4. human learning
ai 学单词
ai 学单词AI学习单词AI(Artificial Intelligence,人工智能)是当今世界科技发展的热门话题之一。
随着人工智能技术的不断进步,越来越多的人开始关注并学习AI相关知识。
在学习AI的过程中,掌握相关的专业术语和单词是非常重要的。
本文将为大家介绍一些与AI学习相关的常用单词和词汇。
一、基础知识1. AI(Artificial Intelligence)- 人工智能:指由人类创造的一种智能系统,能够模拟人类的智能行为和思维方式。
2. Machine Learning(机器学习):一种人工智能的技术分支,通过使用统计学方法和算法,让机器通过大量数据进行学习,从而自动改善和优化性能。
3. Deep Learning(深度学习):机器学习的一种特殊形式,利用神经网络结构进行学习和模式识别。
4. Neural Network(神经网络):一种数学和计算机模型,模拟人类神经系统的学习和决策过程。
5. Algorithm(算法):一系列指令和规则,用于解决特定问题或完成特定任务的数学和逻辑操作。
6. Data Mining(数据挖掘):从大量的数据中发现和提取有意义的信息、模式和知识的过程。
二、相关技术1. Natural Language Processing(自然语言处理):研究如何让机器能够理解、分析和处理人类自然语言的技术。
2. Computer Vision(计算机视觉):指让机器能够“看”和“理解”图像和视觉信息的技术。
3. Robotics(机器人技术):研究和开发能够模拟和替代人类进行各种任务的自动化机器系统。
4. Reinforcement Learning(强化学习):一种机器学习的方法,通过试错和反馈机制来优化机器的行为和决策。
5. Big Data(大数据):指由于互联网和其他信息技术的发展所产生的海量数据。
6. Cloud Computing(云计算):一种基于网络的计算服务模式,利用互联网上的远程服务器实现对资源和应用的共享、存储和处理。
关于人工学习的挑战英语作文
关于人工学习的挑战英语作文Artificial learning is a rapidly evolving field that has captured the attention of researchers, technologists, and the general public alike. As the capabilities of machine learning algorithms and artificial intelligence (AI) continue to advance, the potential applications of these technologies have become increasingly diverse and far-reaching. From automated decision-making systems to natural language processing and computer vision, the impact of artificial learning is being felt across a wide range of industries and domains.However, the development of effective and reliable artificial learning systems is not without its challenges. In this essay, we will explore some of the key challenges and considerations that must be addressed in order to realize the full potential of artificial learning.One of the primary challenges in artificial learning is the issue of data quality and availability. Effective machine learning models require large, high-quality datasets to train on, and the acquisition and curation of such data can be a significant obstacle. In many cases, the necessary data may not exist or may be difficult to obtain due toprivacy concerns, technical limitations, or other factors. Additionally, the data that is available may be biased, incomplete, or of poor quality, which can lead to suboptimal model performance and unreliable results.Another challenge in artificial learning is the issue of interpretability and transparency. Many modern machine learning algorithms, particularly those based on deep neural networks, are often described as "black boxes" – their inner workings and decision-making processes are complex and difficult to understand, even for the researchers and engineers who develop them. This lack of interpretability can be problematic in applications where transparency and accountability are essential, such as in healthcare, finance, or legal decision-making.Furthermore, the development of artificial learning systems often requires significant