Introduction: Computational Boundary of a Self

• The paper attempts to formalize thinking about any possible cognitive system or intelligence. Intelligence is defined (per William James) as competency to reach the same goal by different means (cybernetic definition). It’s about navigating a problem space to achieve a goal despite obstacles.
• Origin and composition are considered less important than shared properties of cognitive systems. The origin story is not reliable and there’s not anything inherently special in any given cognitive system and may depend more on it’s implementation (i.e. hardware in robots, biocomputer in life).

Cognitive Light Cones

• Concept borrowed (and inverted) from physics’ spacetime diagrams (Minkowski). Time is vertical, spatial dimensions are horizontal.
• The cognitive light cone represents the spatio-temporal size of the *biggest* goal a system can pursue. This is NOT about sensory reach, but about the scale of the goal state.
• Examples given progress from simple organisms to complex beings, demonstrating growth in spatio-temporal considerations:
  • Tick/Bacterium: Small light cone; focused on local chemical concentrations.
  • Dog: Larger light cone (memory, prediction); goals like keeping intruders out of its territory. Limited ability to care about events far away in space/time.
  • Human: Very large light cone; goals can include abstract, long-term, even unattainable things (world peace, preventing the sun’s burnout). Limitations still exist in caring capacity.
  • Humans’ large light cones include the ability and understanding to work on unachievable goals and the stress that it results in is likely to be something of great consideration in humans (perhaps) as a species and may explain part of it’s drives, in this way may not be completely unlike other creatures with large cognitive lights cone and goals that do extend to some future event that they will still likely witness in their time.
  • One key takeaway on Human cognitive light cone: The ability, understading of capability, and undertaking/investment in nonachievable goals that might explain motivations.
• The ability and perhaps motivation and direction in species. The capcity to care (to help address another beings suffering for exmaple) on other beings is not able to grow at a 1:1 and is also very important as perhaps with Bodhisattvas.
• Related to the TAME (Technological Approach to Mind Everywhere) framework, which aims to move questions of cognition/sentience/intelligence from philosophy to testable scientific claims. Every statement should create test-questions and predictions for the systems capability.
• Intelligence is not binary; it’s a spectrum. Claims about a system’s cognitive level should define the problem space, the goal, the system’s capabilities, and then be testable, creating new data for science, than a thought exercise alone.

Collective Intelligence

• Compound intelligence consists of a heiarchy: All intelligence is collective. Organisms are made of cells, cells of components, etc., in a nested hierarchy. This is to be contrasted with something like, Integrated information, the former makes specific assumptions.
• Organisms’ large-scale light cones do not belong to individual components (cells, tissues). Cells and their individual parts are geared and set towards and interact with and respond/exist in physical/physiological world; this is the fundamental mechanism/method of higher organization (or it appears). The higher system must emerge through these cells’ actions.
• A “scale-up” process exists, in which subunits (cells) with individual competencies combine to create a larger cognitive entity.
• Levin points that de Cartes view was inaccurate (specifically about pineal gland, though he admires many other traits of de Cartes’). The mistake, fundamentally is that of composition and of a misunderstanding of the scaling capabilities from the underlying components that drive the overall organization. This may also explain certain cancer growth as cancer as being able to operate beyond it’s boundaries when certain properties change (bioelectrical properties).
• Robots don’t get cancer because their parts are *passive*; they don’t have individual agendas, as biological cells do. This risk from the properties that enables higher organizational systems, also brings its own risk and down-side: cancers for example, by making an entity, using smaller and active materials.
• Key question: How do the properties and *novel goals* of the compound intelligence relate to the properties of the parts? How does this scaling up happen, and how can it fail (e.g., cancer)?
• I.i.t. The main disagreement, it seemms, is with a lack of separation between intelligent and non-intteligent behaviour, between intelligent-cognitive tasks vs tasks around consciusness, it has more of a philiophical bent than perhaps having any other reason for the fundamental misunderstanding and fundamental disagreement.

Scale-Free Cognition

• Scale-free, based from a previous publication, the term “scale-free cognition” is used to indicate that principles of cognition apply across different scales. Humans recognize intelligence best at their own scale (medium-sized, medium-speed, 3D).
• We struggle to recognize intelligence at very different scales (e.g., bacteria in the gut solving physiological problems, organs maintaining their own structure).
• Evolution itself could be seen as a large, distributed agent, with each animal being a “hypothesis” about the world, subject to success/failure. We must detach from the need of any type of ‘mind’ being limited to that of roughly Human-scale; stretched out is difficult for the mind, it must remain relative and flexible.
• Name: perhaps is slightly off because the term may imply something about reliance, and there exist different degrees of scale, yet not having scale may only emphasize differences in properties between that in smaller/greater scales and may give undue importance to scaling when, maybe instead, it ought to be on the system’s cognitive behaviour.
• Freedom should, in Levin’s description mean freedom in system doing whatever the systems is likely/intended to do with freeeom of scientists having freedom in approaching that in all types of views, including those previously impossible or hard to see/visualize/analyze.

