学习内驱力的最优性

原文：Optimality of the learn drive

Goals in learning

学习中的目标

Education systems around the world attempt to define optimum curriculum for an average intelligent man on the planet. In that quest, adults use the entire experience of mankind to optimize the goals for a child. The problem with this approach is that all knowledge in a concept network grows layer by layer in a conceptualization process. The adaptation process resulting from the conceptualization is controlled by a set of human needs, the interaction with the environment (esp. the knowledge environment), and the goals. It is possible to explain goals to a child, and hope the adaptation will proceed in the direction of those goals. However, the goals themselves will obtain valuations that will reflect the directions taken by the conceptualization process itself. It is only possible to plant seeds of ideas. It is only possible to plant the seeds of goals. It is not possible to set an extrinsic goal for a brain and hope it arrives on its own by rational control. Without a goal rooted in the knowledge valuation network, it is impossible to employ the learn drive in a quest to achieve that goal.

世界各地的教育系统都试图为地球上的一个平均智力的人确定最佳课程。在这一探索中，成年人利用人类的全部经验来为孩子优化目标。这种方法的问题在于，概念网络^[1]中的所有知识都是在概念化的过程中层层成长的。概念化^[2]所产生的适应过程是由人的一系列需要、与环境（特别是知识环境）的相互作用以及目标所控制的。可以向孩子解释目标，并希望适应过程能朝着这些目标的方向进行。然而，目标本身将获得的价值将反映概念化^[2]过程本身所采取的方向。只有种下观念的种子才有可能。只有种下目标的种子才有可能。不可能为大脑设定一个外在的目标，并希望它通过理性的控制自行到达。如果没有一个植根于知识价值网络^[3]的目标，就不可能运用学习内驱力^[4]去追求实现这个目标。

In free learning, extrinsic goal setting for an intelligent developing brain is virtually impossible

在自由学习^[5]中，智能发展中的大脑外在目标的设定几乎是不可能的

Free exploration

自由探索

The conceptualization process is based on the phenomenon of emergence. It can only be controlled by the environment and the needs. We can plant books in a child's room, but we can't make a child read. All the controlling factors must not interfere with the central valuation system of the entire process: the learn drive. Interference with the learn drive will inevitably lead to a war of the networks and the suppression of the drive. As a result, the entire conceptualization process can be derailed. Interference with the needs, by means of rewards and penalties may distort the conceptualization too. If a child is left hungry, it will experience powerful incentives to direct the conceptualization towards the body's nutritional needs. However, this type of incentivization is not necessarily conducive for high level intellectual achievement. In an extreme case, we can raise a perfect spartan warrior by the age of 5. However, a brain busy with conceptualizing for the goals of warriorship is not likely to soar in the realms of abstract knowledge later in life.

概念化^[2]过程是基于涌现^[6]的现象。它只能受环境和需求的控制。我们可以在孩子的房间里放书，但不能让孩子读书。所有的控制因素都不能干扰整个过程的核心价值体系：学习内驱力^[4]。对学习内驱力的干扰，必然会导致网络的战争和内驱力的压制。如许一来，全部设想过程就会脱轨。对需求的干扰，通过奖惩的方式，也可能会使观念化发生扭曲。如果让孩子饿着肚子，就会体验到强大的激励机制，将观念化引向身体的营养需求。但是，这种激励方式不一定有利于高水平的智力成就。在极端的情况下，我们可以在5岁前培养出一个完美的斯巴达战士。但是，为了战士的目标而忙于构思的大脑，不可能在以后的抽象知识^[7]领域飞速发展。

If the basic needs are satisfied, access to knowledge becomes the chief driving force. Open access to the world wide web of knowledge is probably the least prejudiced tool of efficient and unbiased conceptualization. On the sidelines, the adults may plant enticing baits that trigger explorations. The baits might have a form of a microscope for a future scientist, or a baseball bat for a future sports star. With a bit of luck, the baits can nudge the trajectory, however, all explorations must be free to be optimal.

