我们展示了任何具有自由度和局部自由能的系统如何在自由能原理的限制下,都将发展朝着支持层次结构计算的神经形态形态发展,在该计算中,每个层次结构的每个级别都会构成其投入的粗糙度。,并双重地将其输出的细粒度。这种层次结构发生在整个生物学中,从细胞内信号转导途径的体系结构到哺乳动物大脑中的感知和动作周期的大规模组织。正式地,一方面,锥体 - 康基图(CCCD)作为量子参考帧的模型,另一方面是CCCDS和拓扑量子场理论之间的近距离形式连接,允许在全剂量量子中代表此类计算拓扑量子神经网络的计算框架。
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This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants$\unicode{x2014}$what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world$\unicode{x2014}$also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing$\unicode{x2014}$leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first$\unicode{x2014}$and key$\unicode{x2014}$step towards such an ecology.
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预测性编码提供了对皮质功能的潜在统一说明 - 假设大脑的核心功能是最小化有关世界生成模型的预测错误。该理论与贝叶斯大脑框架密切相关,在过去的二十年中,在理论和认知神经科学领域都产生了重大影响。基于经验测试的预测编码的改进和扩展的理论和数学模型,以及评估其在大脑中实施的潜在生物学合理性以及该理论所做的具体神经生理学和心理学预测。尽管存在这种持久的知名度,但仍未对预测编码理论,尤其是该领域的最新发展进行全面回顾。在这里,我们提供了核心数学结构和预测编码的逻辑的全面综述,从而补充了文献中最新的教程。我们还回顾了该框架中的各种经典和最新工作,从可以实施预测性编码的神经生物学现实的微电路到预测性编码和广泛使用的错误算法的重新传播之间的紧密关系,以及对近距离的调查。预测性编码和现代机器学习技术之间的关系。
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Despite the wealth of empirical data in neuroscience, there are relatively few global theories about how the brain works. A recently proposed free-energy principle for adaptive systems tries to provide a unified account of action, perception and learning. Although this principle has been portrayed as a unified brain theory 1 , its capacity to unify different perspectives on brain function has yet to be established. This Review attempts to place some key theories within the free-energy framework, in the hope of identifying common themes. I first review the free-energy principle and then deconstruct several global brain theories to show how they all speak to the same underlying idea. The free-energy principleThe free-energy principle (BOX 1) says that any selforganizing system that is at equilibrium with its environment must minimize its free energy 2 . The principle is essentially a mathematical formulation of how adaptive systems (that is, biological agents, like animals or brains) resist a natural tendency to disorder [3][4][5][6] . What follows is a non-mathematical treatment of the motivation and implications of the principle. We will see that although the motivation is quite straightforward, the implications are complicated and diverse. This diversity allows the principle to account for many aspects of brain structure and function and lends it the potential to unify different perspectives on how the brain works. In subsequent sections, I discuss how the principle can be applied to neuronal systems as viewed from these perspectives. This Review starts in a rather abstract and technical way but then tries to unpack the basic idea in more familiar terms.Motivation: resisting a tendency to disorder. The defining characteristic of biological systems is that they maintain their states and form in the face of a constantly changing environment [3][4][5][6] . From the point of view of the brain, the environment includes both the external and the internal milieu. This maintenance of order is seen at many levels and distinguishes biological from other self-organizing systems; indeed, the physiology of biological systems can be reduced almost entirely to their homeostasis 7 . More precisely, the repertoire of physiological and sensory states in which an organism can be is limited, and these states define the organism's phenotype. Mathematically, this means that the probability of these (interoceptive and exteroceptive) sensory states must have low entropy; in other words, there is a high probability that a system will be in any of a small number of states, and a low probability that it will be in the remaining states. Entropy is also the average self information or 'surprise' 8 (more formally, it is the negative log-probability of an outcome). Here, 'a fish out of water' would be in a surprising state (both emotionally and mathematically). A fish that frequently forsook water would have high entropy. Note that both surprise and entropy depend on the agen
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In my previous article I mentioned for the first time that a classical neural network may have quantum properties as its own structure may be entangled. The question one may ask now is whether such a quantum property can be used to entangle other systems? The answer should be yes, as shown in what follows.