computational resources and specialized expertise, which can present barriers to entry for smaller organizations or individuals. The high costs associated with hardware, software, and skilled personnel can make it challenging for some entities to invest in and deploy these technologies effectively.Another key challenge in artificial learning is the issue of safety and robustness. As these systems become more sophisticated and are deployed in real-world applications, it is crucial that they are able tooperate reliably and safely, even in the face of unexpected or adversarial inputs. Ensuring the security and resilience of artificial learning systems is an ongoing area of research and development, as researchers work to address issues such as adversarial attacks, model drift, and unexpected edge cases.Additionally, the ethical implications of artificial learning must be carefully considered. As these systems become more powerful and influential, there are concerns about the potential for bias, discrimination, and unintended consequences. Questions around the fairness, accountability, and transparency of artificial learning systems must be addressed to ensure that they are developed and deployed in a responsible and ethical manner.Finally, the integration of artificial learning into existing systems and workflows can also present significant challenges. Effectively incorporating these technologies into complex, real-world environments often requires careful planning, coordination, and change management, as organizations must adapt their processes, infrastructure, and human resources to accommodate the new capabilities and requirements of artificial learning.In conclusion, the challenges facing the development and deployment of effective and reliable artificial learning systems are multifaceted and complex. From data quality and availability tointerpretability, computational resources, safety, ethics, and integration, there are numerous hurdles that must be overcome in order to realize the full potential of these technologies.However, despite these challenges, the field of artificial learning continues to evolve and progress, with researchers, technologists, and policymakers working to address these issues and drive the development of more advanced and capable systems. As these efforts continue, it is likely that we will see increasingly sophisticated and impactful applications of artificial learning in a wide range of domains, transforming the way we work, live, and interact with the world around us.。
人工智能 国外经典课程
人工智能国外经典课程人工智能是当今科技领域的热门话题,国外有许多经典课程涵盖了人工智能的各个领域和技术。
下面我将列举一些国外经典的人工智能课程,这些课程涵盖了人工智能的基础理论、算法和应用等方面。
1. Stanford University - CS229: Machine Learning这门课程由斯坦福大学的吴恩达教授主讲,是机器学习领域的经典之作。
课程内容包括监督学习、无监督学习、强化学习等各种机器学习算法和方法。
2. Massachusetts Institute of Technology - 6.034: Artificial Intelligence这门课程由麻省理工学院的Patrick Henry Winston教授主讲,涵盖了人工智能的基础知识、推理和规划、感知和学习等方面。