如果满足了基本需求，获取知识就成了主要的内驱力。开放性地获取世界的知识网络，可能是最不偏不倚的高效概念化^[2]工具。在旁人看来，大人们可能会埋下诱人的饵料，引发探索。诱饵的形式可能是未来科学家的显微镜，也可能是未来体育明星的棒球棒。运气好的话，诱饵可以推移轨迹，然而，所有的探索必须是自由的，才能达到最优。

Free exploration is necessary for efficient conceptualization. Its direction can occasionally be nudged with lucky incentivization

自由探索是高效概念化^[2]的必要条件。在幸运的激励下，它的方向偶尔可以被点拨一下

Harms of intervention

干预的危害

Each time the adult world attempts to define optimum educational targets, it fails to account for the fact that all educational trajectories are based on incremental optimization based on the valuation of individual pieces of knowledge. All lofty educational goals may easily be ruined by the fact that pre-designed trajectories include learning steps that are too steep or too flat. Each time suboptimal learning choices are made, the process is slowed down, and the learn drive is being conditioned out of the picture. In addition, coercive learning that keeps stumbling over steps that are too steep will inevitably lead to toxic memories and the hate of learning.

成人世界每次试图确定最佳教育目标时，都没有考虑到这样一个事实：所有的教育轨迹都是基于对单个知识的估价而进行的渐进优化。所有高大上的教育目标都可能因为预先设计的轨迹中包含了过陡或过平的学习步骤而轻易毁于一旦。每一次次优的学习选择，都会拖慢学习的进程，学习内驱力被条件性地排除在外。另外，一直在太陡的台阶上跌跌撞撞的强制学习^[8]，势必会让人产生有毒的记忆^[9]，对学习产生憎恶^[10]。

Coercive intervention in the learning process will inevitably suppress the learn drive, and undermine further explorations

在学习过程中进行强制干预，势必会压制学习内驱力^[4]，影响进一步的探索

Learning as hill climbing

学习就像攀登

The learn drive operates by the comparison of value gradients in the environment. By comparing the valuation of pieces of knowledge, and the learntropy of the sources of information, the brain will fashion an objective function to optimally choose the most efficient stream of high value knowledge from the environment. This optimization may lead to a local minimum. A degree of stochastic disruption is often needed in the algorithms used in mathematical optimization. In the same way, the learn drive may employ the random aspect of creativity. This makes the guidance of the learn drive non-deterministic. The cumulative effect of minor stochastic disruptions will be that the same learn drive, in the same environment, for two identical brains will produce vastly different outcomes.

学习内驱力^[4]通过比较环境中的价值梯度来运作。通过比较知识片段的价值，以及信息源的学习熵^[11]，大脑会形成一个目标函数，从环境中最有效地选择高价值的知识流。这种优化可能导致局部最小值。在数学优化中使用的算法中，往往需要一定程度的随机干扰。同样，学习内驱力^[4]也可能采用随机方面的创造力。这使得学习内驱力的引导是非确定性的。小的随机干扰的累积效应将是，同样的学习内驱力^[4]，在同样的环境中，对于两个相同的大脑将产生截然不同的结果。

In a population of children, each will find her own local optimum niche. This will lead to a harmonious balance in the distribution of skill assets in a population. The role of the adult world should be limited to convincingly sketching out lofty horizons, and to non-coercive assistance for those who get stuck in unattractive local minima. The learn drive satisfies the local or populational optimality criterion.

在一个儿童群体中，每个人都会找到自己局部的最佳定位。这将导致一个人群中技能资产的分配达到和谐的平衡。成人世界的作用应该限于令人信服地勾勒出高远的视野，对那些陷入不吸引人的局部最小值的人提供非强制性的帮助。学习内驱力^[4]满足局部或群体最优准则。

The guidance of the learn drive in exploration can be compared to hill-climbing in mathematical optimization

学习内驱力^[4]在探索中的引导可以比作数学优化中的爬坡运动。

Learn drive as optimum guidance

以学习内驱力为最优指导

In the process of building an efficient concept network, new pieces of knowledge must match prior knowledge (see: Jigsaw puzzle metaphor). This implies that it is impossible for the external agent to predict, which pieces would provide such a match. Interactive tutoring may be pretty effective in identifying such pieces, however, the entire process must be under the supervision of the learn drive. For example, a tutor may be pretty efficient at explaining the intricacies of a math problem, but the learn drive may call for a switch to another form or area of learning (e.g. due to domain-specific fatigue).