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每个已知的人工深神经网络(DNN)都对应于规范Grothendieck的拓扑中的一个物体。它的学习动态对应于此拓扑中的形态流动。层中的不变结构(例如CNNS或LSTMS)对应于Giraud的堆栈。这种不变性应该是对概括属性的原因,即从约束下的学习数据中推断出来。纤维代表语义前类别(Culioli,Thom),在该类别上定义了人工语言,内部逻辑,直觉主义者,古典或线性(Girard)。网络的语义功能是其能够用这种语言表达理论的能力,以回答输出数据中有关输出的问题。语义信息的数量和空间是通过类比与2015年香农和D.Bennequin的Shannon熵的同源解释来定义的。他们概括了Carnap和Bar-Hillel(1952)发现的措施。令人惊讶的是,上述语义结构通过封闭模型类别的几何纤维对象进行了分类,然后它们产生了DNNS及其语义功能的同位不变。故意类型的理论(Martin-Loef)组织了这些物体和它们之间的纤维。 Grothendieck的导数分析了信息内容和交流。
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在过去的几年中,计算机视觉的显着进步总的来说是归因于深度学习,这是由于大量标记数据的可用性所推动的,并与GPU范式的爆炸性增长配对。在订阅这一观点的同时,本书批评了该领域中所谓的科学进步,并在基于信息的自然法则的框架内提出了对愿景的调查。具体而言,目前的作品提出了有关视觉的基本问题,这些问题尚未被理解,引导读者走上了一个由新颖挑战引起的与机器学习基础共鸣的旅程。中心论点是,要深入了解视觉计算过程,有必要超越通用机器学习算法的应用,而要专注于考虑到视觉信号的时空性质的适当学习理论。
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This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.
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在当前的嘈杂中间尺度量子(NISQ)时代,量子机学习正在成为基于程序门的量子计算机的主要范式。在量子机学习中,对量子电路的门进行了参数化,并且参数是根据数据和电路输出的测量来通过经典优化来调整的。参数化的量子电路(PQC)可以有效地解决组合优化问题,实施概率生成模型并进行推理(分类和回归)。该专着为具有概率和线性代数背景的工程师的观众提供了量子机学习的独立介绍。它首先描述了描述量子操作和测量所必需的必要背景,概念和工具。然后,它涵盖了参数化的量子电路,变异量子本质层以及无监督和监督的量子机学习公式。
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在本章中,我们确定了基本的几何结构,这些几何结构是采样,优化,推理和自适应决策问题的基础。基于此识别,我们得出了利用这些几何结构来有效解决这些问题的算法。我们表明,在这些领域中自然出现了广泛的几何理论,范围从测量过程,信息差异,泊松几何和几何整合。具体而言,我们解释了(i)如何利用汉密尔顿系统的符合性几何形状,使我们能够构建(加速)采样和优化方法,(ii)希尔伯特亚空间和Stein操作员的理论提供了一种通用方法来获得可靠的估计器,(iii)(iii)(iii)保留决策的信息几何形状会产生执行主动推理的自适应剂。在整个过程中,我们强调了这些领域之间的丰富联系。例如,推论借鉴了抽样和优化,并且自适应决策通过推断其反事实后果来评估决策。我们的博览会提供了基本思想的概念概述,而不是技术讨论,可以在本文中的参考文献中找到。
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量子哈密顿学习和量子吉布斯采样的双重任务与物理和化学中的许多重要问题有关。在低温方案中,这些任务的算法通常会遭受施状能力,例如因样本或时间复杂性差而遭受。为了解决此类韧性,我们将量子自然梯度下降的概括引入了参数化的混合状态,并提供了稳健的一阶近似算法,即量子 - 固定镜下降。我们使用信息几何学和量子计量学的工具证明了双重任务的数据样本效率,因此首次将经典Fisher效率的开创性结果推广到变异量子算法。我们的方法扩展了以前样品有效的技术,以允许模型选择的灵活性,包括基于量子汉密尔顿的量子模型,包括基于量子的模型,这些模型可能会规避棘手的时间复杂性。我们的一阶算法是使用经典镜下降二元性的新型量子概括得出的。两种结果都需要特殊的度量选择,即Bogoliubov-Kubo-Mori度量。为了从数值上测试我们提出的算法,我们将它们的性能与现有基准进行了关于横向场ISING模型的量子Gibbs采样任务的现有基准。最后,我们提出了一种初始化策略,利用几何局部性来建模状态的序列(例如量子 - 故事过程)的序列。我们从经验上证明了它在实际和想象的时间演化的经验上,同时定义了更广泛的潜在应用。