课程通过讲解经典的人工智能方法和案例,帮助学生理解人工智能的核心概念和技术。
3. University of California, Berkeley - CS188: Introduction to Artificial Intelligence这门课程是加州大学伯克利分校的经典人工智能课程,内容包括搜索、规划、机器学习、自然语言处理等方面。
课程通过理论讲解和实践项目,培养学生的人工智能编程能力和解决实际问题的能力。
4. Carnegie Mellon University - 10-701: Introduction to这门课程由卡内基梅隆大学的Tom Mitchell教授主讲,介绍了机器学习的基本理论和算法。
课程内容包括统计学习理论、监督学习和无监督学习方法等,旨在帮助学生理解机器学习的原理和应用。
5. University of Washington - CSE 446: Machine Learning这门课程由华盛顿大学的Pedro Domingos教授主讲,涵盖了机器学习的基本概念、算法和应用。
人工智能技术的知识点整理
人工智能技术的知识点整理人工智能(Artificial Intelligence,简称AI)是近年来发展迅猛的一门技术领域,它致力于使计算机系统具备类似人类智能的功能和能力。
在AI技术的发展过程中,各种知识点相互交织,形成了庞大而复杂的知识网络。
本文将对人工智能技术的知识点进行整理和梳理,以便更好地理解和掌握这一领域。
一、机器学习(Machine Learning)机器学习是人工智能领域的重要基石,它关注计算机系统如何通过经验学习来改善性能。
在机器学习中,主要有以下几个重要知识点:1. 监督学习(Supervised Learning):通过给定输入和对应的输出样本训练模型,从而使其能够预测未知输入的输出。
2. 无监督学习(Unsupervised Learning):通过从输入样本中发现模式和结构,从而提取隐藏的信息和知识。
3. 强化学习(Reinforcement Learning):通过与环境交互,通过奖励和惩罚的机制来学习最优决策策略。
4. 深度学习(Deep Learning):通过模仿人脑神经网络的结构和工作方式,实现复杂的模式识别和决策。
二、自然语言处理(Natural Language Processing)自然语言处理是AI技术中与人类语言相关的领域,主要研究计算机如何理解和处理人类的自然语言。
以下是自然语言处理的几个重点知识点:1. 词法分析(Lexical Analysis):将自然语言的连续字符序列切分成有意义的词汇单位,例如分词、词性标注等。
2. 句法分析(Syntactic Analysis):研究语言中词汇之间的关系,例如依存关系、语法结构等。
3. 语义分析(Semantic Analysis):理解自然语言句子的意义,例如命名实体识别、意图识别等。
4. 机器翻译(Machine Translation):将一种自然语言转化成另一种自然语言的技术。
三、计算机视觉(Computer Vision)计算机视觉是研究如何使计算机通过摄像头或相似的设备感知和理解图像或视频的过程。
Human-level concept learning through probabilistic program induction
new concept, and even children can make meaningful generalizations via “one-shot learning” (1–3). In contrast, many of the leading approaches in machine learning are also the most data-hungry, especially “deep learning” models that have achieved new levels of performance on object and speech recognition benchmarks (4–9). Second, people learn richer representations than machines do, even for simple concepts (Fig. 1B), using them for a wider range of functions, including (Fig. 1, ii) creating new exemplars (10), (Fig. 1, iii) parsing objects into parts and relations (11), and (Fig. 1, iv) creating new abstract categories of objects based on existing categories (12, 13). In contrast, the best machine classifiers do not perform these additional functions, which are rarely studied and usually require specialized algorithms. A central challenge is to explain these two aspects of human-level concept learning: How do people learn new concepts from just one or a few examples? And how do people learn such abstract, rich, and flexible representations? An even greater challenge arises when putting them together: How can learning succeed from such sparse data yet also produce such rich representations? For any theory of
人工智能常见名词解释
人工智能常见名词解释人工智能(Artificial Intelligence,AI)是计算机科学的一个分支,旨在使机器能够模拟和执行人类智能活动。
随着科技的不断进步,人工智能已经成为当今社会发展的热门话题。
本文将对人工智能领域中的一些常见名词进行解释,旨在帮助读者更好地理解人工智能及其相关技术。
1. 机器学习(Machine Learning,ML)机器学习是人工智能的一个重要分支,旨在让机器通过数据和自动化算法提高性能,从经验中学习和改进。
通过对大量数据进行训练和学习,机器能够自动分析和识别模式,并根据这些模式做出预测或决策。
