘宇宙为何存在’并不属于宇宙学范畴。同理，我的问题是：你认为生命是如何起源的？

Yeah, well, it's clear you know from the moment I click record that we're going to need you know probably a bigger boat, a bigger podcast, we'll hopefully have many opportunities to do this in the future. The next question on the you know kind of ease of discussion is, you know, sort of a problem I have with my students and I wonder how you approach it too, that in cosmology, you know, we study the evolution of the universe. The origin is part of it, but the question of, you know, what caused the universe to be in existence is not really part of the of the process of cosmology. And so likewise, my next question is going to be about, you know, what caused life to begin? What is the origin of life as you see it?

要是能提到‘泛种论’就更好了——我常赠送来自早期太阳系的陨石，任何美国境内持有.edu邮箱的人都能领取。迈克尔你会收到一块，你推荐学生注册BrianKing.com/edu的魔法周一邮件列表的话，他们也能获得。

And if you can throw in the word panspermia, that would be helpful for me because I give away these meteorites that, have come from the early solar system. I give them away to everybody who has a dot e d u email address that lives in The United States. So you are gonna get one, Michael. I'm gonna ship one to you. I love any any of your students that you get to sign up for my magic Monday mailing list at Brian King dot com slash e d u.

如果你拥有.edu邮箱地址，这些精美仪器将通过美国邮政服务（而非像我这样靠重力）寄送给你。如果没有，我可以将部分设备赠予那些无法像你我这样享受大学资源的人，迈克尔。具体领取请访问brian keating.com/yt。迈克尔，请告诉我们生命是如何起源的？

If you have an e d u email address, you'll get one of these beauties shipped via, not gravity the way I got it, but, via the US Postal Service. But if you don't, you can I give away some to people that don't have the luxury and the sloth of being like you and me at a university, Michael? So I give those away at brian keating dot com, slash y t. So please do, take us up on that. Michael, tell us how did life originate?

好吧。关于泛种论我没什么可说的，我并不太担心这个问题，因为...抱歉。但这本质上只是把问题推给了其他地方。

Okay. I don't have much to say about panspermia. I'm not terribly worried about it because that yeah. Sorry. But but but that basically just puts it off to somewhere else.

对吧？从这个角度看，生命必然源自他处。所以我不会过分纠结于此。我可以分享几个关于生命起源的有趣观点。其中一点是——重申下，相比生命本身，我更关注心智与认知，实际上我认为后者是前者的超集。

Right? In that sense, it has to be coming from somewhere else. So I'm not going to worry about that terribly much. I can I can say a couple of things that I hope are interesting about, you know, how how life, might have originated? One of the things that, and again, I'm not as worried about life as I am about mind and cognition, and I actually think that that's a superset of living, of things that we recognize as living anyway.

我们发现了一个非常有趣的现象——相关论文几天前刚发表。想象一个分子通路模型：假设有10种分子，彼此间存在上下调控关系，可以绘制出正负相互作用的微型网络，这些化学物质能激活或抑制彼此活性。这是我们几年前的研究成果，来自我课题组的萨拉玛·比斯瓦斯的工作。

There's a really interesting phenomenon that we found and that we put actually this paper just came out a couple of days ago, actually. Imagine a model of a molecular pathway. So you've got, let's say 10 different molecules, each one basically up and down regulates some others. So you can draw a little network of positive and negative interactions chemicals that turn each other on and off or potentially in each other's activity or suppressant. Turns out that, and this is something we did a couple of years ago, and this was Sarama Biswas's work in my group.