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FIG. 1. Schematic diagram of a Variational Quantum Algorithm (VQA). The inputs to a VQA are: a cost function C(θ), with θ a set of parameters that encodes the solution to the problem, an ansatz whose parameters are trained to minimize the cost, and (possibly) a set of training data {ρ k } used during the optimization. Here, the cost can often be expressed in the form in Eq. ( 3), for some set of functions {f k }. Also, the ansatz is shown as a parameterized quantum circuit (on the left), which is analogous to a neural network (also shown schematically on the right). At each iteration of the loop one uses a quantum computer to efficiently estimate the cost (or its gradients). This information is fed into a classical computer that leverages the power of optimizers to navigate the cost landscape C(θ) and solve the optimization problem in Eq. ( 1). Once a termination condition is met, the VQA outputs an estimate of the solution to the problem. The form of the output depends on the precise task at hand. The red box indicates some of the most common types of outputs.
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这篇理论文章研究了如何在计算机中构建类似人类的工作记忆和思维过程。应该有两个工作记忆存储,一个类似于关联皮层中的持续点火,另一个类似于大脑皮层中的突触增强。这些商店必须通过环境刺激或内部处理产生的新表示不断更新。它们应该连续更新,并以一种迭代的方式进行更新,这意味着在下一个状态下,应始终保留一组共同工作中的某些项目。因此,工作记忆中的一组概念将随着时间的推移逐渐发展。这使每个状态都是对先前状态的修订版,并导致连续的状态与它们所包含的一系列表示形式重叠和融合。随着添加新表示形式并减去旧表示形式,在这些更改过程中,有些保持活跃几秒钟。这种持续活动,类似于人工复发性神经网络中使用的活动,用于在整个全球工作区中传播激活能量,以搜索下一个关联更新。结果是能够朝着解决方案或目标前进的联想连接的中间状态链。迭代更新在这里概念化为信息处理策略,一种思想流的计算和神经生理决定因素以及用于设计和编程人工智能的算法。
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近年来,机器学习的巨大进步已经开始对许多科学和技术的许多领域产生重大影响。在本文的文章中,我们探讨了量子技术如何从这项革命中受益。我们在说明性示例中展示了过去几年的科学家如何开始使用机器学习和更广泛的人工智能方法来分析量子测量,估计量子设备的参数,发现新的量子实验设置,协议和反馈策略,以及反馈策略,以及通常改善量子计算,量子通信和量子模拟的各个方面。我们重点介绍了公开挑战和未来的可能性,并在未来十年的一些投机愿景下得出结论。
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十年自2010年以来,人工智能成功一直处于计算机科学和技术的最前沿,传染媒介空间模型已经巩固了人工智能最前沿的位置。与此同时,量子计算机已经变得更加强大,主要进步的公告经常在新闻中。这些区域的基础的数学技术比有时意识到更多的共同之处。传染媒介空间在20世纪30年代的量子力学的公理心脏上采取了位置,这一采用是从矢量空间的线性几何形状推导逻辑和概率的关键动机。粒子之间的量子相互作用是使用张量产品进行建模的,其也用于表达人工神经网络中的物体和操作。本文介绍了这些常见的数学区域中的一些,包括如何在人工智能(AI)中使用的示例,特别是在自动推理和自然语言处理(NLP)中。讨论的技术包括矢量空间,标量产品,子空间和含义,正交投影和否定,双向矩阵,密度矩阵,正算子和张量产品。应用领域包括信息检索,分类和含义,建模字传感和歧义,知识库的推断和语义构成。其中一些方法可能会在量子硬件上实现。该实施中的许多实际步骤都处于早期阶段,其中一些已经实现了。解释一些常见的数学工具可以帮助AI和量子计算中的研究人员进一步利用这些重叠,识别和沿途探索新方向。
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有效推论是一种数学框架,它起源于计算神经科学,作为大脑如何实现动作,感知和学习的理论。最近,已被证明是在不确定性下存在国家估算和控制问题的有希望的方法,以及一般的机器人和人工代理人的目标驱动行为的基础。在这里,我们审查了最先进的理论和对国家估计,控制,规划和学习的积极推断的实现;描述当前的成就,特别关注机器人。我们展示了相关实验,以适应,泛化和稳健性而言说明其潜力。此外,我们将这种方法与其他框架联系起来,并讨论其预期的利益和挑战:使用变分贝叶斯推理具有功能生物合理性的统一框架。