在构建高效概念网络^[1]的过程中，新的知识片段必须与先前的知识相匹配（见：拼图比喻^[12]）。这意味着外部代理不可能预测哪些片段会提供这样的匹配。交互式辅导在识别这种片段方面可能相当有效，然而，整个过程必须在学习内驱力^[4]的监督下进行。例如，一个辅导员可能会相当有效地解释一个数学问题的复杂性，但学习内驱力^[4]可能会要求切换到另一种学习形式或领域（例如由于特定领域的疲劳）。

The learn drive has the capacity to compare the value of two pieces of knowledge (using the knowledge valuation network). It also has a capacity to evaluate the value of the information stream (i.e. it's learntropy). With those capacities, the learn drive can be compared to a sense of smell that can efficiently detect information value in the environment.

学习内驱力^[4]有能力比较两个知识的价值（使用知识估值网络^[3]）。它还具有评估信息流价值的能力（即它的学习熵^[11]）。有了这些能力，学习内驱力可以比作嗅觉，可以有效地检测环境中的信息价值。

Due to its reliance on prior knowledge and unique knowledge valuations, the learn drive is irreplaceable. As such, it provides the optimum guidance in the learning process.

由于对先前知识的依赖和独特的知识估值^[3]，学习内驱力^[4]是不可替代的。因此，它在学习过程中提供了最佳的指导。

The guidance of the learn drive is optimum. The child is always right

学习内驱力^[4]的引导是最优的。孩子总是对的

It is easy to misunderstand the claim of the optimality of the learn drive. It is an efficient control system that supervises the selection of pieces of knowledge and the selection of information channels (on the basis of their learntropy). This does not imply that the reliance on the learn drive is hermetic. The learn drive needs to compete with other drives (e.g. the sex drive). In that sense, the learning choice may be suboptimum, esp. in conditions of reward deprivation that amplify selected drives (see: Reward deprivation).

学习内驱力的优化的说法很容易被误解。它是一个有效的控制系统，它监督知识片段的选择和信息渠道的选择（基于它们的学习熵^[11]）。这并不意味着对学习内驱力^[4]的依赖是封闭的。学习内驱力需要与其他内驱力（如性内驱力）竞争。在这个意义上，学习选择可能是次优的，特别是在奖赏剥夺的条件下，会放大选定的内驱力（见：奖赏剥夺）。

Moreover, the optimum choice of knowledge or information channel does not ensure optimum knowledge. In an extreme case, knowledge may be false and lead to the death of an individual. The optimality of the learn drive is limited to choices made in learning.

此外，知识或信息渠道的最优选择并不能保证知识的最优。在极端情况下，知识可能是错误的，并导致个体的死亡。学习内驱力的最优仅限于学习中的选择。

Optimality of the learn drive does not imply optimality of decisions, let alone behaviors, let alone the outcomes

学习内驱力的最优并不意味着决策的最优，更不意味着行为的最优，更不意味着结果的最优

Populational optimality

群体最优

The emergence of knowledge conceptualized under the guidance of the learn drive can be compared to the emergence of species in the evolution. Optimality criterion ensures locally optimum trajectory that can lead to a diverse and well-balanced ecosystem. As there is no optimum species, there is no optimum of human knowledge.

