论文题目：Machine learning: Trends, perspectives, and prospects
论文来源:Machine learning: Trends, perspectives, and prospects_2015_Science
翻译人：BDML@CQUT实验室

Machine learning: Trends, perspectives, and prospects

M. I. Jordan1* and T. M. Mitchell2*

导读

Despite practical challenges, we are hopeful that informed discussions among policy-makers and the public about data and the capabilities of machine learning, will lead to insightful designs of programs and policies that can balance the goals of protecting privacy and ensuring fairness with those of reaping the benefits to scientific research and to individual and public health. Our commitments to privacy and fairness are evergreen, but our policy choices must adapt to advance them, and support new techniques for deepening our knowledge.

尽管存在实际挑战，但我们希望决策者之间就公开的数据和机器学习能力进行的知情讨论，将导致编程的深刻设计，可以在保护隐私达到一个目标平衡，坚信公平将会给科学研究、个人隐私、公共健康带来益处。我们对隐私和公平的承诺一直在坚持，但我们的个人决策选择必须适应不断发展的要求，并支持新技术以加深我们的知识。

Abstract

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

摘要

机器学习解决了如何通过经验学习自动改进的计算机的问题。它是当今发展最快的技术领域之一，位于计算机科学和统计学的交汇处，也是人工智能和数据科学的核心。机器学习的最新进展既受到新学习算法和理论的发展的推动，也受到在线数据和低成本计算的不断发展的推动。在科学，技术和商业中可以发现采用了数据密集型机器学习方法，从而导致在医疗，制造，教育，金融建模，警务和市场营销等众多领域进行了更多基于证据的决策。

正文

Machine learning is a discipline focused on two interrelated questions: How can one construct computer systems that automatically improve through experience? and What are the fundamental statistical-computational- information-theoretic laws that govern all learning systems, including computers, humans, and organizations? The study of machine learning is important both for addressing these fundamental scientific and engineering questions and for the highly practical computer software it has produced and fielded across many applications.

机器学习是一门专注于两个相互关联的问题的学科：如何构建一个通过经验自动改进的计算机系统？统计计算信息理论的基本定律是什么，支配着所有的学习系统，包括计算机、人类和组织？机器学习的研究对于解决这些基础科学和工程问题以及它在许多应用中产生和应用的高度实用的计算机软件都很重要。

Machine learning has progressed dramatically over the past two decades, from laboratory curiosity to a practical technology in widespread commercial use. Within artificial intelligence (AI), machine learning has emerged as the method of choice for developing practical software for computer vision, speech recognition, natural language processing, robot control, and other applications. Many developers of AI systems now recognize that, for many applications, it can be far easier to train a system by showing it examples of desired input-output behavior than to program it manually by anticipating the desired response for all possible inputs. The effect of machine learning has also been felt broadly across computer science and across a range of industries concerned with data-intensive issues, such as consumer services, the diagnosis of faults in complex systems, and the control of logistics chains. There has been a similarly broad range of effects across empirical sciences, from biology to cosmology to social science, as machine-learning methods have been developed to analyze high-throughput experimental data in novel ways. See Fig. 1 for a depiction of some recent areas of application of machine learning.

机器学习在过去的二十年里取得了巨大的进步，从实验室的好奇心到商业广泛应用的实用技术。在人工智能（AI）中，机器学习已经成为开发计算机视觉、语音识别、自然语言处理、机器人控制和其他应用的实用软件的首选方法。许多人工智能系统的开发人员现在认识到，对于一些应用来说，它能很轻易的去训练一个系统，只要展示它所期望的输入输出行为比人为手工编程来预计所有可能输入的反映。在计算机科学以及涉及数据密集型问题的一系列行业中，也广泛地感受到了机器学习的影响，例如消费者服务，复杂系统中的故障诊断以及物流链控制。从生物学到宇宙学再到社会科学，在整个经验科学领域都有类似的广泛影响，因为已经开发了机器学习方法来以新颖的方式分析高通量实验数据。图1描述了有关机器学习的一些最新应用领域。

Fig. 1. Applications of machine learning. Machine learning is having a substantial effect on many areas of technology and science; examples of recent applied success stories include robotics and autonomous vehicle control (top left), speech processing and natural language processing (top right), neuroscience research (middle), and applications in computer vision (bottom).

图1.机器学习的应用。机器学习对技术和科学的许多领域都产生了重大影响。最近应用的成功案例包括机器人技术和自动驾驶汽车控制（左上），语音处理和自然语言处理（右上），神经科学研究（中）和计算机视觉中的应用（下）。

A learning problem can be defined as the problem of improving some measure of performance when executing some task, through some type of training experience. For example, in learning to detect credit-card fraud, the task is to assign a label of “fraud” or “not fraud” to any given credit-card transaction. The performance metric to be improved might be the accuracy of this fraud classifier, and the training experience might consist of a collection of historical credit-card transactions, each labeled in retrospect as fraudulent or not. Alternatively, one might define a different performance metric that assigns a higher penalty when “fraud” is labeled “not fraud” than when “not fraud” is incorrectly labeled “fraud.” One might also define a different type of training experience—for example, by including unlabeled credit-card transactions along with labeled examples.