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The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., tendency to oversimplify) and prior technological limitations in favor of a more continuous, gradualist view necessitated by the study of evolution, developmental biology, and intelligent machines. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing" - the ability of the same substrate to simultaneously compute different things. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of computational materials as reported in the rapidly-growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events, as it has already done at quantum and relativistic scales. Here, we review examples of biological and technological polycomputing, and develop the idea that overloading of different functions on the same hardware is an important design principle that helps understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
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自由能原理及其必然的积极推论构成了一种生物启发的理论,该理论假设生物学作用保留在一个受限制的世界首选状态中,即它们最小化自由能。根据这一原则,生物学家学习了世界的生成模型和未来的计划行动,该模型将使代理保持稳态状态,以满足其偏好。该框架使自己在计算机中实现,因为它理解了使其计算负担得起的重要方面,例如变异推断和摊销计划。在这项工作中,我们研究了深度学习的工具,以设计和实现基于主动推断的人造代理,对自由能原理进行深入学习的呈现,调查工作与机器学习和主动推理领域相关,以及讨论实施过程中涉及的设计选择。该手稿探究了积极推理框架的新观点,将其理论方面扎根于更务实的事务中,为活跃推理的新手提供了实用指南,并为深度学习从业人员的起点提供了研究,以调查自由能源原则的实施。
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积极推断是复杂系统中的认知和行为的叙述,它在贝叶斯推论的理论地幔下举起动作,感知和学习。积极的推论已经看到学术研究中的应用越来越多,特别是在寻求模拟人类或动物行为的领域。虽然近年来,来自有效推理文献产生的一些代码已经用Python和Julia这样的开源语言编写,迄今为止,用于模拟活动推理代理的最流行的软件是SPM,Matlab库的DEM工具箱最初开发用于神经影像数据的统计分析和建模。因此,在纯粹的数字和科学学科的应用程序方面,表现出对积极推断的兴趣,因此为在开源科学计算语言中模拟了激活推论的通用,广泛可用的和用户友好的代码,这一切都表现为纯粹的数字以及跨科学学科的应用程序。像python。我们在这里呈现的Python包,Pymdp(参见https://github.com/fifer-active/pymdp)表示朝这个方向的重要一步:即,我们提供了用于模拟有源推断的第一个开源包,部分 - 可观察的马尔可夫决策过程或POMDPS。我们查看包的结构,并解释了模块化设计和定制等优点,同时提供沿着文本代码块,以便演示如何使用它以轻松地构建和运行主动推断过程。我们开发了PyMDP,以增加有效推理框架的可访问性和暴露于有多种纪律背景的研究人员,工程师和开发人员。本着开源软件的精神,我们也希望它在不断增长的积极推理界中产生新的创新,发展和合作。
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我们建议出现的定量和客观概念。我们的建议使用算法信息理论作为一个客观框架的基础,其中某个字符串编码观测数据。这种字符串的Kolmogorov结构功能中有多个滴剂被视为出现的标志。我们的定义除了扩展了粗粒和边界条件的概念外,还提供了一些理论上的结果。最后,我们面对对动态系统和热力学的应用。
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