我们的研究表明，即便是这些极其简单的分子网络（更不用说细胞、大脑或突触了）——

What we showed was that even even those very simple molecular networks, never mind cells or brains or synapses or

那些东西

any of that

——仅仅是一小组能相互激活抑制的化学物质，就已经能实现六种不同类型的学习：习惯化、敏感化、巴甫洛夫条件反射、联想学习等。这些能力远在进化发挥作用之前就已存在。当然，进化会将其优化到极致。

stuff, just just a small group of chemicals that turn each other on and off already was, able to do six different kinds of learning. They can do habituation, sensitization, and they can do Pavlovian conditioning. They can do associative learning. So that you get long before evolution kicks in. Now evolution, of course, is optimize the hell out of it.

我们确实发现生物网络——真实的生物网络在这方面比随机网络表现更好。不过即便是随机网络也具备些许这种特性。最近我们还发现另一件事——这项成果前几天刚发表，是Federico Pagosi的研究——通过计算因果整合度，粗略来说，这套新数学方法能量化系统在多大程度上超越其组成部分的简单叠加。这个曾属于哲学范畴的命题，关于是否存在更高层级的因果关系，或是还原论与整体论之争，通过包括我研究中心的Eric Houltle、Giulio Tononi、Olaf Sporns等众多学者的工作，如今已发展出数学分支来实际量化。

We did find that that biological, like real biological networks do better at this than random networks. But even random networks do this a little bit. And the other thing we found out just recently, and this is this is a thing that was that was published the other day, and this is Federico Pagosi's work, that if you compute measures of causal integration, which is to say very roughly, it's a new set of mathematical techniques that allow you to quantify to what extent is something more than the sum of its parts. Idea that used to be a philosophical thing is, are there any higher levels of causation or is it reductionism versus holism? That that used to be a philosophical argument through through the work of, of a number of people, including, Eric Houltle, who works, at my at my center here, and, and Giulio Tononi and Olaf Sporns and and some others.

在某些系统中确实可以进行这种计算。你可以明确判断：究竟是各部分在发挥作用，还是确实存在超越部分功能的更高层级。我们将这套数学方法应用于各类研究，特别是这些通路模型。

There's been a branch of mathematics developed that can actually quantify that In in certain systems, you can literally do the calculation. You can say, okay. Yeah. The the parts are doing all the work or the actually, there is a higher level that's doing something that the parts aren't doing. So we can apply this math to all sorts of things, and we applied it to these to these, pathway models.

我们发现每当系统学习新事物时，其因果涌现度就会提升。换言之，这种通过刺激响应进行训练的过程——意味着未来你会以不同于初始状态的方式作出反应——使你的整体协调性不断增强，不再仅是部分的简单加和。

And we found out that every time they learn something, their causal emergence goes up. In other words, the process of the process of of responding to stimuli in a way that trains you, meaning that you will respond to them in the future differently than you responded to them fresh going in, That process makes you more than raises the amount by which you are a coherent whole integrated entity, not just a sum of parts.

迈克尔，请允许我以个人角度提及：你在同事丹尼尔·丹尼特去世后撰写了感人至深的悼文。我有幸在他临终前完成了他的最后一次播客访谈。显然这发生在他离世之前。能否谈谈他对你超越实验室范畴的影响？特别是他的思维模式，似乎对你影响至深——毕竟他离世才约一年。

Michael, on a personal note, you had a wonderful obituary after the death of your colleague, the late great Daniel Dennett. I had the honor of doing his last podcast interview right before he passed away. I guess it's obvious. He he did it before he passed away. Can you talk a little bit about this, the impact that he had maybe beyond the laboratory and so forth, but what his modality of thinking, it seemed to really have affected you and it's only about a year since he passed away.

他确实是位非凡的人物。我青少年时期就开始阅读他的著作，那边书架上全是他写的书。能在塔夫茨大学本科期间选修他主讲的心灵哲学课程，我感到无比荣幸。那大概是1991年左右的事。

Yeah, was an amazing person, know I grew up reading his books. I have all his books back there that I read when I was a teenager and beyond. I felt incredibly privileged to take a course with him when got to Tufts as an undergrad. He taught the philosophy of mind course. This would have been in '91 or something like that.