Surprise and Infotaxis

• Agents strive to minimize “surprisal,” which relates to stress. Stress and surprise are equivalent in this context. This is related to concepts like the Free Energy Principle (Friston).
• Regenerative development/regeneration can be viewed as cells navigating “morphospace” (the space of possible anatomical configurations) and minimizing errors, that could represent itself through stresses, to reach the target morphology (e.g., a salamander limb).
• Intelligence in biological systems is not simply complex emergence, but the ability to reach a goal despite perturbations and novelty; It’s active error/stress mitigation and purposeful decision making for optimization/reducing future risks/hazards and generally problem solving. It MUST be experimented upon to verify intelligence, through new behaviours as data.
• Salamander limb regeneration: Cells regenerate the correct limb, and then *stop* when it’s complete. This is framed not as feed-forward emergence from local rules, but as homeostatic error minimization by a collective intelligence with a “set point” (target morphology).
  • This is not dissimilar from thermoregulation.
• Development regulation (cancer suppresion, metamorphisis, etc. have common trait and act not disimilarly with systems to keep errors low.
• Homeostatic ability/behavior: loop in system, has target(set points), works towards goals and away from any potential risks (from environment/genetic) and the like and tries, as efficiently as it can to get as close as possible to said set-points, taking whatever the system can within it’s realm of capabilites.
  • These abilities might be as robust as finding NEW ways to find optimizations/reaching set-points in the given situations to try and overcome obstactles.
• Key point: This requires a *set point* (like a thermostat). Cells expend metabolic energy to achieve large-scale goals (organ shape), going beyond their individual needs. This can occur if the environment changes to accomodate this energy expenditure (heat) by system.
  • Stress is the term used here for that ‘keep-trying-to-minimze-the-error-state-which-may-create-the-expenditure-of-extra-energy’
  • Stress has commonalities across domains: Engineers on meso materials, psychologists on general ‘well-being, behavior’, evolutionary psychologists of well-being/condition of eco systems/societies and others in different areas that are hard-er to recognize, but which still share similiar properties such as in micro-environments for cells and systems and their interactions (stresses here not that dissimilar to stresses on systems.
  • There are specific, large requirements from collective systems that are working, in aggregate, from simple components such as skin cells, who otherwise may, normally do simpler processes, these ‘goal differences’ result and could manifest into unique and perhaps useful signals and/or molecules.
  • Geometric Stress example: and eye grown artificially, elsewhere may not, and can never stress specific cellular cells individually but are the system (collection of tissues/components, and whole system working) stressed. For instance: the tissue’s overall configuration can impact on overall body. Another example is if a new eye forms in tad-pole tails.
• The spreading of this stress, is key: by letting the underlying system understand where the greatest difference lies, it can find new way/mitigation-patterns/methods to reduce total ‘errors’, not that different to a cooling-magnetic having all underlying componets, in this scenario and configuration find alignment: free/potential energy.
  • Temperature might have a bearing too, which may change with more stress/movement of different, smaller agents with new degrees of flexibility/freedome for underlying particles; creating conditions more likely and more conductive to more likely, new behaviors and changes (and perhaps errors in the right situations)
  • Stess might make the parts work to benefit overall whole system and may explain away certain questions with regards altruism.

Gap Junctions and Scaling of Collective Intelligence

• Gap Junctions: Proteins that form “submarine hatch”-like pores between cells, allowing small molecules, current, and signals to pass directly.
• “Magic” of Gap Junctions (according to Levin): They facilitate the *scaling* of collective intelligence.
  • Traditional Signaling: Secreted molecules. The receiving cell knows the information originated *outside*. It can be ignored, treated with caution, or learned from. The receiving cell has more control.
  • Gap Junction Signaling: Signals pass directly into the receiving cell’s internal environment, *without metadata* indicating origin. There is *no* indication that it’s not internal.
  • The absence of signals of signal orign and type fundamentally means an uncertainty in behavior and decision making from higher up, at individual/collective/system.
  • Result: A “mind meld.” The receiving cell cannot distinguish the signal from its own internal signals. It erases individual cell identity and promotes collective computation and decision-making.
  • Benefit for decision making with collective systems by preventing (removing/mitigating risks) in conflict/misaligned/different interests from underlying structures that may otherwise benefit from ‘selfish’ decision and actions to the detriment of higher level structure (the body, here in this discussion),
    • It creates a larger computational space, and better spatial-integration: a single system, as oppose to independent, singular-focused, units.
    • Enables collective sensing of biomechanical forces (as a cell sheet). It permits, and results in a greater, aggregate organization, having greater reach, with collective/overall goal: making them harder for cells (to take example) in micro, or more localized events, harder to ignore.
    • It is also worth noting that while they appear as fundamental: this configuration can go wrong and, in so far as this has a negative effect, would mean care must be taken as the overall aggregate-organizational structure can/could be to the benefit and/or detriment of the component structures and there’s always a ‘give-and-take’, an inherent and possibly (maybe!) ever-present fundamental risk and the need/requirement of finding balance/middle path that benefits overall structure as well as the parts making up this new-collective system (in cells for example this could mean cellular death for body ‘success’; like skin cells during a hand scratch after going rock-climbing).