在学习内驱力^[4]的指导下，知识概念化^[2]的涌现^[6]可以比作进化中物种的涌现。最优标准保证了局部最优的轨迹，可以形成一个多样化的、平衡的生态系统。由于没有最优的物种，所以人类的知识也没有最优。

As Georgios Zonnios put it:

正如Georgios Zonnios所说：

Evolution naturally proceeds towards a state of high interdependence between the many different parts. For knowledge, this means individual learners will learn things that are relevant to their context. For society, this means that all angles of knowledge will be covered by different people in different ways. Where opportunities abound and resources are sufficient, change may occur very quickly. For example, a significant shortage of workers in a specific field will naturally cause individuals to work towards that field, possibly by increasing incentives to work there

进化自然而然地朝着许多不同部分之间的高度相互依存的状态发展。对于知识而言，这意味着个体学习者将学习与其背景相关的东西。对于社会而言，这意味着知识的各个角度将被不同的人以不同的方式覆盖。在机会多、资源充足的地方，变化可能会很快发生。例如，在某一特定领域严重缺乏工人，自然会使个人向该领域努力，可能会增加在该领域工作的激励措施。

Curriculum designers attempt to play God and design a perfect singular species that could optimally use the earth's resources. This is a futile effort that undermines the lofty goals of education. Analogously, the late specialization in college is tantamount to deferring speciation till the emergence of the Dinosauria.

课程设计者试图扮演上帝，设计一个完美的单一物种，可以最佳地利用地球资源。这是一种徒劳的努力，破坏了教育的崇高目标。类比而言，大学后期的专业化，等于把物种形成推迟到恐龙出现的时候。

There is a simple mountain climb metaphor for the superiority of the learn drive over direct instruction:

有一个简单的爬山比喻^[13]，学习内驱力^[4]对于直接教学的优越性。

In mountain climbing, the adult may see the summit (goals), but the child can see the path (via the learn drive). The adult will always attempt to deterministically go for the summit in sight. The child may climb to new heights (i.e. new discoveries). The view of the path ensures local optimality of the climb, and global optimality for a population of climbers. This way the individual climb does not need to be globally optimal nor deterministic. For more see: Mountain climb metaphor of schooling

在登山过程中，成人可能会看到山顶（目标），但孩子可以看到路径（通过学习内驱力）。成人总是会试图确定性地去追求眼前的高峰。孩子可能会攀登到新的高度（即新的发现）。视线的路径保证了攀登的局部最优性，也保证了攀登者群体的全局最优性。这样个体的攀登不需要全局最优，也不需要确定性。详见：登山的学校教育类比^[13]

Architectural differentiation

结构上的差异

The power of free learning is rooted in the fact that the same abstract knowledge can be represented by different concept network architectures with different valuations, stabilities, retrivabilities, etc.

自由学习^[5]的力量根源于同一抽象知识^[7]可以由不同的概念网络^[1]架构来表示，其估值、稳定性、可提取性等都不相同。

Different networks may produce different outcomes for the same input. They may produce different solutions to the same problem. Most of all, they may favor different models for the same supporting evidence.

不同的网络可能对相同的输入产生不同的结果。它们可能会对同一个问题产生不同的解决方案。最重要的是，它们可能对相同的支持证据青睐不同的模型。

Even if the topology of the network was to be identical, the outcomes depend on link properties and a degree of creative randomness. The outcomes of conceptual computation feed back to the network and result in diversification that is inevitable even if the environmental inputs and brain states where to be identical.

即使网络的拓扑结构是相同的，结果也取决于链接属性和一定程度的创造性随机性。概念计算^[14]的结果会反馈到网络中，导致多样化，即使环境输入和大脑状态完全相同，多样化也是不可避免的。

For example, a scientist who arrives at studying the brain from the field of artificial neural networks may have connectionist misinterpretation of how the brain works. As I arrived to the same field of study via the two component model of long-term memory, I instantly favor the grandmother cell theory, which in turn favors my own take on the brain's conceptualization process, which in turn favors the differentiation in education, which loops back to my fervent support of free learning. A well-schooled approach would be to study the perfect textbook called "The Brain" and there would be no competing schools of thought. There would only be the one "true" school as defined in the perfect textbook.