学习问题可以定义为通过某种类型的训练经验来提高执行某些任务时的某种性能度量的问题。例如，在学习检测信用卡欺诈时，任务是为任何给定的信用卡交易标记“欺诈”或“非欺诈”标签。待改进的性能指标可能是该欺诈分类器的准确性，并且培训经验可能包括历史信用卡交易的集合，每笔交易在回溯中都被标记为欺诈或不欺诈。或者，可以定义一个不同的性能指标，当“欺诈”被标记为“非欺诈”时，比“非欺诈”被错误地标记为“欺诈”时，受到更高的惩罚。还可以定义一种不同类型的训练实验，例如，通过包括未标记的信用卡交易以及标记的示例。

A diverse array of machine-learning algorithms has been developed to cover the wide variety of data and problem types exhibited across different machine-learning problems (1, 2). Conceptually, machine-learning algorithms can be viewed as searching through a large space of candidate programs, guided by training experience, to find a program that optimizes the performance metric. Machine-learning algorithms vary greatly, in part by the way in which they represent candidate programs (e.g., decision trees, mathematical functions, and general programming languages) and in part by the way in which they search through this space of programs (e.g., optimization algorithms with well-understood convergence guarantees and evolutionary search methods that evaluate successive generations of randomly mutated programs). Here, we focus on approaches that have
been particularly successful to date.

如今已经开发了各种各样的机器学习算法，以涵盖跨越不同机器学习问题（1、2）展示的各种数据和问题类型。从概念上讲，机器学习算法可以看作是在训练经验的指导下搜索大量候选程序，以找到优化性能指标的程序。机器学习算法的变化很大，部分原因是它们表示候选程序的方式（例如决策树，数学函数和通用编程语言），部分原因是它们在程序的空间中进行搜索的方式（例如，具有易于理解的收敛保证的优化算法和评估随机变异程序的连续生成的进化搜索方法）。在这里，我们将重点放在迄今为止特别成功的方法上。

Many algorithms focus on function approximation problems, where the task is embodied in a function (e.g., given an input transaction, output a “fraud” or “not fraud” label), and the learning problem is to improve the accuracy of that function, with experience consisting of a sample of known input-output pairs of the function. In some cases, the function is represented explicitly as a parameterized functional form; in other cases, the function is implicit and obtained via a search process, a factorization, an optimization procedure, or a simulation-based procedure. Even when implicit, the function generally depends on parameters or other tunable degrees of freedom, and training corresponds to finding values for these parameters that optimize the performance metric.

许多算法关注函数逼近问题，其中任务体现在函数中（例如，给定交易输入，输出“欺诈”或“非欺诈”标签），学习问题是提高该函数的准确性，经验具有由函数的已知输入/输出对组成的样本。在某些情况下，函数被明确表示为参数化函数形式。在其他情况下，该函数是隐式的，可以通过搜索过程，分解，优化过程或基于仿真的过程来获取。即使是隐式的，这个函数依靠参数或者其它的自由可调度，和训练去发现这些参数对应的值方法，来优化性能指标。

Whatever the learning algorithm, a key scientific and practical goal is to theoretically characterize the capabilities of specific learning algorithms and the inherent difficulty of any given learning problem: How accurately can the algorithm learn from a particular type and volume of training data? How robust is the algorithm to errors in its modeling assumptions or to errors in the training data? Given a learning problem with a given volume of training data, is it possible to design a successful algorithm or is this learning problem fundamentally intractable? Such theoretical characterizations of machine-learning algorithms and problems typically make use of the familiar frameworks of statistical decision theory and computational complexity theory. In fact, attempts to characterize machine-learning algorithms theoretically have led to blends of statistical and computational theory in which the goal is to simultaneously characterize the sample complexity (how much data are required to learn accurately) and the computational complexity (how much computation is required) and to specify how these depend on features of the learning algorithm such as the representation it uses for what it learns (3–6). A specific form of computational analysis that has proved particularly useful in recent years has been that of optimization theory, with upper and lower bounds on rates of convergence of optimization procedures merging well with the formulation of machine-learning problems as the optimization of a performance metric (7, 8).

无论采用哪种学习算法，关键的科学和实践目标都是从理论上描述特定学习算法的能力和任何给定学习问题的固有困难：该算法如何从特定类型和数量的训练数据中学习的准确？该算法如何解决其建模假设中的错误或训练数据中的错误？给定具有一定数量的训练数据的学习问题，是否有可能设计一个成功的算法，或者此学习问题从根本上是棘手的？机器学习算法和问题的这种理论表征通常利用统计决策理论和计算复杂性理论的熟悉框架。实际上，从理论上描述机器学习算法的尝试已导致统计和计算理论的融合，其目的是同时描述样本复杂度（需要多少数据才能准确学习）和计算复杂度（需要多少计算量），并指定这些内容如何依赖于学习算法的特征，例如特征可以用于它的学习（3-6）。最近几年证明特别有用的一种特殊形式的计算分析是优化理论，优化程序的收敛速度的上限和下限与作为性能指标优化的机器学习问题的公式很好地融合在一起（7、8）。

As a field of study, machine learning sits at the crossroads of computer science, statistics and a variety of other disciplines concerned with automatic improvement over time, and inference and decision-making under uncertainty. Related disciplines include the psychological study of human learning, the study of evolution, adaptive control theory, the study of educational practices, neuroscience, organizational behavior, and economics. Although the past decade has seen increased crosstalk with these other fields, we are just beginning to tap the potential synergies and the diversity of formalisms and experimental methods used across these multiple fields for studying systems that improve with experience.