我选修了他的课，他令人叹服。需要说明的是，我们并非在所有观点上都一致，在这个领域确实存在诸多分歧。但他首先是位真正的绅士，对任何形式的争强好胜毫无兴趣。

I took that course with him and he was incredible. Now to be clear, we don't necessarily agree on everything. There are many things that we didn't actually agree on in terms of this field. But he was he was an amazing first of all, he was a gentleman. He was not interested in any kind of games, in one upmanship, any of that stuff.

他真正关注的是通过互动推动论证精进，促成更深刻的理解。他总能清晰表达，并帮助周围人厘清观点，继而共同探索可能。他始终温和友善，慷慨分享时间、思想和建议。当我回到塔夫茨任教后，我们还合作过一些项目。

He he he was really interested in, getting to a better argument, to a better version of, whatever understanding we could all get by interacting with each other. And he was just incredible in terms of being clear getting everybody else around him to be clear about what they were trying to say and then just seeing what we could make of it. He was always kind and he was always generous with his time and with his ideas and with his advice. Yeah, I couldn't say more nice things about him. So, you know, when I came back to Tufts as a faculty member, we collaborated on some things.

我们曾合作撰写了一篇论文，探讨认知的底层机制，这让我非常自豪。是的，他是个了不起的人，一位杰出的导师，我认为他思想非常深邃。确实，

We wrote a paper together, which I'm very proud of, talking about, cognition all the way down. And, yeah, he was an amazing person, an amazing mentor and a very deep thinker I think. Yeah,

他去世时我深感悲痛。至少我采访过天启四骑士中的三位。虽然没机会采访克里斯托弗·希钦斯，但丹无疑位列那份杰出名单的顶端。那么现在，我希望为第一部分（期待未来还有更多部分）作结，如我之前承诺的，要谈谈你们关于异种机器人的精彩研究。首先，什么是异种机器人？它们如何像生物体般运动、愈合甚至协作——在没有大脑或神经系统的情况下实现这些？

I was very distressed when he passed away. Got to interview three of the four horsemen of the apocalypse at least. I never got to interview Christopher Hitchens but Dan was certainly you know at the very top of that very August list. So okay I want to finish up for this part one of hopefully many parts together and that's with your wonderful work as I promised earlier about xenobots. So first of all, what are xenobots and how are they behaving like living organisms moving, healing, even cooperating, working together without any brain or nervous system.

嗯，明确地说，许多生物都没有大脑或神经系统，但它们能完成各种有趣的行为。不过...

Well, I mean, to be clear, there are many, organisms that don't have a brain or nervous system and they do all kinds of interesting things. But but That's

所以我常对教师俱乐部的同仁们说——要知道水母没有大脑已存在了六千五百万年。所以你还是有希望的。不是说你，迈克尔。

why I say I call those colleagues of mine at the faculty club, know. I say jellyfish have existed without brains for sixty five million years. So there's hope for you yet. Not not you, Michael. But

是啊，没错。嗯，对。你...你说得对。我想...确实。

Yeah. Yeah. Well, yes. You you said it. I think But, yeah.

你看，首先让我解释什么是异种机器人。这是我校实验室与佛蒙特大学乔什·邦加德的合作项目。我们成立了ICBO（计算生物体设计研究所），异种机器人算是这个计划的首个成果。计算机科学部分由乔什实验室的萨姆·克里格完成，生物学工作主要由我团队的道格·布莱基斯顿负责。

I mean, look, here here's first of all, I'll tell I'll tell you what the xenobots are. So this joint work between my lab and Josh Bongard at the University of Vermont. And, we are, we've organized this thing called the ICBO, which is the Institute for Computationally Designed Organisms. The Zenobots are kind of the first, the Fuwa, we're the first, Boussoujo from that whole effort. And, the computer science part of this was done by Sam Krieger in Josh's lab, most of the biology was done by Doug Blakiston in my group.