例如，一个从人工神经网络领域抵达研究大脑的科学家，可能会对大脑的工作方式产生连接主义的误解。当我通过长期记忆的双组分模型^[15]到达同样的研究领域时，我立刻赞成祖母细胞理论，而祖母细胞理论又有利于我自己对大脑概念化^[2]过程的看法，而祖母细胞理论又有利于教育中的差异化，这又环环相扣到我对自由学习^[5]的狂热支持。学得好的方法是学习一本完美的教科书《大脑》，不会有竞争的学派。只有那本完美的教科书中所定义的 "真正 "的学校。

For those architectural reasons, the sequence of learning determines the layering and the ultimate structure of knowledge. Homogenized learning at school, aims at identical models and identical architectures. In reality, schooling collapses due to the natural differentiation of concept network architectures. Instead of paddling against the river of diverging conceptualization processes, we should let each student build her own semantic framework for each abstract model. This is the key to human innovation.

由于这些架构原因，学习的顺序决定了知识的分层和最终结构。学校的同质化学习，目标是相同的模式和相同的架构。现实中，由于概念网络架构的自然分化，学校教育就会崩溃。与其在分化的概念化^[2]过程中逆流而上，不如让每个学生为每一个抽象的模型建立自己的语义框架^[16]。这是人类创新的关键。

Conceptualization is always divergent even if environmental inputs, brain states, and network topologies were identical at the starting point

即使环境输入、大脑状态和网络拓扑在起点上是相同的，概念化^[2]也总是分化的

Videogame argument

电子游戏讨论

The most often raised argument against the optimality of the learn drive is the claim that kids left free to choose would play videogames all day long.

最常被提出的反对学习内驱力^[4]优化的论点，是说让孩子们自由选择会整天玩电子游戏。

Parents are right that kids would indeed binge on gaming in the first period of freedom. That binging would gradually ease due to the reward depletion that increases valuations of competitive rewards: friends, sports, YouTube, social media, etc.

家长们说的没错，孩子们在自由的最初时期确实会狂热地打游戏。这种狂欢会因为奖励的消耗，增加对竞争性奖励的估价而逐渐缓解：朋友、运动、YouTube、社交媒体等。

School is the prime cause of that game binging. Young kids may grow with a gaming console used by their father, only to begin their true adventure with gaming in proportion to the pressures of schooling. At some point, a secondary factor may start playing a role: inconsistent parent generating variable reward by occasional bans or limits on digital devices. This can spiral into a true addiction that may take quite a while to recover from at the time when the child is given total freedom.

学校是造成这种游戏狂欢的首要原因。年幼的孩子可能会伴随着父亲使用的游戏机成长，只是在学业的压力下，他们才开始真正的游戏冒险，比例。在某些时候，一个次要因素可能会开始发挥作用：前后不一致的父母通过偶尔禁止或限制数字设备产生可变的奖励。这可能会演变成一种真正的成瘾，在孩子获得完全自由的时候，可能需要相当长的时间才能恢复。

Even a simple and consistent limit on gaming may backfire if it is too narrow relative to the needs. If there is no saturation, if the child is left unsatisfied, the value of the reward of gaming will increase by sensitization. This is analogous to sensitization that occurs when we are allowed to incompletely satisfy the thirst for water (e.g. at 80% only). This will result in relative suppression of other sources of reward even if they are freely available. Next time we are thirsty, we may fight for water a bit earlier and a bit harder. By choosing narrow time margins for gaming, the restrictions may increase the craving even if they are set consistently.

如果相对于需求来说太过狭窄，即使是简单而一致的游戏限制，也可能适得其反。如果没有饱和，如果让孩子得不到满足，游戏的奖励价值就会因敏化而增加。这类似于当我们被允许不完全满足对水的渴求时发生的敏化（如只满足80%）。这将导致对其他报酬来源的相对抑制，即使它们可以自由获得。下次口渴时，我们可能会更早、更努力地争取水。通过选择狭窄的时间边际进行博弈，即使持续设置限制，也可能会增加渴望。

The optimality of the learn drive can be undermined in the same way as a single pest species can ruin a perfectly harmonious ecosystem.

学习内驱力^[4]的优化会被破坏，就像一个害虫物种会破坏一个完美和谐的生态系统一样。

Even a small seemingly rational intervention in the reward system of the brain may override the reward of the learn drive and undermine its optimality

即使是对大脑的奖励系统进行小小的看似合理的干预，也可能会盖过学习内驱力^[4]的奖励，破坏其优化。

Local maximum problem

局部最优问题

In the optimization process, there are blind pathways strewn with candy leading kids astray in videogames. In theory it seems possible to design a learning space in which a human would land in a local maximum of some virtual reality. Such a design is unlikely in the light of the access to an infinite variety of explorations on the web. However, it is interesting theoretically.