Cell Membranes and Information

• Gatenby’s claim: ~99% of Shannon information in a cell is in the membrane and transmembrane gradient.
• Interpretation: The cell membrane is the primary interface for cell-cell communication and interaction with the outside world. It contains receptors, ion channels, and biomechanical structures. All other cells, within a ‘larger’ organism are a ‘foreign agent’ or unknown to underlying-systems and could represent as a potential (or an actual, confirmed risk).
• Cells ‘hack’ other cells/outside world at membrane interface. There are all the interesting structures and systems that give important clues and information about what cell interactions exist, do, create.
• Shannon information is observer-relative; different observers can derive different amounts of information from the same signal. This highlights interpretation-dependant information (from signals or actions that do something), including observer dependant views, not that they can be.
• It has other interesting components and properties to consider as an additional, external agent (as ‘the human’ is external to underlying cells: cells here can then view, and consider humans in the general world: for that interaction-ability for systems)
• Expanding the horizon of light-cones lets shannon info to acquire a greater range (larger in scope: in breadth and height).

Meaning and the Observer

• Chris Field is credited here for an important, complex take.
• Central role of the *observer* in information theory (Shannon or otherwise). Meaning is not inherent in a signal; it’s *imputed* by an observer.
• Meaning is Observer-dependant: this concept highlights this to the N’th degree; a different view of this might lead to different conclusions and decisions/observations (and subsequent actions based from).
• Different observers can derive different meanings from the same data. Perspective matters (relates to Josh Bongard’s “polycomputing” idea).
• Multiple ways can have advantages in analyzing/looking at similar data that might then lead to insights impossible/unseen from previously impossible ways of looking at systems (perspective is useful for different angles, and with right ‘perspectives’: the tools for it).
• Polycomputation and perspective that might result in greater understanding by ‘squeezeing in extra power to the bio/computation in process’; that will lead to greater processing/computation ability.
• This emphasizes perspective shifts as useful, separate (or even independent) tool to get great insight that may give ideas to solutions/behaviours for a system (and vice versa) and has much application, perhaps more then ‘common’, to those beyond simple/narrow computation ability.

Cancer as a Breakdown of Cognitive Scaling

• Reframing Cancer: Not *why* we get cancer, but *why isn’t everything cancer*? Our cells’ “default” state is to reproduce and move where life is good (tumor-like behavior). The more ‘surprising’ question: is the reverse.
• The puzzle: what is being regulated to ‘control’ a structure; to have this overall-goal in a direction in aggregate: it appears to have a collective/overall higher control for aggregate, whole.
• Individual cells have small “selves” defined by their local environment.
• Electrical and biochemical networks establish the boundary between self and the outside world. A cell connected to many others has a *large* self (potentially the whole body).
• With the right signaling pathways: cells can grow/change/be-directed: and the mechanisms for higher-level-systems might (could!) and may depend on this.
• An oncogene that disrupts gap junctional communication *shrinks* the cell’s self to a single cell. The cell then pursues individual-level goals (metastasis, proliferation), which deviate from the collective.
• It highlights breakdown (failures) as an almost-expected outcome, in these otherwise powerful systems, but the very mechanism by which that makes it useful also inherently create the very possibility/mechanisms/outcomes of misaligned or poorly-aligned states that might cause this stress/issues that could go unheeded/unattended too (because these systems are complex, intricate).
• Gap Junctions is key, to preventing the cells (example/use) to ‘regain’ that original identity with it’s associated individual decision/behavior.
• Gap junctions make all other external forces a common issue: meaning that they do something, if that new issue is in direct-conflict/alignment with those the other entities, will create issues that otherwise can’t be addressed and in that way could have issues, is a source of great importance and of stress to ‘keep’ that integrity (for optimal system operation).
• Signals such as electrical signals might also be key, here (which could have its own interesting implications that, while currently unexplored/explained in this interview, nonetheless represent fundamental understanding-path that is of crucial importance that may go-unaddressed here)
• Ennviromental interactions/communications might become ‘external’/not-important signals/input to smaller system once higher ‘connections’ in/of these new networks are broken and new behavior becomes dominant, as consequence.
• Self: is different, yet of equal value to it’s immediate self vs that higher organization system that does otherwise act together for the overall-higher-system in that sense then; could still be seen as ‘selfish-behavior’ even if/when operating to improve aggregate structures such as entire ‘being/system’: meaning that any decision is made that favors these goals are inherently ‘selfish’ actions, on the level they occur/happen/exist; the scope makes difference.
• Cancer cells are no *more* selfish than normal cells; they just have a tiny self and thus pursue smaller goals. Cancer’s inherent behavior is a natural outcome of such situations and environments that have similar ‘breaks’ of properties/structures of ‘connection’ and thus the very thing (aggregate structures of importance such as for overall body or larger ‘higher systems’): would mean that it is normal, not an irregular occurrence/outcome given such system failures:
• It opens, at least theoretically: a ‘novel’-solution space, in how such cells/tissues may be mitigated: re-integration of structures through electrical signalling, could lead to an intereative (and useful!) mechanism to control/guide new and more stable cells (to make higher organization).