我们的基本发现是：如果从早期青蛙胚胎中分离出部分前体皮肤细胞（因此得名异种机器人，使用非洲爪蟾的拉丁学名Xenopus laevis）。这些青蛙产卵形成胚胎后，我们在极早期阶段分离出将形成外层皮肤覆盖的细胞。关键在于，正常情况下这些细胞会被胚胎中其他细胞'胁迫'执行特定任务——形成单调的二维胚胎外膜来阻挡细菌。但在自然状态下，由于受其他细胞调控，它们只能发挥这种有限功能。

What we basically found out is that if you isolate some prospective skin cells from the early frog embryo, and this is why they're called Xenobots. Xenopus lavus is the Latin name for the frog that we're using. And so this frog lays eggs, they become embryos, at a very early stage we isolate some cells that are going to become kind of like an outer skin covering. And the deal is that under normal circumstances, these cells get basically get bullied by the other cells in the embryo to do a very specific thing, to be this boring outer two dimensional, kind of covering for the embryo, keep out the bacteria, and that's that. That's what they do in the natural state because they are hacked by these other cells that are there.

如果你解放它们，具体来说就是不添加任何新的遗传物质，没有合成生物电路，没有基因组编辑，没有奇怪的纳米材料或药物之类的东西。我们目前所做的只是将它们从通常受到的影响中解放出来。然后你可以问，它们是什么，它们还想做什么？这有点像它们多细胞性的重启，它们可以做很多事情。它们可能会彼此爬开，也可能会死亡。

If you liberate them, so very specifically not putting in any new genetic material, no synthetic biology circuits, no genomic editing, no weird nanomaterials or drugs or anything like that, All we have done at this point is liberate them from the influences that they normally get. Then you can ask the question, what are they what else do they want to do? It's kind of a reboot of their multicellularity and they could do many things. They could they could crawl away from each other. They could die.

它们可以形成一个二维薄片，一个整体，做很多事情。但它们实际做的是聚集在一起，形成这个小球，小球表面覆盖着纤毛。这些微小的运动毛发会组织起来，纤毛开始摆动，基本上排列起来使这个物体能在水中游动。通常这些纤毛用于在青蛙体表分布黏液，但现在它们可以游泳了。

They could form a two dimensional sheet, a monolithic, do many things. What they instead do is they get together. They form this this this little this little ball, and that little ball is covered with cilia. These are little motile, tiny motile hairs that then organize, and they start the the the cilia start start waving, and they basically align so that the thing can now swim through the water. Normally, these cilia are used to distribute mucus over the body of the frog, but now now now they can swim.

于是它们就在水中游动。我要告诉你们它们实际上能做的几件事。显然，它们是活的生物体，由活细胞组成。除了作为生物机器人平台之外，它们最有趣的地方在于。

And so they're they they swim around in in the water. And and I'll just tell you a few things that, that it turns out they're capable of. Now, you know, they're obviously living organisms. They're made of living cells. The major interesting thing about them beyond the fact that they're a biorobotics platform.

也就是说，一旦我们理解它们的工作原理，我们就有可能将它们用于各种很酷的应用，比如清理环境，以及无数其他事情。但另一个很酷的地方是，与真正的青蛙不同，如果你问是什么决定了青蛙的特性，大家会说，这是针对特定环境的选择历史。追溯到最初，通过环境测试并淘汰不适应的个体，才设计出青蛙的样子。这就是为什么青蛙看起来像青蛙。但异种机器人从未有过这样的选择过程。

So that is we could we could once we understand how they work, we could potentially use them for all sorts of cool applications, cleaning up the environment and just a million different things. But the other cool thing about them is that unlike the actual frog, if you ask about what sets the properties of the frog, everybody will say, well, it's a history of selection against specific environments. Going all the way back, that's when the computations were done to design what a frog is, is by testing things against the environment and killing off everything that didn't work. That's why the frog looks like a frog. Well, there's never been any xenobots.