作为一个研究领域，机器学习处于计算机科学，统计学和其他各种学科的十字路口，这些学科涉及与时俱进的自动改进以及不确定性下的推理和决策。相关学科包括人类学习的心理学研究，进化研究，自适应控制理论，教育实践研究，神经科学，组织行为学和经济学。尽管在过去的十年中，与其他领域的交叉有所增加，但我们才刚刚开始挖掘潜在的协同作用，以及在这些多个领域中使用的形式主义和实验方法的多样性，以研究随经验而改进的系统。

Drivers of machine-learning progress
The past decade has seen rapid growth in the ability of networked and mobile computing systems to gather and transport vast amounts of data, a phenomenon often referred to as “Big Data.” The scientists and engineers who collect such data have often turned to machine learning for solutions to the problem of obtaining useful insights, predictions, and decisions from such data sets. Indeed, the sheer size of the data makes it essential to develop scalable procedures that blend computational and statistical considerations, but the issue is more than the mere size of modern data sets; it is the granular, personalized nature of much of these data. Mobile devices and embedded computing permit large amounts of data to be gathered about individual humans, and machine-learning algorithms can learn from these data to customize their services to the needs and circumstances of each individual. Moreover, these personalized services can be connected, so that an overall service emerges that takes advantage of the wealth and diversity of data from many individuals while still customizing to the needs and circumstances of each. Instances of this trend toward capturing and mining large quantities of data to improve services and productivity can be found across many fields of commerce, science, and government. Historical medical records are used to discover which patients will respond best to which treatments; historical traffic data are used to improve traffic control and reduce congestion; historical crime data are used to help allocate local police to specific locations at specific times; and large experimental data sets are captured and curated to accelerate progress in biology, astronomy, neuroscience, and other dataintensive empirical sciences. We appear to be at the beginning of a decades-long trend toward increasingly data-intensive, evidence-based decisionmaking across many aspects of science, commerce, and government.

机器学习进度的驱动因素

在过去的十年中，网络和移动计算系统收集和传输大量数据的能力迅速增长，这种现象通常被称为“大数据”。收集此类数据的科学家和工程师经常转向机器学习，以解决从此类数据集获得有用的见解，预测和决策的问题。确实，庞大的数据量对于开发融合了计算和统计考虑因素的可扩展程序至关重要，但是问题不仅仅在于现代数据集的大小。这是许多数据的细化，个性化性质所导致的。移动设备和嵌入式计算允许收集大量有关个人的数据，并且机器学习算法可以从这些数据中学习以根据每个人的需求和情况定制其服务。此外，这些个性化服务可以连接在一起，从而使整体服务出现，该服务可以利用来自许多个人的丰富数据和多样性，同时仍可以根据每个人的需求和情况进行定制。在商业，科学和政府的许多领域中都可以找到这种趋势，即捕获和挖掘大量数据以改善服务和生产率的趋势。使用历史病历来发现哪些患者对哪种治疗最有效；历史交通数据用于改善交通控制和减少拥堵；历史犯罪数据用于帮助在特定时间将当地警察分配到特定地点；并收集和整理大型实验数据集，以加快生物学，天文学，神经科学和其他数据密集型经验科学的进展。我们似乎正处于数十年来的趋势的开始，这种趋势是在科学，商业和政府的许多方面进行越来越多的数据密集型，基于证据的决策。

With the increasing prominence of large-scale data in all areas of human endeavor has come a wave of new demands on the underlying machine-learning algorithms. For example, huge data sets require computationally tractable algorithms, highly personal data raise the need for algorithms that minimize privacy effects, and the availability of huge quantities of unlabeled data raises the challenge of designing learning algorithms to take advantage of it. The next sections survey some of the effects of these demands on recent work in machine-learning algorithms, theory, and practice.

随着大规模数据在人类努力的各个领域中日益重要，已经对底层的机器学习算法提出了新的要求。例如，海量数据集需要计算上容易处理的算法，高度个人化的数据提出了对最大限度减少隐私影响的算法的需求，而大量未标记数据的可用性提出了设计学习算法以利用它的挑战。下一部分将调查这些需求对机器学习算法，理论和实践中的最新工作的影响。

Core methods and recent progress
The most widely used machine-learning methods are supervised learning methods (1). Supervised learning systems, including spam classifiers of e-mail, face recognizers over images, and medical diagnosis systems for patients, all exemplify the function approximation problem discussed earlier, where the training data take the form of a collection of (x, y) pairs and the goal is to produce a prediction y* in response to a query x*. The inputs x may be classical vectors or they may be more complex objects such as documents, images, DNA sequences, or graphs. Similarly, many different kinds of output y have been studied. Much progress has been made by focusing on the simple binary classification problem in which y takes on one of two values (for example, “spam” or “not spam”), but there has also been abundant research on problems such as multiclass classification (where y takes on one of K labels), multilabel classification (where y is labeled simultaneously by several of the K labels), ranking problems (where y provides a partial order on some set), and general structured prediction problems (where y is a combinatorial object such as a graph, whose components may be required to satisfy some set of constraints). An example of the latter problem is part-of-speech tagging, where the goal is to simultaneously label every word in an input sentence x as being a noun, verb, or some other part of speech. Supervised learning also includes cases in which y has realvalued components or a mixture of discrete and real-valued components.

核心方法及近期进展

最广泛使用的机器学习方法是监督学习方法（1）。有监督的学习系统，包括电子邮件的垃圾邮件分类器，图像上的人脸识别器以及患者的医疗诊断系统，都说明了前面讨论的函数逼近问题，其中训练数据采用（x，y）对的集合形式并且目标是响应查询x*生成预测 y *。输入x可以是经典向量，也可以是更复杂的对象，例如文档，图像，DNA序列或图形。类似地，许多不同种类的输出y也被研究过。通过关注y取两个值之一（例如“垃圾邮件”或“非垃圾邮件”）的简单二进制分类问题，已经取得了很大的进步，但是对于诸如多类分类（其中y代表K个标签中的一个），多标签分类（其中y由K个标签中的几个同时标记），排序问题（其中y在某些集合上提供部分顺序）和一般的结构化预测问题（其中y是一个组合对象例如图形，可能需要其组件满足某些约束集）也进行了大量的研究。后一个问题的一个示例是词性标记，其中的目的是同时将输入句子x中的每个单词标记为名词，动词或其他词性。监督学习还包括y具有实值分量或离散分量和实值分量混合的情况。

Supervised learning systems generally form their predictions via a learned mapping f (x), which produces an output y for each input x (or a probability distribution over y given x). Many different forms of mapping f exist, including decision trees, decision forests, logistic regression, support vector machines, neural networks, kernel machines, and Bayesian classifiers (1). A variety of learning algorithms has been proposed to estimate these different types of mappings, and there are also generic procedures such as boosting and multiple kernel learning that combine the outputs of multiple learning algorithms. Procedures for learning f from data often make use of ideas from optimization theory or numerical analysis, with the specific form of machinelearning problems (e.g., that the objective function or function to be integrated is often the sum over a large number of terms) driving innovations. This diversity of learning architectures and algorithms reflects the diverse needs of applications, with different architectures capturing different kinds of mathematical structures, offering different levels of amenability to post-hoc visualization and explanation, and providing varying trade-offs between computational complexity, the amount of data, and performance.