在优化过程中，有一些胡同散落着糖果，将孩子们引入电子游戏的歧途。理论上，似乎可以设计一个学习空间，让人在某个虚拟现实的局部最大限度地下降。鉴于在网络上可以获得无限多样的探索，这样的设计是不太可能的。然而，从理论上讲，它是有趣的。

If someone was to find a fake summit in virtual reality, coercive learning is the exact societal error that could perpetuate the fake find for ever. Learn drive is partially stochastic and its optimality must be seen from the populational point of view.

如果有人在虚拟现实中发现一个假的极值，强制学习正是社会的错误，它可以使假的发现永远延续下去。学习驱内驱力是部分随机的，它的最优性必须从群体的角度来看。

Optimality of the learn drive refers to its being the best comparator of the value of knowledge, and of the information channels. It does not mean that the learn drive is free from the competition from other rewards (e.g. gambling, alcohol, sex, etc.). The key to harmonious development is freedom. It is the limits on freedom that result in reward deprivation that may lead to addictions (incl. game addiction).

学习内驱力的优化是指它是知识价值、信息渠道的最佳比较者。这并不意味着学习内驱力可以不受其他奖励（如赌、酒、性等）的竞争。和谐发展的关键是自由。正是由于对自由的限制，导致奖励被剥夺，才可能导致成瘾（包括游戏成瘾）。

If the learn drive was to land one in a local maximum, freedom to explore is the best chance to get out of the trap, at least as a population

如果说学习内驱力^[4]是让人陷入局部极小值，那么探索的自由就是跳出局部陷阱的最好机会，至少作为一个群体来说

Optimal control theory

优化控制理论

There is an ironic twist to the concept of the learn drive, and its use in education. One of the last acts of coercion in my 22 years of schooling was a clash with Professor Puchalka at the University of Technology. In 1986, I was finally free to learn the way I wanted. Having gotten rid of my military service problem, I was free to quit the university. However, I opted to pursue an MS degree in computer science using a so-called individual path of study. I could add and remove subjects from my list of books to study (see: How I invented perfect schooling). There was only one caveat. My new path had to be approved by the faculty and Prof. Puchalka agreed on one condition: his own lecture on control theory was to remain on the list. It remained compulsory (if I wanted a degree). Puchalka said "control theory is everything. No engineer could leave the school with a degree without understanding the subject". He was right: control theory dominates so many branches of science that without it, we keep choosing wrong strategies all over the place. Puchalka was also awfully wrong. It is precisely the control theory that should tell him that you cannot control the learn drive of a student.

学习内驱力^[4]的概念及其在教育中的应用有一个讽刺性的转折。在我 22 年的求学生涯中，最后一次强迫行为是在理工大学与普查卡教授发生冲突。1986 年，我终于可以自由地按照自己的意愿学习了。摆脱了兵役问题后，我可以自由地退出大学。但是，我选择了使用所谓的个人学习路径，攻读计算机科学硕士学位。我可以从我的书本清单中添加和删除科目来学习（见：我如何发明完美的学校教育）。只有一个注意事项。我的新路径必须得到学院的批准，普查尔卡教授同意了一个条件：他自己关于控制理论的讲座要留在清单上。它仍然是必修课（如果我想获得学位的话）。Puchalka 说："控制理论就是一切。任何工程师都不可能在不了解这门学科的情况下离开学校，获得学位"。他说得没错：控制理论支配着科学的许多分支，如果没有它，我们就会不断地选择错误的策略，到处都是。普查尔卡也错得很离谱。恰恰是控制理论应该告诉他，你无法控制学生的学习内驱力^[4]。

The interaction of (1) the conceptualizing brain armed with the learn drive with (2) the environment is an example of a continuously operating dynamical system, i.e. the exact kind of system Prof. Puchalka wanted me to study. Stable and effective control is based on the freedom of choice. The learn drive guidance system is the optimum controller in the learning process. The learn drive uses knowledge valuation network as a filter of the input received by the sensor. That filtered signal is fed into a signal comparator. The controlled process variable is learntropy of information channels available in the environment. It is compared with the expected value of learntropy derived as a trailing average of input valuation. When the value of the signal drops below a certain level, the learn drive system may initiate a search for new sources of knowledge.

