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The free energy principle is a formal statement that explains how living and non-living systems remain in non-equilibrium steady-states by restricting themselves to a limited number of states. It establishes that systems minimise a free energy function of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception in neuroscience, where it is also known as active inference.

The free energy principle is a formal statement that explains how living and non-living systems remain in non-equilibrium steady-states by restricting themselves to a limited number of states. It establishes that systems minimise a free energy function of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception in neuroscience, where it is also known as active inference.

<font color="#ff8000"> 自由能原理Free energy principle</font>是一个正式的陈述，它解释了生物系统和非生物系统如何通过将自己限制在有限的几个状态而保持在非平衡稳态。它表明系统最小化了内部状态的自由能函数，而内部状态包含了对环境中隐藏状态的信念。自由能的内隐最小化在形式上与变分贝叶斯方法有关，最初由 Karl Friston 引入，作为<font color="#ff8000">神经科学</font>中对具身知觉的解释，在那里它也被称为<font color="#ff8000"> 主动推理Active inference</font>。

<font color="#ff8000"> 自由能原理Free energy principle</font>是一个正式的陈述，它解释了生物系统和非生物系统如何通过将自己限制在有限的几个状态而保持在<font color="#ff8000"> 非平衡稳态Non-equilibrium steady-states</font>。它表明系统最小化了内部状态的自由能函数，而内部状态包含了对环境中隐藏状态的信任。自由能的内隐最小化在形式上与<font color="#ff8000"> 变分贝叶斯方法Variational Bayesian methods</font>有关，最初由 Karl Friston 引入，作为<font color="#ff8000">神经科学</font>中对具身知觉的解释，在那里它也被称为<font color="#ff8000"> 主动推理Active inference</font>。

The free energy principle explains the existence of a given system by modeling it through a Markov blanket that tries to minimize the difference between their model of the world and their sense and associated perception. This difference can be described as "surprise" and is minimized by continuous correction of the world model of the system. As such, the principle is based on the Bayesian idea of the brain as an “inference engine”. Friston added a second route to minimization: action. By actively changing the world into the expected state, systems can also minimize the free energy of the system. Friston assumes this to be the principle of all biological reaction. Friston also believes his principle applies to mental disorders as well as to artificial intelligence. AI implementations based on the active inference principle have shown advantages over other methods. Discussions of the principle have also been criticized as invoking metaphysical assumptions far removed from a testable scientific prediction, making the principle unfalsifiable. In a 2018 interview, Friston acknowledged that the free energy principle is not properly falsifiable: "the free energy principle is what it is — a principle. Like Hamilton’s Principle of Stationary Action, it cannot be falsified. It cannot be disproven. In fact, there’s not much you can do with it, unless you ask whether measurable systems conform to the principle."

The free energy principle explains the existence of a given system by modeling it through a Markov blanket that tries to minimize the difference between their model of the world and their sense and associated perception. This difference can be described as "surprise" and is minimized by continuous correction of the world model of the system. As such, the principle is based on the Bayesian idea of the brain as an “inference engine”. Friston added a second route to minimization: action. By actively changing the world into the expected state, systems can also minimize the free energy of the system. Friston assumes this to be the principle of all biological reaction. Friston also believes his principle applies to mental disorders as well as to artificial intelligence. AI implementations based on the active inference principle have shown advantages over other methods. Discussions of the principle have also been criticized as invoking metaphysical assumptions far removed from a testable scientific prediction, making the principle unfalsifiable. In a 2018 interview, Friston acknowledged that the free energy principle is not properly falsifiable: "the free energy principle is what it is — a principle. Like Hamilton’s Principle of Stationary Action, it cannot be falsified. It cannot be disproven. In fact, there’s not much you can do with it, unless you ask whether measurable systems conform to the principle."

<font color="#ff8000">自由能原理</font>解释了一个给定系统的存在，它通过一个马尔可夫覆盖层建模，试图最小化他们的世界模型和他们的感觉和相关知觉之间的差异。这种差异可以被描述为”出其不意” ，并通过不断修正系统的世界模型来减少这种差异。因此，这个原理是基于贝叶斯的观点，即大脑是一个“推理机”。弗里斯顿为最小化增加了第二条路线: 行动。通过积极地将世界改变为预期的状态，系统也可以使系统的自由能最小化。弗里斯顿认为这是所有生物反应的原理。弗里斯顿还认为，他的原则即适用于精神障碍也适用于人工智能。基于主动推理原则的人工智能实现比其他方法显示出优势。关于这一原则的讨论也受到批评，认为它引用的形而上学假设与可检验的科学预测相去甚远，使这一原则不可证伪。在2018年的一次采访中，弗里斯顿承认，自由能原理不能被恰当地证伪: “自由能原理就是它的本来面目ーー一个原理。就像汉密尔顿的静止作用原理一样，它不能被证伪。这是不能被推翻的。事实上，除非你问可衡量的系统是否符合这一原则，否则你用它做不了什么。”

<font color="#ff8000">自由能原理</font>解释了一个给定系统的存在，它通过一个<font color="#ff8000">马尔可夫毯Markov blanket</font>建模，试图最小化他们的世界模型和他们的感觉和相关知觉之间的差异。这种差异可以被描述为”出其不意” ，并通过不断修正系统的世界模型来减少这种差异。因此，这个原理是基于贝叶斯的观点，即大脑是一个“推理机”。弗里斯顿为最小化增加了第二条路线: 行动。通过积极地将世界改变为预期的状态，系统也可以使系统的自由能最小化。弗里斯顿认为这是所有生物反应的原理。弗里斯顿还认为，他的原则即适用于精神障碍也适用于人工智能。基于主动推理原则的人工智能实现比其他方法显示出优势。关于这一原则的讨论也受到批评，认为它引用的形而上学假设与可检验的科学预测相去甚远，使这一原则不可证伪。在2018年的一次采访中，弗里斯顿承认，自由能原理不能被恰当地证伪: “自由能原理就是它的本来面目ーー一个原理。就像汉密尔顿的静止作用原理一样，它不能被证伪。这是不能被推翻的。事实上，除非你问可衡量的系统是否符合这一原则，否则你用它做不了什么。”

The notion that self-organising biological systems – like a cell or brain – can be understood as minimising variational free energy is based upon Helmholtz’s work on unconscious inference  and subsequent treatments in psychology and machine learning. Variational free energy is a function of observations and a probability density over their hidden causes. This variational density is defined in relation to a probabilistic model that generates predicted observations from hypothesized causes. In this setting, free energy provides an approximation to Bayesian model evidence. Therefore, its minimisation can be seen as a Bayesian inference process. When a system actively makes observations to minimise free energy, it implicitly performs active inference and maximises the evidence for its model of the world.

The notion that self-organising biological systems – like a cell or brain – can be understood as minimising variational free energy is based upon Helmholtz’s work on unconscious inference  and subsequent treatments in psychology and machine learning. Variational free energy is a function of observations and a probability density over their hidden causes. This variational density is defined in relation to a probabilistic model that generates predicted observations from hypothesized causes. In this setting, free energy provides an approximation to Bayesian model evidence. Therefore, its minimisation can be seen as a Bayesian inference process. When a system actively makes observations to minimise free energy, it implicitly performs active inference and maximises the evidence for its model of the world.