监督学习系统通常通过学习映射f(x)形成预测，该映射为每个输入x生成输出y（或给定x上y的概率分布）。存在许多不同形式的映射f，包括决策树，决策森林，逻辑回归，支持向量机，神经网络，核机和贝叶斯分类器（1）。已经提出了多种学习算法来估计这些不同类型的映射，并且还存在诸如boost和多核学习之类的通用过程，其结合了多种学习算法的输出。从数据中学习f的过程通常利用优化理论或数值分析中的思想，并以特定形式的机器学习问题（例如，目标函数或要集成的函数通常是大量术语的总和）来推动创新。学习架构和算法的多样性反映了应用程序的不同需求，不同的架构捕获了不同种类的数学结构，为事后的可视化和解释提供了不同级别的适应性，并在计算复杂度，数据和性能提供了不同的权衡。

One high-impact area of progress in supervised learning in recent years involves deep networks, which are multilayer networks of threshold units, each of which computes some simple parameterized function of its inputs (9, 10). Deep learning systems make use of gradient-based optimization algorithms to adjust parameters throughout such a multilayered network based on errors at its output. Exploiting modern parallel computing architectures, such as graphics processing units originally developed for video gaming, it has been possible to build deep learning systems that contain billions of parameters and that can be trained on the very large collections of images, videos, and speech samples available on the Internet. Such large-scale deep learning systems have had a major effect in recent years in computer vision (11) and speech recognition (12), where they have yielded major improvements in performance over previous approaches (see Fig. 2). Deep network methods are being actively pursued in a variety of additional applications from natural language translation to collaborative filtering.

近年来，在监督学习中一个具有重大影响的领域涉及深层网络，它是阈值单元的多层网络，每个网络都计算其输入的一些简单参数化函数（9、10）。深度学习系统利用基于梯度的优化算法，基于输出处的误差来调整整个多层网络中的参数。利用现代并行计算结构，例如最初为视频游戏开发的图形处理单元，就可以构建包含数十亿个参数的深度学习系统，并且可以在网上对大量可用的图像，视频和语音样本进行训练。近年来，这样的大规模深度学习系统在计算机视觉（11）和语音识别（12）中产生了重大影响，与以前的方法相比，它们在性能方面产生了重大改进（见图2）。从自然语言翻译到协作过滤，各种其他应用程序都在积极地追求深度网络方法。

Fig. 2. Automatic generation of text captions for images with deep networks. A convolutional neural network is trained to interpret images, and its output is then used by a recurrent neural network trained to generate a text caption (top). The sequence at the bottom shows the word-by-word focus of the network on different parts of input image while it generates the caption word-by-word.

图2.自动生成具有深层网络图像的文本标题。卷积神经网络经过训练以解释图像，然后其输出被训练为生成文本标题的循环神经网络使用（顶部）。底部的序列显示了网络在输入图像的不同部分上的逐字聚焦，同时它逐字生成了标题。

The internal layers of deep networks can be viewed as providing learned representations of the input data. While much of the practical success in deep learning has come from supervised learning methods for discovering such representations, efforts have also been made to develop deep learning algorithms that discover useful representations of the input without the need for labeled training data (13). The general problem is referred to as unsupervised learning, a second paradigm in machine-learning research (2).

可以将深度网络的内部层视为提供输入数据学习表示形式。虽然深度学习的许多实际成功都来自用于发现此类表示形式的监督学习方法，但人们仍在努力开发深度学习算法，该算法可发现输入的有用表示形式，而无需标记的训练数据（13）。一般问题被称为无监督学习，这是机器学习研究中的第二个范式（2）。

Broadly, unsupervised learning generally involves the analysis of unlabeled data under assumptions about structural properties of the data (e.g., algebraic, combinatorial, or probabilistic). For example, one can assume that data lie on a low-dimensional manifold and aim to identify that manifold explicitly from data. Dimension reduction methods—including principal components analysis, manifold learning, factor analysis, random projections, and autoencoders (1, 2)—make different specific assumptions regarding the underlying manifold (e.g., that it is a linear subspace, a smooth nonlinear manifold, or a collection of submanifolds). Another example of dimension reduction is the topic modeling framework depicted in Fig. 3. A criterion function is defined that embodies these assumptions—often making use of general statistical principles such as maximum likelihood, the method of moments, or Bayesian integration—and optimization or sampling algorithms are developed to optimize the criterion. As another example, clustering is the problem of finding a partition of the observed data (and a rule for predicting future data) in the absence of explicit labels indicating a desired partition. A wide range of clustering procedures has been developed, all based on specific assumptions regarding the nature of a “cluster.” In both clustering and dimension reduction, the concern with computational complexity is paramount, given that the goal is to exploit the particularly large data sets that are available if one dispenses with supervised labels.