(1)用学习内驱力^[4]武装起来的概念化^[2]的大脑与(2)环境的相互作用是一个持续运行的动态系统的例子，也就是说，这正是普查尔卡教授要我研究的系统。稳定而有效的控制是建立在自由选择的基础上的。学习内驱力引导系统是学习过程中的最优控制器。学习内驱力使用知识估值网络^[3]作为传感器接收的输入的过滤器。该滤波后的信号被输入到信号比较器中。被控制的过程变量是环境中可用信息通道的学习熵^[11]。它与作为输入估值的跟踪平均值而得出的学习力的期望值进行比较。当信号的值降到某一水平以下时，学习内驱力系统可以启动搜索新的知识源。

Only the learn drive system maximizes the learntropy of the input signal. A teacher introduces a control error in the system. Reactance is the controller's response to the error. Freedom is a conditio sine qua non of efficient learning. That includes the freedom to skip control theory. I would be back to control theory anyway; at the right time, in the right context.

只有学习内驱力^[4]系统能使输入信号的学习熵^[11]最大化。老师在系统中引入一个控制误差。反应是控制器对误差的响应。自由是高效学习的必要条件。包括跳过控制理论的自由。反正我会回到控制理论；在合适的时间，合适的环境下。

You cannot make a student learn efficiently without building a foundation in the knowledge valuation network. Even worse, coercion leads to reactance that may hurt the learning process. In 1986, I was desperate to learn programming. I was right to give programming a priority. Not only did programming lay some groundwork for a better understanding of the control theory (algorithms can be more intuitive than calculus). More importantly, programming directed me on the path to SuperMemo that totally changed the course of my life. Puchalka was a great expert in control theory, but this did not help him understand the optimum theory of control of the learning process. I cheated on the exam (probably the only time in my life), left school with very poor understanding of control theory, and had some toxic associations with the subject for quite a few years afterwards. Luckily, dendritic exploration of the world of knowledge had to lead me back to the subject. It is the control theory that provides the theoretical underpinnings of the optimality of the learn drive. Teachers should never coerce students into learning. The smarter the kid, the bigger the reactance and the more violent the defense of one's own autonomy. 33 years later, professor Puchalka has long retired, and I feel utterly vindicated. He was right about the value of control theory, but I was right about my terms of learning. This episode from my own history adds extra fuel to my fight for the educational liberation of the young generation: Compulsory schooling must end

如果不在知识估值网络^[3]中建立基础，你就无法让学生高效学习。更糟糕的是，强迫会导致反抗，可能会伤害学习过程。1986 年，我急切地想学习编程。我把编程放在优先位置是正确的。编程不仅为更好地理解控制理论奠定了一些基础（算法可以比微积分更直观）。更重要的是，编程引导我走上了 SuperMemo 的道路，彻底改变了我的人生轨迹。普查尔卡是控制理论方面的专家，但这并不能帮助他理解学习过程的最佳控制理论。我在考试中作弊（可能是我一生中唯一的一次），离开学校时对控制理论的理解非常不透彻，在之后的相当长的时间里，我对这门学科产生了一些有毒的联想。幸运的是，树枝状的知识世界的探索，不得不让我回到了这个学科。正是控制论为学习内驱力^[4]的优化提供了理论基础。教师千万不要强迫学生学习。越是聪明的孩子，反抗越大，对自己的自主性的捍卫越猛烈。33 年后，普恰尔卡教授早已退休，我觉得彻底平反了。他对控制理论的价值是对的，但我对自己的学习条件是对的。我自己历史上的这段插曲，为我争取年轻一代的教育解放增添了额外的内驱力：强制教育必须结束^[17]

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