机器学习在各个领域都有广泛应用,例如医疗诊断、金融风险评估和自动驾驶等。
2. 深度学习(Deep Learning)深度学习是机器学习中的一种技术,通过建立多层神经网络来模拟人脑的工作原理。
与传统的机器学习算法相比,深度学习可以自动从数据中提取特征,并进行高效的分类和预测。
深度学习在语音识别、图像识别和自然语言处理等领域取得了显著的成果,推动了人工智能技术的发展。
3. 自然语言处理(Natural Language Processing,NLP)自然语言处理是研究计算机与人类自然语言之间交互的一门学科。
通过自然语言处理技术,计算机可以理解和生成人类的语言,并进行语义分析、情感识别和机器翻译等任务。
自然语言处理在智能助理、在线客服和机器翻译等应用中得到广泛应用。
4. 机器视觉(Computer Vision)机器视觉是一种使用数字图像处理和模式识别技术,使计算机能够“看”和理解图像和视频的能力。
机器视觉可以用于目标检测、图像分类、人脸识别和行为分析等任务。
它在无人驾驶、安防监控和医学影像诊断等领域有着重要的应用。
5. 增强学习(Reinforcement Learning)增强学习是一种通过试错学习和奖励机制来使机器智能化的方法。
在增强学习中,机器通过与环境的交互来学习行为策略,并通过奖励或惩罚来调整策略的优劣。
人工智能 术语
人工智能术语人工智能术语人工智能(Artificial Intelligence,简称AI)是一种模拟人类智能行为的技术和方法。
它通过模拟人类的思维能力,使机器能够像人一样进行学习、推理、决策和解决问题。
以下是一些常见的人工智能术语。
1. 机器学习(Machine Learning):机器学习是一种基于数据和模型的算法,通过分析和处理大量的数据来训练机器,使其能够自动地识别模式和规律,并做出相应的决策和预测。
2. 深度学习(Deep Learning):深度学习是机器学习的一种特殊形式,其模型由多个神经网络层组成。
深度学习通过多层次的非线性变换,能够对复杂的数据进行更准确的建模和分析。
3. 神经网络(Neural Network):神经网络是一种模拟人脑神经元结构和功能的数学模型。
它由多个节点和连接组成,通过输入数据和权重的计算,进行信息传递和处理。
4. 自然语言处理(Natural Language Processing,简称NLP):自然语言处理是研究人类语言的一门学科,旨在使计算机能够理解、分析和生成自然语言。
NLP在机器翻译、语义分析等领域有广泛应用。
5. 计算机视觉(Computer Vision):计算机视觉是使计算机能够理解和解释图像和视频的技术。
它包括图像识别、目标检测、图像生成等任务,广泛应用于人脸识别、无人驾驶等领域。
6. 强化学习(Reinforcement Learning):强化学习是一种通过试错和反馈来训练智能体的学习方法。
智能体根据环境的反馈,不断调整自己的行为,以达到最优的目标。
7. 数据挖掘(Data Mining):数据挖掘是从大量数据中发现模式和知识的过程。
通过机器学习和统计分析等技术,数据挖掘可以帮助人们发现隐藏在数据中的规律和趋势。
8. 自动驾驶(Autonomous Driving):自动驾驶是利用人工智能技术使汽车能够在没有人类驾驶的情况下自动行驶的技术。
人工智能英汉
人工智能英汉Aβα-Pruning, βα-剪枝, (2) Acceleration Coefficient, 加速系数, (8) Activation Function, 激活函数, (4) Adaptive Linear Neuron, 自适应线性神经元,(4)Adenine, 腺嘌呤, (11)Agent, 智能体, (6)Agent Communication Language, 智能体通信语言, (11)Agent-Oriented Programming, 面向智能体的程序设计, (6)Agglomerative Hierarchical Clustering, 凝聚层次聚类, (5)Analogism, 类比推理, (5)And/Or Graph, 与或图, (2)Ant Colony Optimization (ACO), 蚁群优化算法, (8)Ant Colony System (ACS), 蚁群系统, (8) Ant-Cycle Model, 蚁周模型, (8)Ant-Density Model, 蚁密模型, (8)Ant-Quantity Model, 蚁量模型, (8)Ant Systems, 蚂蚁系统, (8)Applied Artificial Intelligence, 应用人工智能, (1)Approximate Nondeterministic Tree Search (ANTS), 近似非确定树搜索, (8) Artificial Ant, 人工蚂蚁, (8)Artificial Intelligence (AI), 人工智能, (1) Artificial Neural Network (ANN), 人工神经网络, (1), (3)Artificial Neural System, 人工神经系统,(3) Artificial Neuron, 人工神经元, (3) Associative Memory, 联想记忆, (4) Asynchronous Mode, 异步模式, (4) Attractor, 吸引子, (4)Automatic Theorem Proving, 自动定理证明,(1)Automatic Programming, 自动程序设计, (1) Average Reward, 平均收益, (6) Axon, 轴突, (4)Axon Hillock, 轴突丘, (4)BBackward Chain Reasoning, 逆向推理, (3) Bayesian Belief Network, 贝叶斯信念网, (5) Bayesian Decision, 贝叶斯决策, (3) Bayesian Learning, 贝叶斯学习, (5) Bayesian Network贝叶斯网, (5)Bayesian Rule, 贝叶斯规则, (3)Bayesian Statistics, 贝叶斯统计学, (3) Biconditional, 双条件, (3)Bi-Directional Reasoning, 双向推理, (3) Biological Neuron, 生物神经元, (4) Biological Neural System, 生物神经系统, (4) Blackboard System, 黑板系统, (8)Blind Search, 盲目搜索, (2)Boltzmann Machine, 波尔兹曼机, (3) Boltzmann-Gibbs