广义地讲，无监督学习通常涉及在有关数据结构特性的假设（例如代数，组合或概率）的前提下对未标记数据进行分析。例如，可以假设数据位于低维流形上，并旨在从数据中明确标识该流形。降维方法（包括主成分分析，流形学习，因子分析，随机投影和自动编码器（1、2））对底层流形做出了不同的特定假设（例如，它是线性子空间，平滑非线性流形或子流形的集合）。降维的另一个示例是图3中描述的主题建模框架。定义了一个标准函数来体现这些假设（通常利用一般的统计原理，例如最大似然，矩量法或贝叶斯积分）以及优化或开发了采样算法以优化标准。作为另一示例，聚类是在缺少指示期望分区的显式标签的情况下找到观测数据的分区（以及用于预测未来数据的规则）的问题。根据有关“集群”性质的特定假设，已经开发了各种各样的集群程序。在聚类和降维方面，考虑到目标是要利用特别大的数据集（如果人们放弃了监督标签的话），那么对计算复杂性的关注是至关重要的。

Fig. 3. Topic models. Topic modeling is a methodology for analyzing documents, where a document is viewed as a collection of words, and the words in the document are viewed as being generated by an underlying set of topics (denoted by the colors in the figure). Topics are probability distributions across words (leftmost column), and each document is characterized by a probability distribution across topics (histogram). These distributions are inferred based on the analysis of a collection of documents and can be viewed to classify, index, and summarize the content of documents.

图3.主题模型。主题建模是一种用于分析文档的方法，其中文档被视为单词的集合，文档中的单词被视为由基础主题集（由图中的颜色表示）生成。主题是单词之间的概率分布（最左列），每个文档的特征是主题之间的概率分布（直方图）。这些分布是根据对文档集合的分析得出的，可以查看这些分布以对文档的内容进行分类，索引和汇总。

A third major machine-learning paradigm is reinforcement learning (14, 15). Here, the information available in the training data is intermediate between supervised and unsupervised learning. Instead of training examples that indicate the correct output for a given input, the training data in reinforcement learning are assumed to provide only an indication as to whether an action is correct or not; if an action is incorrect, there remains the problem of finding the correct action. More generally, in the setting of sequences of inputs, it is assumed that reward signals refer to the entire sequence; the assignment of credit or blame to individual actions in the sequence is not directly provided. Indeed, although simplified versions of reinforcement learning known as bandit problems are studied, where it is assumed that rewards are provided after each action, reinforcement learning problems typically involve a general control-theoretic setting in which the learning task is to learn a control strategy (a “policy”) for an agent acting in an unknown dynamical environment, where that learned strategy is trained to chose actions for any given state, with the objective of maximizing its expected reward over time. The ties to research in control theory and operations research have increased over the years, with formulations such as Markov decision processes and partially observed Markov decision processes providing points of contact (15, 16). Reinforcement-learning algorithms generally make use of ideas that are familiar from the control-theory literature, such as policy iteration, value iteration, rollouts, and variance reduction, with innovations arising to address the specific needs of machine learning (e.g., largescale problems, few assumptions about the unknown dynamical environment, and the use of supervised learning architectures to represent policies). It is also worth noting the strong ties between reinforcement learning and many decades of work on learning in psychology and neuroscience, one notable example being the use of reinforcement learning algorithms to predict the response of dopaminergic neurons in monkeys learning to associate a stimulus light with subsequent sugar reward (17).

第三个主要的机器学习范式是强化学习（14，15）。在这里，训练数据中可用的信息是有监督学习和无监督学习之间的中间信息。强化学习中的训练数据不是指示给定输入的正确输出的训练示例，而是假设只提供一个动作是否正确的指示；如果一个动作不正确，则仍然存在找到正确动作的问题。更一般地说，在输入序列的设置中，假定奖励信号指的是整个序列；在序列中，对单个行为的信任或责备并不直接提供。事实上，虽然研究了强化学习的简化版本bandit问题，其中假设在每个动作之后提供奖励，但是强化学习问题通常涉及一个一般的控制理论设置，其中学习任务是为代理学习控制策略（“策略”）一种未知的动态环境，在这种环境中，学习到的策略被训练为针对任何给定状态选择动作，目标是随着时间的推移使其预期回报最大化。近年来，控制理论和运筹学研究的联系日益密切，马尔可夫决策过程和部分观察的马尔可夫决策过程等公式提供了联系点（15，16）。强化学习算法通常使用控制理论文献中熟悉的思想，如策略迭代、值迭代、展开和方差缩减，并通过创新来解决机器学习的特定需求（例如，大规模问题，很少假设未知的动态环境，使用有监督的学习架构来表示策略）。同样值得注意的是，强化学习与心理学和神经科学数十年的学习工作之间有着密切的联系，其中一个显著的例子是使用强化学习算法来预测猴子学习将刺激光与随后的糖奖赏联系起来的多巴胺能神经元的反应（17）。

Although these three learning paradigms help to organize ideas, much current research involves blends across these categories. For example, semisupervised learning makes use of unlabeled data to augment labeled data in a supervised learning context, and discriminative training blends architectures developed for unsupervised learning with optimization formulations that make use of labels. Model selection is the broad activity of using training data not only to fit a model but also to select from a family of models, and the fact that training data do not directly indicate which model to use leads to the use of algorithms developed for bandit problems and to Bayesian optimization procedures. Active learning arises when the learner is allowed to choose data points and query the trainer to request targeted information, such as the label of an otherwise unlabeled example. Causal modeling is the effort to go beyond simply discovering predictive relations among variables, to distinguish which variables causally influence others (e.g., a high white-blood-cell count can predict the existence of an infection, but it is the infection that causes the high white-cell count). Many issues influence the design of learning algorithms across all of these paradigms, including whether data are available in batches or arrive sequentially over time, how data have been sampled, requirements that learned models be interpretable by users, and robustness issues that arise when data do not fit prior modeling assumptions.