Therapeutic Implications of the Bioelectric View

• Cells can be cancerous without there having been genomic damages to begin with: cancers, given new structures/connections of communications might also be grown in artificial bodies: it does not require inherent faults to grow.
• By creating artificial electrical fields in-vitro or ‘else’: one might even use structures like embryos as new bio-computer/electrical signaling based pathways in computation that do not exist, not do require genome structures but would have use: by connecting together such components to make novel and new biological systems that otherwise wouldn’t and would not need to exist with it’s specific properties.
• This leads to thinking: a therapeutic strategy, which involves reconnecting cancerous cells to the bioelectric network, causing them to normalize even with oncogenes. They have found it effective in their current bioeletrical/xenobot and tadpole studies to mitigate/treat cancer by ‘redirecting’ cell behaviour; instead of the ‘old, default’ ways/methods that might also do harm.
• *Don’t* kill the cells, but instead reconnect, change goals, and set priorities (set/goal posts). The molecular hard-ware may be not useful/of focus, but to reprogram (to do this would result in less stress-damage-related to kill-first). Focus then is bioelectrical signaling pathway, here instead of molecular (kill-first) methodology and/or hardware related solutions/methods (such as what has historically been the way such situations may get addressed that results in non-benign outcomes and failures and could/would do better, than perhaps older systems) to give it new configurations to do more effective, stable/integrated system control, which is very difficult (impossible maybe? and would, or might not address fundamental reasons and mechanisms).
  • ‘Telling cell’: is not easy, hard to direct behavior (to ‘talk’): this does suggest a fundamental communication-challenge that does exist, or appear to exist with cells and cell tissues.
• Shift from low-level manipulation (genomic editing, rewiring pathways) to *high-level* communication, “retraining” the system to use its own competencies. Not that there are many effective systems to even begin to direct the signals (perhaps), let alone systems, at an electrical levels, but: that this may change/has potential in future and, more so in general systems.
• Analogy: Don’t rewire the computer hardware every time you want to switch programs. Use the existing software interface and “built-in” control systems. Use higher level/better (hopefully more robust/safe/stable etc.) levels, which may improve chances for overall goals of larger, more robust organization systems, too and in so doing address larger organization failures, through better information/control mechanisms (signals in particular, or whatever data/structures do create differences in cell systems)

Drugs, Words, and the Multiscale System

• “Drugs and words have the same mechanism of action” (Benedetti): High-level cognitive expectations (placebo effect, context) can filter down to molecular biology.
• Our “Executive decisions”/behavior can directly translate down-the-chain for action by parts/component that have the very capability to be active/reconfigure themselves: the “executive decisions”, for exmaple to move out-of-bed; require action from ‘lower system’: down-chain commands for decisions/movements to effect on lower scale for it to ‘result’ on larger levels: high decision > cell component action > outcome in large-body (like humans).
• Explains things like hypnodermatology, placebo effects, and even everyday voluntary actions. High-level cognitive states can influence molecular physiology.
• Important to emphasize how significant the very notion of this “scale”-interaction” from very small scales of individual ‘things’ making the whole; which can and is seen as very much less understood/important/of value.
• This is how our bodies behave; how it appears the way it appears, every second; and when, and if/when working ‘well’, gives stability/predictable configuration/structure: and is a key factor. It can, given all this also very much explain that perhaps seemingly small interactions can and/or will affect aggregate/whole in what could be surprising way with new-scales.

Components of the Morphogenetic Field

• A cell gets influenced at multiple levels: There exist ‘other’ considerations, not discussed in talk: but some factors.
  • The cell takes a ‘vote’ from the field, based on many signals:
• Biochemical signals, Biomechanical forces (tension, pressure, extracellular matrix (ECM)), and Bioelectrical signals.
• Bioelectricity isn’t the *only* factor, but it’s crucial because it acts as the “excitable medium” and *computational layer* that stores the cognitive information of the collective intelligence, and this *computational layer* may have implications that go (well?) beyond/much/more into the system(s) than all the previous properties discussed, combined and could create important shifts in new-areas that previously were harder to achieve.
• Brain learned electrical signaling trick from pre-neural evolution. The exact same principle used.

Harold Saxton Burr’s “Privileged Information”