Distribution, 波尔兹曼-吉布斯分布, (3)Bottom-Up, 自下而上, (4)Building Block Hypotheses, 构造块假说, (7) CCell Body, 细胞体, (3)Cell Membrane, 细胞膜, (3)Cell Nucleus, 细胞核, (3)Certainty Factor, 可信度, (3)Child Machine, 婴儿机器, (1)Chinese Room, 中文屋, (1) Chromosome, 染色体, (6)Class-conditional Probability, 类条件概率,(3), (5)Classifier System, 分类系统, (6)Clause, 子句, (3)Cluster, 簇, (5)Clustering Analysis, 聚类分析, (5) Cognitive Science, 认知科学, (1) Combination Function, 整合函数, (4) Combinatorial Optimization, 组合优化, (2) Competitive Learning, 竞争学习, (4) Complementary Base, 互补碱基, (11) Computer Games, 计算机博弈, (1) Computer Vision, 计算机视觉, (1)Conflict Resolution, 冲突消解, (3) Conjunction, 合取, (3)Conjunctive Normal Form (CNF), 合取范式,(3)Collapse, 坍缩, (11)Connectionism, 连接主义, (3) Connective, 连接词, (3)Content Addressable Memory, 联想记忆, (4) Control Policy, 控制策略, (6)Crossover, 交叉, (7)Cytosine, 胞嘧啶, (11)DData Mining, 数据挖掘, (1)Decision Tree, 决策树, (5) Decoherence, 消相干, (11)Deduction, 演绎, (3)Default Reasoning, 默认推理(缺省推理),(3)Defining Length, 定义长度, (7)Rule (Delta Rule), 德尔塔规则, 18(3) Deliberative Agent, 慎思型智能体, (6) Dempster-Shafer Theory, 证据理论, (3) Dendrites, 树突, (4)Deoxyribonucleic Acid (DNA), 脱氧核糖核酸, (6), (11)Disjunction, 析取, (3)Distributed Artificial Intelligence (DAI), 分布式人工智能, (1)Distributed Expert Systems, 分布式专家系统,(9)Divisive Hierarchical Clustering, 分裂层次聚类, (5)DNA Computer, DNA计算机, (11)DNA Computing, DNA计算, (11) Discounted Cumulative Reward, 累计折扣收益, (6)Domain Expert, 领域专家, (10) Dominance Operation, 显性操作, (7) Double Helix, 双螺旋结构, (11)Dynamical Network, 动态网络, (3)E8-Puzzle Problem, 八数码问题, (2) Eletro-Optical Hybrid Computer, 光电混合机, (11)Elitist strategy for ant systems (EAS), 精化蚂蚁系统, (8)Energy Function, 能量函数, (3) Entailment, 永真蕴含, (3) Entanglement, 纠缠, (11)Entropy, 熵, (5)Equivalence, 等价式, (3)Error Back-Propagation, 误差反向传播, (4) Evaluation Function, 评估函数, (6) Evidence Theory, 证据理论, (3) Evolution, 进化, (7)Evolution Strategies (ES), 进化策略, (7) Evolutionary Algorithms (EA), 进化算法, (7) Evolutionary Computation (EC), 进化计算,(7)Evolutionary Programming (EP), 进化规划,(7)Existential Quantification, 存在量词, (3) Expert System, 专家系统, (1)Expert System Shell, 专家系统外壳, (9) Explanation-Based Learning, 解释学习, (5) Explanation Facility, 解释机构, (9)FFactoring, 因子分解, (11)Feedback Network, 反馈型网络, (4) Feedforward Network, 前馈型网络, (1) Feasible Solution, 可行解, (2)Finite Horizon Reward, 横向有限收益, (6) First-order Logic, 一阶谓词逻辑, (3) Fitness, 适应度, (7)Forward Chain Reasoning, 正向推理, (3) Frame Problem, 框架问题, (1)Framework Theory, 框架理论, (3)Free-Space Optical Interconnect, 自由空间光互连, (11)Fuzziness, 模糊性, (3)Fuzzy Logic, 模糊逻辑, (3)Fuzzy Reasoning, 模糊推理, (3)Fuzzy Relation, 模糊关系, (3)Fuzzy Set, 模糊集, (3)GGame Theory, 博弈论, (8)Gene, 基因, (7)Generation, 代, (6)Genetic Algorithms, 遗传算法, (7)Genetic Programming, 遗传规划(遗传编程),(7)Global Search, 全局搜索, (2)Gradient Descent, 梯度下降, (4)Graph Search, 图搜索, (2)Group Rationality, 群体理性, (8) Guanine, 鸟嘌呤, (11)HHanoi Problem, 梵塔问题, (2)Hebbrian Learning, 赫伯学习, (4)Heuristic