尽管这三种学习范式有助于组织思想，但当前许多研究涉及这些类别的融合。例如，半监督学习在监督学习的上下文中利用未标记的数据来增强标记的数据，而判别训练则将为无监督学习而开发的体系结构与利用标签的优化公式相结合。选择模型是一项广泛的活动，不仅使用训练数据来拟合模型，而且还可以从一系列模型中进行选择，并且训练数据没有直接指示要使用哪个模型，这一事实导致了针对bandit问题开发的算法和贝叶斯优化程序。当允许学习者选择数据点并查询训练者以请求有针对性的信息（例如未标记数据的标签）时，就会出现主动学习。因果建模努力去解决变量之间的预测关系，还可以区分哪些变量对其他变量有因果关系（例如，一个白细胞数量能够影响疾病的存在，但是是疾病导致了白细胞数量）。许多问题都会影响所有这些范式中学习算法的设计，包括数据是成批提供还是随时间顺序到达，数据是如何采样的，对用户可解释的学习模型的要求以及数据完成后不符合先前的建模假设出现的健壮性问题。

Emerging trends
The field of machine learning is sufficiently young that it is still rapidly expanding, often by inventing new formalizations of machine-learning problems driven by practical applications. (An example is the development of recommendation systems, as described in Fig. 4.) One major trend driving this expansion is a growing concern with the environment in which a machine-learning algorithm operates. The word “environment” here refers in part to the computing architecture; whereas a classical machine-learning system involved a single program running on a single machine, it is now common for machine-learning systems to be deployed in architectures that include many thousands or ten of thousands of processors, such that communication constraints and issues of parallelism and distributed processing take center stage. Indeed, as depicted in Fig. 5, machine-learning systems are increasingly taking the form of complex collections of software that run on large-scale parallel and distributed computing platforms and provide a range of algorithms and services to data analysts.

新趋势

机器学习领域还很年轻，以致它仍在迅速扩展，通常是通过发明由实际应用驱动的机器学习问题的新形式来实现的。（如图4所示是推荐系统的开发的例子。）推动这种发展的一个主要趋势是人们对机器学习算法运行环境的关注日益增长。这里的“环境”一词是指计算架构。传统的机器学习系统涉及在单个机器上运行的单个程序，但是现在，将机器学习系统部署在包含成千上万个处理器的体系结构中非常普遍，从而导致通信限制和并行性和分布式处理问题成为焦点。确实，如图5所示，机器学习系统越来越多地采用复杂软件集合的形式，这些软件在大规模并行和分布式计算平台上运行，并为数据分析人员提供一系列算法和服务。

Fig. 4. Recommendation systems. A recommendation system is a machine-learning system that is based on data that indicate links between a set of a users (e.g., people) and a set of items (e.g., products). A link between a user and a product means that the user has indicated an interest in the product in some fashion (perhaps by purchasing that item in the past).The machine-learning problem is to suggest other items to a given user that he or she may also be interested in, based on the data across all users.

图4.推荐系统。推荐系统是一种机器学习系统，其基于表明一组用户（例如，人）和一组项目（例如，产品）之间的联系的数据。用户与产品之间的联系意味着用户已经以某种方式（可能是过去购买该产品）表明对该产品感兴趣。机器学习问题是根据所有用户的数据向给定的用户建议他或她可能也会对此感兴趣。

Fig. 5. Data analytics stack. Scalable machine-learning systems are layered architectures that are built on parallel and distributed computing platforms. The architecture depicted here—an opensource data analysis stack developed in the Algorithms, Machines and People (AMP) Laboratory at the University of California, Berkeley—includes layers that interface to underlying operating systems; layers that provide distributed storage, data management, and processing; and layers that provide core machine-learning competencies such as streaming, subsampling, pipelines, graph processing, and model serving.

图5.数据分析堆栈。可扩展的机器学习系统是基于并行和分布式计算平台构建的分层体系结构。此处描述的架构是在加州大学伯克利分校的算法，机器和人（AMP）实验室开发的开源数据分析堆栈，其中包括与底层操作系统交互的层；提供分布式存储，数据管理和处理的层；以及提供核心机器学习能力（例如流，次采样，管道，图形处理和模型服务）的层。

The word “environment” also refers to the source of the data, which ranges from a set of people who may have privacy or ownership concerns, to the analyst or decision-maker who may have certain requirements on a machine-learning system (for example, that its output be visualizable), and to the social, legal, or political framework surrounding the deployment of a system. The environment also may include other machine-learning systems or other agents, and the overall collection of systems may be cooperative or adversarial. Broadly speaking, environments provide various resources to a learning algorithm and place constraints on those resources. Increasingly, machine-learning researchers are formalizing these relationships, aiming to design algorithms that are provably effective in various environments and explicitly allow users to express and control trade-offs among resources.

“环境”一词也指数据的来源，范围从可能有隐私或所有权问题的人到对机器学习系统可能有特定要求的分析师或决策者（可视化输出），以及围绕系统部署的社会，法律或政治框架。环境还可以包括其他机器学习系统或其他代理，并且系统的总体集合可以是合作的或对立的。广义上讲，环境为学习算法提供了各种资源，并对这些资源施加了约束。越来越多的机器学习研究人员将这些关系形式化，旨在设计在各种环境中证明有效的算法，并明确允许用户表达和控制资源之间的权衡。

As an example of resource constraints, let us suppose that the data are provided by a set of individuals who wish to retain a degree of privacy. Privacy can be formalized via the notion of “differential privacy,” which defines a probabilistic channel between the data and the outside world such that an observer of the output of the channel cannot infer reliably whether particular individuals have supplied data or not (18). Classical applications of differential privacy have involved insuring that queries (e.g., “what is the maximum balance across a set of accounts?”) to a privatized database return an answer that is close to that returned on the nonprivate data. Recent research has brought differential privacy into contact with machine learning, where queries involve predictions or other inferential assertions (e.g., “given the data I’ve seen so far, what is the probability that a new transaction is fraudulent?”) (19, 20). Placing the overall design of a privacy-enhancing machine-learning system within a decision-theoretic framework provides users with a tuning knob whereby they can choose a desired level of privacy that takes into account the kinds of questions that will be asked of the data and their own personal utility for the answers. For example, a person may be willing to reveal most of their genome in the context of research on a disease that runs in their family but may ask for more stringent protection if information about their genome is being used to set insurance rates.