• Harold Saxton Burr (1930s-40s): Measured electrical fields in various biological systems (cells, embryos, trees, humans). He was one of first good voltage measurers of this sort, even during time/periods without precise tools; without means to manipulate that environment/electrial systems directly to any reasonable measure and to measure accurately that and without direct insight and/or ability/way to impact specific structures/mechanism that resulted (and continues to give these, that may come out of his discoveries).
• Burr described the *future* development of bioelectricity, and (according to Levin) he was remarkably accurate despite having only crude measurement tools. He accurately understood some mechanisms that could exist for structures in organisms/collective biological systems that make some cells grow or do new configurations without explicit-directions but yet that which are highly, robust, complex and useful (he perhaps found hints into a form of biological bio-computation in/through cells, from a voltage/energy and interaction from other electrical mechanisms), an electrical layer; a “computational bioelectricity”: where cells work individually as well in aggregate for overall body-configuration through unique/undirected behaviours that come to create overall systems. He lacked more understanding from his tools/in-vivo tools: but the voltage level could indicate certain actions that go beyond ‘simple structures’ by enabling cells in ‘micro environments’, not that much unlike how an isolated/non-interactive or perhaps even non-important micro-electrical activity from micro, seemingly (to ‘higher up, outside’/non-connected agent(s)/viewer), but that may ‘non-specifically direct’ underlying and connected nodes through simple communication, with enough of it, and large scope: give the impression, action of directed.
• Burr’s work was influential for Levin, because it may highlight and emphasize the need for high precision to not overlook some specific important signals/behavior of systems, and specifically a type that can (easily? maybe even accidentally) be ‘masked-away’; through less percise tools; a reminder.
• A call that ‘Burr made these same discoveries’, without the need for tools (not ‘advanced’; from perspective-perspective perhaps, though he created what was probably the only, that ‘one’ to understand in-vivo systems).
• Other Pioneers, who built further on discoveries were also mentioned, though perhaps that of which gave direction was made in his earlier/later work, that have greater bearing/impact of what future potential directions/impact it may still have to come (such as those areas/disciplines mentioned that may make new implications as technology/science does its future works; perhaps that can enable even ‘higher resolution’ systems, that otherwise do give very specific data and/or can ‘target’/influence it too, specifically too for what outcome it may create that otherwise can’t and does give potential to solve, currently unresolved systems in biology.

Zen Buddhism and Compassion

• Final paragraph of “Scale-Free Cognition” paper mentions Zen Buddhism.
• Connection between cognitive light cones and the Buddhist concept of enlarging compassion (actively working towards the welfare of all beings).
• Thinking about diverse intelligences forces us to revise simplistic distinctions about who/what deserves compassion. Ethics needs updating in light of new possibilities (hybrids, cyborgs, etc.).
• The *goal* of enlarging our light cones (through technology or other means) is not just technology itself, but to *increase our capacity for compassion*.
• The talk referenced to Buddhism is from a different perspective and does (likely/potentially) expand-on previously not ’emphasized’ traits/understanding for how that “Compassion”-concept may relate and does explain: compassion has a key trait that must also improve alongside intelligence, even if they have, in some way ‘similar mechanism(s)’; for there to exist that fundamental property of ‘compassion’; that perhaps drives all intelligence and has not that dissimilar-configurations in structure and/or direction(goals/target)/understanding. Compassion is key: and intelligence and compassion must work together and go alongside one-another and it appears perhaps not only and ‘simply’ for it to be morally/ethically/’fair’: as without ‘Compassion-with-it’; a “light cone”/an intellgent behavior-like may get misdirected/misguided, given that intelligence, if not guided-by-compassion is perhaps missing a component (as fundamental and core property of it): for ‘correct’/’intended’-way: without care, and love, could lead the intelligence(in this scenario/example being discussed) to get to a state with undesired configuration (by failing to have core direction/components with intelligence not having inherent traits/direction of system(s) with-compassion: might be key) for larger goal to have a very unique outcome (compassion must accompany intelligent-systems for stable-goal); for its goals, without misalignments, it may never address higher structure considerations, it is not limited to compassion towards (smaller/sub-entities); instead there is perhaps an alignment required between scales.
• “Compassion”: this isn’t that general care: of just thought-excercises; compassion being: an active (key: actions; something active and real-work, as opose to just mental-considerations), is fundamental.
• There are many complex components here, and a larger paper can give details that make the discussion(talk-itself) do, if in the wrong/very limited scope may have very wrong implications.

Links

• www dot drl-dot org – mentioned to contain information about the presenter, and other information mentioned through-out interview.
  • Mentioned that the presenter/speaker is huge-fan of some of AI Art works such as (Midjourney): and has blog posts/articles (that can be interesting: is not directly connected to this interview, though worth, asides, nonetheless, a look from that view if desired/in/with similar interets)
  • Note of the site’s complexity, breadth of areas covered and inter-links, this may make navigating this complex.
• Biology, Buddhism and AI: care as a driver of intelligece: paper and reference to Buddhist concept mentioned in last/wrap-up, section and could benefit with having that as part of consideration/discussion with more references/people on a/that discussion (there appear to exist greater context and perhaps some of those not immediately relevant, unless with a lot of additional context) to make some complex issues; not very simply covered here and/or that lack (critical-thinking perhaps!), key here: which it would be with some fundamental-aspects about these systems and considerations that does take complex understanding of what compassion is: “An Active”; vs (that which otherwise appears as: “that it means feelings”), that does guide that behaviour, action.