从未有过任何选择让它们成为好的异种机器人。以下是它们的一些行为：首先，它们表达的数百个基因与在体内时不同，因此具有完全不同的转录组。在这些基因中，有许多有趣的现象，我将挑选一个来讨论。

There's never been any selection to be a good xenobot. And here are some things they do. First of all, they express hundreds of genes differently than they would have in the body. So they have a completely different transcriptome. Among those genes, many interesting things, I'll just pick one to talk about.

它们表达了一组与其他生物听觉相关的基因。我们实际测试时，在培养皿下放置扬声器播放特定频率，发现它们确实会根据你给予的声波振动改变行为。它们还会做另一件疯狂的事——我们称之为运动自我复制：如果你在培养皿中撒入一些游离的上皮细胞，它们会实现冯·诺伊曼梦想中的机器人行为——四处移动并用环境中找到的材料自我复制。它们会单个或集体地四处游走，将这些细胞聚集成小球。

They express a bunch of genes that and other creatures are related to hearing. And so we actually tested that and we put a speaker underneath the dish and we played certain frequencies and we found out that, yeah, in fact, they changed their behavior depending on the the sound vibration that you're giving them. They they do this other crazy thing we call kinematic self replication, which is if you sprinkle a bunch of loose epithelial cells into the dish, they do what, the kind of Von Neumann's dream of a robot that goes around and find and makes copies of itself from parts it's fine from materials it finds in the environment. They will literally, both singly and as a collective, will go around. They will collect these cells into little balls.

由于这些细胞本身是具有自主性的材料（就像我们最初使用的细胞一样），这些小球会成熟并成为下一代异种机器人。猜猜它们会做什么？完全一样的事情——四处游动，制造下一代。

And because the the cells themselves are an agential material, just like the ones we started with, the little balls mature and become the next generation of xenobots. And Guess what they do? They do exactly the same thing. They run around. Make the next generation.

那么现在，据我们所知，地球上没有其他生物以这种方式繁殖。我认为地球上从未出现过运动性自我复制。这些生物机器人和人造结构提出了非常有趣的问题：如果新生物没有进化史，它们的特性从何而来？这是我们作为社会正在面对的问题，随着人工智能、赛博格、混种生物以及我们创造的所有这些奇异事物的出现，它们的特性究竟源自何处？

So now, you know, there's to our knowledge, no other creature on Earth reproduces this way. I don't think there's ever been a kinematic self replication on Earth. So they raise these kind of bio bots and other kinds of constructs raise these very interesting questions of where do the properties of novel beings come from if they don't have an evolutionary history? This is something that we are now confronting, as a society with AIs and with cyborgs and hybrids and all these weird things that we're all making. Where do their properties actually come from?

这里我们暂且搁置，也许下次再讨论，因为我认为这实际上涉及数学真理来源的柏拉图形式空间。不过这就是异种机器人。我们还有另一个由我课题组的Gesem Gomushkaya博士开发的项目，我们称之为人形机器人——因为有人说异种机器人用的是两栖动物胚胎细胞，可能具有特殊性。好吧。

Here, we had, you know, maybe next time we'll talk about it because I think it is this this platonic space of forms where where the truths of mathematics come from, actually. But but so that's xenobots. And we have another thing that was, developed by Gesem Gomushkaya in my group who was a PhD student. We call them anthrobots because some people said, well, xenobots, you know, the amphibians are kind of plastic and it's embryonic, so maybe it's a it's a one off kind of special thing. So fine.

距离青蛙胚胎最远的是什么？成年人类。让我们试试这个。于是我们取用成年患者的气管上皮细胞，结果发现它们也能聚集形成这些微小的运动生物。

What's the what's the furthest you can get away from from embryonic frog? Well, adult human. Let's try that. So we took cells from adult human patients, tracheal epithelial cells. Turns out that they too, come together and form these little motile creatures.