作为资源约束的一个例子，让我们假设数据是由一组希望保留一定程度隐私的个人提供的。隐私可以通过“差异隐私”的概念来形式化，“差异隐私”定义了数据与外部世界之间的概率通道，使得通道输出的观察者无法可靠地推断出特定个人是否提供了数据（18）。差异隐私的经典应用涉及确保对私有化数据库的查询（例如“一组帐户中的最大余额是多少？”），返回的答案与对非私有数据返回的答案相近。最近的研究已将差异性隐私与机器学习联系起来，其中查询涉及预测或其他推断性断言（例如，“鉴于我到目前为止所看到的数据，新交易是欺诈的概率是多少？”）（19，20）。将隐私增强得机器学习系统的整体设计放在决策理论框架中，为用户提供了一个调节旋钮，使他们可以选择所需的隐私级别，并考虑到将要对数据提出的各种问题以及他们自己对答案的个人效用。例如，一个人可能愿意在对家庭中所患疾病的研究中揭示其大部分基因组，但如果有关其基因组的信息被用于设定保险费率，则可能会要求更严格的保护。

Communication is another resource that needs to be managed within the overall context of a distributed learning system. For example, data may be distributed across distinct physical locations because their size does not allow them to be aggregated at a single site or because of administrative boundaries. In such a setting, we may wish to impose a bit-rate communication constraint on the machine-learning algorithm. Solving the design problem under such a constraint will generally show how the performance of the learning system degrades under decrease in communication bandwidth, but it can also reveal how the performance improves as the number of distributed sites (e.g., machines or processors) increases, trading off these quantities against the amount of data (21, 22). Much as in classical information theory, this line of research aims at fundamental lower bounds on achievable performance and specific algorithms that achieve those lower bounds.

通讯是另一个需要在分布式学习系统的整体环境中进行管理的资源。例如，数据可能分布在不同的物理位置，因为它们的大小不允许它们在单个站点上聚合，或者由于管理边界。在这种情况下，我们可能希望对机器学习算法施加一个比特率通信约束。在这种约束下解决设计问题通常将显示出学习系统的性能如何在通信带宽降低的情况下降低，但也可以揭示随着分布式站点（例如机器或处理器）数量的增加，这些数量与数据量的交易性能如何提高（21，22）。就像在经典信息论中一样，这方面的研究主要针对可实现的性能的基本下限和实现这些下限的特定算法。

A major goal of this general line of research is to bring the kinds of statistical resources studied in machine learning (e.g., number of data points, dimension of a parameter, and complexity of a hypothesis class) into contact with the classical computational resources of time and space. Such a bridge is present in the “probably approximately correct” (PAC) learning framework, which studies the effect of adding a polynomial-time computation constraint on this relationship among error rates, training data size, and other parameters of the learning algorithm (3). Recent advances in this line of research include various lower bounds that establish fundamental gaps in performance achievable in certain machine-learning problems (e.g., sparse regression and sparse principal components analysis) via polynomial-time and exponential-time algorithms (23). The core of the problem, however, involves time-data tradeoffs that are far from the polynomial/exponential boundary. The large data sets that are increasingly the norm require algorithms whose time and space requirements are linear or sublinear in the problem size (number of data points or number of dimensions). Recent research focuses on methods such as subsampling, random projections, and algorithm weakening to achieve scalability while retaining statistical control (24, 25). The ultimate goal is to be able to supply time and space budgets to machine-learning systems in addition to accuracy requirements, with the system finding an operating point that allows such requirements to be realized.

这项研究的主要目标是使机器学习中研究的统计资源的种类（例如，数据点的数量，参数的维数和假设类的复杂性）与经典的时间和空间计算资源联系起来。这样的桥梁存在于“大概近似正确”（PAC）学习框架中，该框架研究了添加多项式时间计算约束对学习算法的误差率，训练数据大小和其他参数之间的关系上的影响（3）。该研究领域的最新进展包括各种下界，这些下界通过多项式时间和指数时间算法在某些机器学习问题（例如，稀疏回归和稀疏主成分分析）中可实现的性能上建立了根本性的差距（23）。然而，问题的核心涉及时间数据权衡远大于多项式/指数边界。越来越多的大型数据集要求算法的时间和空间在问题大小（数据点数或维数）方面是线性或次线性的。最近的研究集中在次采样，随机投影和算法弱化以实现可伸缩性同时保留统计控制的方法上（24，25）。最终目标是除了准确性要求外，还能够为机器学习系统提供时间和空间预算，并使系统找到一个可以实现此类要求的运行点。

Opportunities and challenges
Despite its practical and commercial successes, machine learning remains a young field with many underexplored research opportunities. Some of these opportunities can be seen by contrasting current machine-learning approaches to the types of learning we observe in naturally occurring systems such as humans and other animals, organizations, economies, and biological evolution. For example, whereas most machine-learning algorithms are targeted to learn one specific function or data model from one single data source, humans clearly learn many different skills and types of knowledge, from years of diverse training experience, supervised and unsupervised, in a simple-to-more-difficult sequence (e.g., learning to crawl, then walk, then run). This has led some researchers to begin exploring the question of how to construct computer lifelong or never-ending learners that operate nonstop for years, learning thousands of interrelated skills or functions within an overall architecture that allows the system to improve its ability to learn one skill based on having learned another (26–28). Another aspect of the analogy to natural learning systems suggests the idea of team-based, mixed-initiative learning. For example, whereas current machine-learning systems typically operate in isolation to analyze the given data, people often work in teams to collect and analyze data (e.g., biologists have worked as teams to collect and analyze genomic data, bringing together diverse experiments and perspectives to make progress on this difficult problem). New machine-learning methods capable of working collaboratively with humans to jointly analyze complex data sets might bring together the abilities of machines to tease out subtle statistical regularities from massive data sets with the abilities of humans to draw on diverse background knowledge to generate plausible explanations and suggest new hypotheses. Many theoretical results in machine learning apply to all learning systems, whether they are computer algorithms, animals, organizations, or natural evolution. As the field progresses, we may see machine-learning theory and algorithms increasingly providing models for understanding learning in neural systems, organizations, and biological evolution and see machine learning benefit from ongoing studies of these other types of learning systems.