简介：自我计算边界

• 本文尝试对任何可能的认知系统或智能进行形式化思考。根据威廉·詹姆斯（William James）对智能的定义（或控制论定义），智能指能以不同方式达到同一目标的能力。这关乎在面对障碍时，如何在问题空间中实现目标。
• 系统的起源与组成被认为不如它们所表现出的认知属性来得重要。起源并不可靠，也没有哪个认知系统从本质上“特殊”，更多取决于其实现方式（例如机器人硬件或生物计算机）。

认知光锥

• 借用并反转了物理学中时空图（闵可夫斯基图）里的概念。时间方向在垂直轴，空间维度在水平轴。
• 认知光锥代表系统所能追求的最大时空尺度目标。这与感官范围无关，核心在于目标状态在时空上的规模。
• 示例按生物体复杂度递进，展现其时空考量的增长：
  • 蜱虫/细菌：小光锥，关注于局部化学浓度。
  • 狗：较大的光锥（有记忆和预测能力）；例如阻止入侵者进入其领地。但对较遥远的时空事件关心有限。
  • 人类：非常大的光锥；目标可以涉及抽象、长期甚至无法实现的事物（世界和平、防止太阳衰竭）。但在“关心能力”上仍有限。
  • 人类的大光锥包括有能力且理解如何致力于那些无法达成的目标，也因此带来压力。这也许是人类驱动力的一部分，类似其他具有大认知光锥并追求超出当下的目标的生物。
  • 关于人类认知光锥的要点：拥有意识到自身能力的能力，并愿意付出或投入于无法完成的目标，这或许能解释部分动机。
• 这里也涉及物种的动机与指向。一个物种对他者（例如他者的痛苦）的“关心能力”并不会与认知光锥规模简单地 1:1 同步增长。在某些传统中（如菩萨思想），这点尤其重要。
• 这一切也与 TAME（“Technological Approach to Mind Everywhere”）框架相关，旨在把对认知/感知/智能的讨论从哲学层面转为可检验的科学命题。每个判断都需产生可测试的假设和预测，明确系统所能解决的问题空间和目标。
• 智能并非二元概念，而是光谱。对系统“认知水平”的陈述要明确问题空间、目标与系统能力，并能通过新数据检验，而不只是思辨。

集体智能

• 复合智能由层级结构组成：所有智能都是“集体的”。有机体由细胞组成，细胞由更微观的成分组成，层层嵌套。这与“集成信息”等观点不同，它有其独特的假设。
• 有机体的大尺度光锥并不属于某个单一组成部分（细胞、组织）。细胞及其组分在物理/生理世界中运作；更高层的系统仰仗于它们的活动而涌现。
• 存在一种“向上扩展”的过程：各具能力的子单元（细胞）组合，形成更大规模的认知实体。
• Levin 指出笛卡尔的观点不准确（尤其是对松果体的看法，尽管他敬佩笛卡尔的很多其他思想）。其根本误解在于对构成关系以及底层结构扩展到整体组织能力的认识不足。这也可能解释癌症的产生——当生物电属性等特征发生变化时，癌细胞可在超越原本的“边界”下进行活动。
• 机器人不会得癌症，因为它们的部件是被动的；并不存在生物细胞那样的各自“诉求”。正是这种可带来更高组织水平的特性，也暗含了负面后果：如癌症。
• 核心问题：复合智能的新属性和“新目标”与其组成部分的特性之间的关系是什么？这种向上扩展是如何形成，又如何会失败（例如在癌症中）？
• 关于一体化信息理论（IIT）的分歧主要在对智能与非智能行为的区分，以及对“智能认知任务”与“意识”任务的理解存在更多哲学层面的冲突，而非纯科学争议。

无尺度认知（Scale-Free Cognition）

• “无尺度认知”在先前文献中被提出，指认知原理可适用于不同尺度。人类最能识别与自身相似尺度（中等大小、中等速度、三维空间）的智能。
• 人们往往难以识别在相差甚大的尺度上（如肠道细菌在生理问题上的解决方案、器官自行维持结构）的智能。
• 进化本身可被视为一个分布式的“大型智能体”，每个物种皆是对世界的“假设”，并要接受成败检验。要摆脱对“思维”仅限于人类尺度的执念；跨越大范围或极小范围会很困难，但须保持灵活视角。
• 命名上，“无尺度”或许容易让人误以为完全不考虑尺度差异，实际上只是强调在不同尺度上依然存在相似的认知原则，并不意味各尺度没有差别。
• Levin 所言的“自由”更多指科学家可自由从各类难以观察或分析的角度，去研究这些不同视角下的系统，打破以往的局限。