Information, 启发式信息, (2) Heuristic Search, 启发式搜索, (2)Hidden Layer, 隐含层, (4)Hierarchical Clustering, 层次聚类, (5) Holographic Memory, 全息存储, (11) Hopfield Network, 霍普菲尔德网络, (4) Hybrid Agent, 混合型智能体, (6)Hype-Cube Framework, 超立方体框架, (8)IImplication, 蕴含, (3)Implicit Parallelism, 隐并行性, (7) Individual, 个体, (6)Individual Rationality, 个体理性, (8) Induction, 归纳, (3)Inductive Learning, 归纳学习, (5) Inference Engine, 推理机, (9)Information Gain, 信息增益, (3)Input Layer, 输入层, (4)Interpolation, 插值, (4)Intelligence, 智能, (1)Intelligent Control, 智能控制, (1) Intelligent Decision Supporting System (IDSS), 智能决策支持系统,(1) Inversion Operation, 倒位操作, (7)JJoint Probability Distribution, 联合概率分布,(5) KK-means, K-均值, (5)K-medoids, K-中心点, (3)Knowledge, 知识, (3)Knowledge Acquisition, 知识获取, (9) Knowledge Base, 知识库, (9)Knowledge Discovery, 知识发现, (1) Knowledge Engineering, 知识工程, (1) Knowledge Engineer, 知识工程师, (9) Knowledge Engineering Language, 知识工程语言, (9)Knowledge Interchange Format (KIF), 知识交换格式, (8)Knowledge Query and ManipulationLanguage (KQML), 知识查询与操纵语言,(8)Knowledge Representation, 知识表示, (3)LLearning, 学习, (3)Learning by Analog, 类比学习, (5) Learning Factor, 学习因子, (8)Learning from Instruction, 指导式学习, (5) Learning Rate, 学习率, (6)Least Mean Squared (LSM), 最小均方误差,(4)Linear Function, 线性函数, (3)List Processing Language (LISP), 表处理语言, (10)Literal, 文字, (3)Local Search, 局部搜索, (2)Logic, 逻辑, (3)Lyapunov Theorem, 李亚普罗夫定理, (4) Lyapunov Function, 李亚普罗夫函数, (4)MMachine Learning, 机器学习, (1), (5) Markov Decision Process (MDP), 马尔科夫决策过程, (6)Markov Chain Model, 马尔科夫链模型, (7) Maximum A Posteriori (MAP), 极大后验概率估计, (5)Maxmin Search, 极大极小搜索, (2)MAX-MIN Ant Systems (MMAS), 最大最小蚂蚁系统, (8)Membership, 隶属度, (3)Membership Function, 隶属函数, (3) Metaheuristic Search, 元启发式搜索, (2) Metagame Theory, 元博弈理论, (8) Mexican Hat Function, 墨西哥草帽函数, (4) Migration Operation, 迁移操作, (7) Minimum Description Length (MDL), 最小描述长度, (5)Minimum Squared Error (MSE), 最小二乘法,(4)Mobile Agent, 移动智能体, (6)Model-based Methods, 基于模型的方法, (6) Model-free Methods, 模型无关方法, (6) Modern Heuristic Search, 现代启发式搜索,(2)Monotonic Reasoning, 单调推理, (3)Most General Unification (MGU), 最一般合一, (3)Multi-Agent Systems, 多智能体系统, (8) Multi-Layer Perceptron, 多层感知器, (4) Mutation, 突变, (6)Myelin Sheath, 髓鞘, (4)(μ+1)-ES, (μ+1) -进化规划, (7)(μ+λ)-ES, (μ+λ) -进化规划, (7) (μ,λ)-ES, (μ,λ) -进化规划, (7)NNaïve Bayesian Classifiers, 朴素贝叶斯分类器, (5)Natural Deduction, 自然演绎推理, (3) Natural Language Processing, 自然语言处理,(1)Negation, 否定, (3)Network Architecture, 网络结构, (6)Neural Cell, 神经细胞, (4)Neural Optimization, 神经优化, (4) Neuron, 神经元, (4)Neuron Computing, 神经计算, (4)Neuron Computation, 神经计算, (4)Neuron Computer, 神经计算机, (4) Niche Operation, 生态操作, (7) Nitrogenous base, 碱基, (11)Non-Linear Dynamical System, 非线性动力系统, (4)Non-Monotonic Reasoning, 非单调推理, (3) Nouvelle Artificial Intelligence, 行为智能,(6)OOccam’s Razor, 奥坎姆剃刀, (5)(1+1)-ES, (1+1) -进化规划, (7)Optical Computation, 光计算, (11)Optical Computing, 光计算, (11)Optical Computer, 光计算机, (11)Optical Fiber, 