我们称之为人形机器人。这些小家伙有9000个差异表达基因，它们拥有完整的基因组对吧？而且能做很酷的事，比如修复神经损伤。

We call them anthrobot. Those guys have 9,000 differently expressed genes. They have the genome then. Right? And they can do cool things like they can heal neural wounds.

如果你把它们放在人类神经元培养层上，用手术刀划出深痕，它们能沉降到伤口处形成我们所谓的超级机器人集群。四天后移开时，你会发现它们将断裂两侧重新连接愈合。同样，历史上从未出现过人形机器人，也从未有过针对优秀人形机器人的自然选择。我们必须理解这些能力从何而来。

So if you put them on a bed of human neurons, you take a scalpel, put a big scratch through it, they can settle into that wound and form what we call a super bot cluster. And when you lift them up four days later, what you see is that they took the two sides of the gap and they healed them together. And again, there's never been any anthrobot. There's never been selection to be a good anthrobot. We have to understand where do these things come from.

谁能想到你们的气管上皮细胞——那些默默处理花粉等物质的细胞——竟能形成可自主移动的小生物，还能四处修复神经损伤。这只是这些生物潜能的冰山一角。

And who would have thought that your tracheal epithelial cells, which sit there quietly dealing with pollen and who knows what else, are able to make a self multi little creature that can go around and heal things like neural wounds. This is just the tip of the iceberg for these things.

比起青蛙混合体，我谦逊地建议下次用大象作为塔夫茨大学的吉祥物。我来自纽约塞尔默附近，那里是巴纳姆贝利马戏团的发源地，也是塔夫茨大学部分历史的起点。Michael Levin，这次演讲太精彩了，我两年前就预料到了。

Well instead of frog mix, humbly suggest the elephant next time as the mascot of Tufts. I'm from near Selmers, New York which if you know is right where Barnum and Bailey got their start and where Tufts got part of its start. So Michael Levin, this has been phenomenal. Knew it would be. It's two years in the making.

很高兴你花了这么多时间。你真是太慷慨了。

I'm glad you took so much of your time. You're so generous.

谢谢

Thank you

非常感谢。希望我们还能再合作。正如我所说，我们还有很多问题，我正在数着

so much. And I hope that we'll do it again. As I said, we have questions enough for, I'm counting up

这里，

here,

从第一部分到第四部分。也许下次可以当面交流，那就太好了。非常感谢你，迈克尔。

parts up to part four. So maybe in person even, that would be great. Thank you so much, Michael.

祝你今天愉快。很高兴认识你。

Have a wonderful day. Very good to meet you.

好的。太棒了。谢谢

Okay. Great. Thank you

所以

The so

迈克尔·莱文的研究意义远不止于生物学领域，它触及物理学、意识以及我们在生物宇宙中地位的根基。如果认知确实贯穿所有层面，这对人类理解智能意味着什么？对宇宙中其他人工与外星智能又意味着什么？如果你喜欢这场关于生命与意识电学基础的对话，我相信你也会喜欢我与罗杰·彭罗斯爵士和斯图尔特·哈默罗夫的访谈节目，我们在其中探讨了大脑中的量子力学，以及意识是否可能源于微管中的量子过程。两位杰出思想家，关于意识与生命本质的两种革命性观点。

implication of Michael Levin's work extends far beyond biology, touches upon the very foundations of physics, consciousness, and our place in the biological cosmos. If cognition really does go all the way down, what does that mean for human understanding of intelligence? And what does that mean for other artificial and alien intelligences throughout the universe? I know if you enjoyed this conversation about the electric basis of life and consciousness, I know you'll enjoy my episode with Sir Roger Penrose and Stuart Hameroff, where we explore quantum mechanics in the brain and whether consciousness might emerge from quantum processes in microtubules. Two brilliant minds, two revolutionary ideas about the nature of consciousness and life itself.

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