惊奇与信息觅食（Infotaxis）

• 智能体倾向于最小化“惊奇”（或压力）。在此，“压力”与“惊奇”相当。它与 Friston 提出的“自由能原理”类似。
• 再生发育/再生过程可被视为细胞在“形态空间”（所有解剖形态可能性）中导航、不断减少错误或应激，最终抵达目标形态（如蝾螈肢体）。
• 生物系统中的智能并非只是复杂涌现，而是能在干扰与新状况下持续达成目标；是一种主动的错误/压力调控与有目的的决策能力。要检验智能，必须通过新行为实验产生数据。
• 蝾螈肢体再生：细胞能再生正确的肢体，然后在完成后“停止”。这被视为一个集合智能通过某种“设定点”（目标形态）来进行的稳态（homeostatic）纠偏过程，而非仅由局部规则前馈产生。
• 这与体温调节等稳态机制相似。
• 再生调控（抑制癌症、变形、等）都有共同特征：通过系统性的机制维持错误最小化。
• 所谓稳态行为：系统存在一个目标（设定点），为此付出能量去纠正偏差、规避风险，以尽力达到目标。若环境变化，也会努力去调节并靠近该目标。
• 这个过程或许非常灵活，能在面对障碍时找到新方法和新路径，以最小化系统错误或压力。
• 关键前提是：必须存在一个“设定点”，如恒温器一般。细胞为实现大规模目标（如器官形状）而消耗代谢能量，这超越了单个细胞自身的小需求。环境的相应配合或许也会在此过程中起作用。
• “压力”在此是指系统不断尝试将偏离目标的“误差状态”降至最低所引发的能量或代谢代价。
• 在工程学、心理学、进化学等领域，“压力”概念都有类似之处，指不同行业/领域对“偏差或损耗”的努力调控。
• 集体系统所需的大规模纠偏中，个体（如皮肤细胞）若平时只做小范围工作，在整体目标下则可能发挥新的或更大的作用。这也会带来整体层面的“几何压力”，如“在身体其他部位生成一个新的器官”。
• 这种压力在整个系统中扩散，有助于各组成部分识别最大的差距所在，从而尝试新的方法来降低整体“误差”。
• 某种程度上类似于磁体冷却时，微观元件寻找能量最低态：在生物层面，各子单元（细胞）或许也会在更大范围内协调对齐。
• 压力可令局部为了整体利益而行动，这或许也能解释看似“利他”的现象。

缝隙连接（Gap Junction）与集体智能的扩展

• 缝隙连接（Gap Junctions）：一种蛋白结构，能在相邻细胞之间形成类似“潜水艇舱口”的通道，允许小分子、电流、信号直接通过。
• 在 Levin 看来，Gap Junctions 的“神奇”之处在于它能促进集体智能的扩展：
• 传统信号：分泌因子。接受信号的细胞清楚该信息来自“外部”，可选择忽略或谨慎对待。接受细胞拥有更多主动权。
• 缝隙连接信号：信号直接进入接受细胞的内部环境，**没有元信息**表明其来源。接受细胞无法判断该信号来自自身或外部。
• 结果即类似“思维融合”：接受细胞无法区分此信号和自身内部信号，从而淡化单个细胞的个体身份，促进集体化的信息处理与决策。
• 这种机制可避免“利益冲突”，在更大层面进行计算与空间整合，让整个组织作为一个系统，而非一个个各自为政的单元。
• 这使得细胞层面能集体感知生物力学力（如细胞层张力），形成更广阔的目标整体。某些细胞的“自私”决策难以单独施展。
• 但也要注意，这种机制若出问题（如缝隙连接中断），可能导致组织协同失灵，出现癌症或其他紊乱。

细胞膜与信息

• Gatenby 的论点：细胞中约 99% 的香农信息在细胞膜及跨膜梯度中。
• 解读：细胞膜是细胞与外部环境及其他细胞沟通的主要界面。它拥有受体、离子通道和生物力学结构。对于单个细胞而言，任何其他细胞都是“外部”或潜在风险源。
• 细胞在膜界面“破解”其他细胞/外界世界，其中充满了关键信号与结构。
• 香农信息在本质上依赖观察者；不同观察者可从同一信号中提取不同信息。信号的意义受制于解释者。
• 对更大光锥的扩展也意味着香农信息层面的延展，覆盖更大范围的目标或环境。

意义与观察者

• Chris Field 在此有重要观点。
• 信息论（香农或其他）中，“观察者”角色至关重要。信号自身并不包含固有意义；意义由观察者赋予。
• 意义受观察者的视角影响：不同观察者从同一数据可得出不同结论，体现了 Josh Bongard 的“多重计算”理念（polycomputing）。
• 不同视角可从同样的信息中挖掘出不同的洞察；在对复杂系统（包括生物系统）的研究中，这种多重视角分析很有价值。
• “多重计算”与多重视角相结合，或能提供额外的计算/理解能力。

癌症：认知扩展的失调

• 癌症的新视角：与其问“为什么会得癌症”，不如问“为什么我们不会一直处在癌症状态？” 因为细胞的“默认”行为往往就是增殖、在环境适宜处定居（类似肿瘤）。真正值得惊讶的是健康结构为何如此常见。
• 因此谜题在于：是什么在调控“整体结构”的方向，使细胞能以整体目标行动？
• 单个细胞只在局部范围内定义其“小我”，由其周边环境决定。
• 电信号和生化网络共同划定了自我与外部世界的边界。若细胞与大量其他细胞紧密相连，它的“自我”边界就能扩大到整个身体。
• 当某些基因（如癌基因）破坏缝隙连接通信，细胞的“自我”范围缩小到它自身；它转而追求单细胞层面的目标（转移、无限增殖），与集体目标背离。
• 这提示了一个期待已久的结论：集体大系统越复杂，它失效时越可能出现这种“个体化”回退。
• 缝隙连接有助于阻止细胞回归“原始”的自利模式。
• 失去电耦合后，环境对该细胞而言变得可被忽略，它只顾自身需求并扩散繁殖。
• 癌细胞并不比正常细胞“更自私”，它们只是缩小了自我范围。对它们而言，只剩下局部目标。而“让所有细胞维持整体健康”的大目标失联了。
• 理论上，这为癌症的新疗法提供灵感：通过电信号等方式使癌细胞重新接入集体网络，让它们“回归”大我。