机遇和挑战

尽管机器学习在实践和商业上取得了成功，但是它仍然是一个新的领域，有许多未开发的研究机会。通过将当前的机器学习方法与我们在自然发生的系统（例如人类和其他动物，组织，经济和生物进化）中观察到的学习类型进行对比，可以看到其中一些机会。例如，尽管大多数机器学习算法的目标是从一个单一的数据源中学习一种特定的函数或数据模型，但人类显然可以从多年的有监督和无监督的各种训练经验中学习许多不同的技能和知识类型，从一个简单到复杂的序列（例如，学习爬行，行走，然后奔跑）。这导致一些研究人员开始探索以下问题：如何构建计算机多年不间断运行的终身学习或永无止境的学习，在整个体系结构中学习数千种相互关联的技能或函数，使系统在学习另一种技能的基础上提高其学习一项技能的能力（26-28）。类比自然学习系统的另一个方面提出了基于团队的混合主动学习的思想。例如，尽管当前的机器学习系统通常独立运行以分析给定的数据，但是人们经常以团队的形式收集和分析数据（例如，生物学家作为团队收集和分析基因组数据，将各种实验和观点汇集在一起，以在这个难题上取得进展）。能够与人类合作以共同分析复杂数据集的新的机器学习方法可能将机器从大量数据集中提取出细微统计规律的能力与人类利用各种背景知识来产生合理解释和提出新的假设。机器学习的许多理论结果都适用于所有学习系统，无论它们是计算机算法，动物，组织还是自然进化。随着该领域的发展，我们可能会看到机器学习理论和算法越来越多地提供了用于理解神经系统，组织和生物进化中的学习的模型，并且看到机器学习将从这些其他类型的学习系统的持续研究中受益。

As with any powerful technology, machine learning raises questions about which of its potential uses society should encourage and discourage. The push in recent years to collect new kinds of personal data, motivated by its economic value, leads to obvious privacy issues, as mentioned above. The increasing value of data also raises a second ethical issue: Who will have access to, and ownership of, online data, and who will reap its benefits? Currently, much data are collected by corporations for specific uses leading to improved profits, with little or no motive for data sharing. However, the potential benefits that society could realize, even from existing online data, would be considerable if those data were to be made available for public good.

与任何强大的技术一样，机器学习引发了一个问题，即社会应鼓励和阻止哪些潜在用途。如上所述，近年来，由于其经济价值的原因，人们试图收集新型个人数据导致了明显的隐私问题。数据价值的增长还引发了第二个道德问题：谁将有权访问和拥有在线数据，谁将从中受益？当前，公司为特定用途收集了大量数据，从而提高了利润，而很少或没有共享数据的动机。但是，如果将这些数据提供给公众，社会甚至可以从现有的在线数据中实现的潜在利益将是巨大的。

To illustrate, consider one simple example of how society could benefit from data that is already online today by using this data to decrease the risk of global pandemic spread from infectious diseases. By combining location data from online sources (e.g., location data from cell phones, from credit-card transactions at retail outlets, and from security cameras in public places and private buildings) with online medical data (e.g., emergency room admissions), it would be feasible today to implement a simple system to telephone individuals immediately if a person they were in close contact with yesterday was just admitted to the emergency room with an infectious disease, alerting them to the symptoms they should watch for and precautions they should take. Here, there is clearly a tension and trade-off between personal privacy and public health, and society at large needs to make the decision on how to make this trade-off. The larger point of this example, however, is that, although the data are already online, we do not currently have the laws, customs, culture, or mechanisms to enable society to benefit from them, if it wishes to do so. In fact, much of these data are privately held and owned, even though they are data about each of us. Considerations such as these suggest that machine learning is likely to be one of the most transformative technologies of the 21st century. Although it is impossible to predict the future, it appears essential that society begin now to consider how to maximize its benefits.

为了说明这一点，请考虑一个简单的例子，这个例子说明了社会如何通过使用这些数据来降低全球传染病传播的风险，从而从中获得好处。通过将来自在线资源的位置数据（例如，来自手机的位置数据，来自零售店的信用卡交易以及来自公共场所和私人建筑物中的安全摄像头的地点数据）与在线医疗数据（例如，急诊室入院情况）相结合，如果一个昨天与别人密切联系的人刚被送进急诊室感染传染病，提醒他们应注意的症状和应采取的预防措施，那么今天实施一个简单的系统立即打电话给个人的做法是可行的。在这里，个人隐私与公共卫生之间显然存在紧张和权衡，整个社会都需要做出如何权衡的决定。但是，这个例子的最大意义是，尽管数据已经在线，但我们目前尚无法律，习俗，文化或机制来支持，只要社会愿意，它们就可以使社会从中受益。实际上，尽管这些数据是关于我们每个人的数据，但其中许多都是私人持有和拥有的。这些考虑表明，机器学习可能是21世纪最具变革性的技术之一。尽管无法预测未来，但似乎社会必须开始考虑如何最大限度地发挥其利益。

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