从生物电角度看治疗

• 细胞可在不存在基因损伤的情况下表现出癌症特征；这意味着在人工环境中，只要通信和信号被扰乱，也可产生肿瘤样行为。
• 若在体外施加人工电场，或可利用生物电与胚胎等结构来构建新型生物计算/信号系统，不必依赖基因组层面的改造。
• 这也指向一种潜在的治疗策略：与其杀死癌细胞，不如通过修复它们与集体的电耦合使其“回归正常”。即便有致癌基因，细胞也可被“重新定向”，这在 Levin 的相关实验中已见一定成效。
• 不再只关注低层（基因编辑、分子通路），而是采用高层的“沟通”方式，让系统运用自身的复杂能力。避免强行从硬件层面去“拆线”，转而利用更高层次的“软件接口”去调度。
• 类比：不必每次想换程序都去拆装计算机的硬件，而是通过已有软件接口，使用系统自带的管理能力。

药物、语言与多尺度系统

• “药物和语言作用机制相同”（Benedetti 语）：指高层认知期望（安慰剂效应、环境情境）可向下影响分子生物学。
• 人的“执行决策”或行为可传递至下级模块，再通过部件执行具体动作：比如决定起床，需要肌肉收缩、细胞级别反应。
• 这可解释例如“催眠皮肤病学”（hypnodermatology）、安慰剂以及日常的自愿行为。高层心理状态可以影响分子层面。
• 强调多尺度互动的重要性：高层目标与小尺度单元之间保持协调，才能实现对身体的稳态管理。如果精细度或沟通途径不足，整体-局部的交互可能出现失灵。

形态发生场的组成要素

• 单个细胞所受影响是多重的：不仅有生物电，还有其他信号因素：
• 细胞从“场”获得“投票”，包括：生化信号、力学信号（张力、压力、细胞外基质）及生物电信号。
• 生物电并非唯一因素，但它至关重要，因为它是一种“可激励介质”（excitable medium）和“计算层”，存储着集体智能的认知信息；这层“计算”有潜能比其他因素更能在系统中触发大规模效应。
• 神经系统其实是对更早期的生物电信号加以专门化利用。原理是一致的。

Harold Saxton Burr 的“特权信息”

• Harold Saxton Burr（20 世纪 30-40 年代）曾测量过多种生物系统（细胞、胚胎、树木、人类）的电场。他是早期在此领域做电压测量的先锋。
• Burr 描述了生物电在未来发展的图景（Levin 认为他当时的观点在当时的技术条件下已相当先进）。
• 他当时仅能用粗糙仪器进行观测，却预见到生物体通过电势的方式进行大规模组织与调控，这与后来的“生物电计算”概念呼应。
• 对 Levin 而言，Burr 的研究既是启示，也提醒人们要有更高精度来发现和操纵生物电信号。
• Burr 之外的其他先驱也在这条道路上作出重要贡献。随着技术发展，能更精准地测量和调控电信号，或许在今后能更好地解决生物学中复杂的结构与自组织之谜。

禅宗与慈悲

• 在 “Scale-Free Cognition” 论文结尾处，作者提及禅宗。
• 将认知光锥与佛教中扩大慈悲心的概念联系起来，指向积极关怀所有众生。
• 思考多样化的智能体，势必要求我们修正对“谁/什么值得同情”的简单二分，也需要在混合体、生化机械融合等新兴事物面前更新伦理。
• 通过技术或其他手段“扩大光锥”的目标，绝非只为了技术本身，而是为了增强我们的慈悲能力。
• 关于禅宗的引用出现在最后，也许暗示当认知光锥变得更大，若缺乏慈悲指引，智能可能走向错误的方向；智能与慈悲应同时提升。
• “慈悲”并非仅是情绪或想法，更意味着行动。若没有这份“共情”或关爱，扩展后的大尺度智能可能背离更高层面的和谐。

相关链接

• www dot drl-dot org —— 演讲者的信息及访谈中提及的一些资料都在此站点。
• 提到演讲者对一些 AI 艺术（如 Midjourney）非常感兴趣，并在博客或文章中有所讨论。与本访谈主题并不直接相关，但对相关兴趣者或许有价值。
• 该站点内容广泛，链接众多，浏览时需一些耐心。
• “生物学、佛教与 AI：将关怀视为智能驱动力”的论文提及佛教概念，出现在访谈末尾。对于这一话题可以做更多延伸讨论（在更大背景或更多专家合作下），以进一步探究关于慈悲、智能以及复杂系统伦理的多重交叉。