光纤, (11)Optical Waveguide, 光波导, (11)Optical Interconnect, 光互连, (11) Optimization, 优化, (2)Optimal Solution, 最优解, (2)Orthogonal Sum, 正交和, (3)Output Layer, 输出层, (4)Outer Product, 外积法, 23(4)PPanmictic Recombination, 混杂重组, (7) Particle, 粒子, (8)Particle Swarm, 粒子群, (8)Particle Swarm Optimization (PSO), 粒子群优化算法, (8)Partition Clustering, 划分聚类, (5) Partitioning Around Medoids, K-中心点, (3) Pattern Recognition, 模式识别, (1) Perceptron, 感知器, (4)Pheromone, 信息素, (8)Physical Symbol System Hypothesis, 物理符号系统假设, (1)Plausibility Function, 不可驳斥函数(似然函数), (3)Population, 物种群体, (6)Posterior Probability, 后验概率, (3)Priori Probability, 先验概率, (3), (5) Probability, 随机性, (3)Probabilistic Reasoning, 概率推理, (3) Probability Assignment Function, 概率分配函数, (3)Problem Solving, 问题求解, (2)Problem Reduction, 问题归约, (2)Problem Decomposition, 问题分解, (2) Problem Transformation, 问题变换, (2) Product Rule, 产生式规则, (3)Product System, 产生式系统, (3) Programming in Logic (PROLOG), 逻辑编程, (10)Proposition, 命题, (3)Propositional Logic, 命题逻辑, (3)Pure Optical Computer, 全光计算机, (11)QQ-Function, Q-函数, (6)Q-learning, Q-学习, (6)Quantifier, 量词, (3)Quantum Circuit, 量子电路, (11)Quantum Fourier Transform, 量子傅立叶变换, (11)Quantum Gate, 量子门, (11)Quantum Mechanics, 量子力学, (11) Quantum Parallelism, 量子并行性, (11) Qubit, 量子比特, (11)RRadial Basis Function (RBF), 径向基函数,(4)Rank based ant systems (ASrank), 基于排列的蚂蚁系统, (8)Reactive Agent, 反应型智能体, (6) Recombination, 重组, (6)Recurrent Network, 循环网络, (3) Reinforcement Learning, 强化学习, (3) Resolution, 归结, (3)Resolution Proof, 归结反演, (3) Resolution Strategy, 归结策略, (3) Reasoning, 推理, (3)Reward Function, 奖励函数, (6) Robotics, 机器人学, (1)Rote Learning, 机械式学习, (5)SSchema Theorem, 模板定理, (6) Search, 搜索, (2)Selection, 选择, (7)Self-organizing Maps, 自组织特征映射, (4) Semantic Network, 语义网络, (3)Sexual Differentiation, 性别区分, (7) Shor’s algorithm, 绍尔算法, (11)Sigmoid Function, Sigmoid 函数(S型函数),(4)Signal Function, 信号函数, (3)Situated Artificial Intelligence, 现场式人工智能, (1)Spatial Light Modulator (SLM), 空间光调制器, (11)Speech Act Theory, 言语行为理论, (8) Stable State, 稳定状态, (4)Stability Analysis, 稳定性分析, (4)State Space, 状态空间, (2)State Transfer Function, 状态转移函数,(6)Substitution, 置换, (3)Stochastic Learning, 随机型学习, (4) Strong Artificial Intelligence (AI), 强人工智能, (1)Subsumption Architecture, 包容结构, (6) Superposition, 叠加, (11)Supervised Learning, 监督学习, (4), (5) Swarm Intelligence, 群智能, (8)Symbolic Artificial Intelligence (AI), 符号式人工智能(符号主义), (3) Synapse, 突触, (4)Synaptic Terminals, 突触末梢, (4) Synchronous Mode, 同步模式, (4)TThreshold, 阈值, (4)Threshold Function, 阈值函数, (4) Thymine, 胸腺嘧啶, (11)Topological Structure, 拓扑结构, (4)Top-Down, 自上而下, (4)Transfer Function, 转移函数, (4)Travel Salesman Problem, 旅行商问题, (4) Turing Test, 图灵测试, (1)UUncertain Reasoning, 不确定性推理, (3)Uncertainty, 不确定性, (3)Unification, 合一, (3)Universal Quantification, 全称量词, (4) Unsupervised Learning, 非监督学习, (4), (5)WWeak Artificial Intelligence (Weak AI), 弱人工智能, (1)Weight, 权值, (4)Widrow-Hoff Rule, 维德诺-霍夫规则, (4)。
Human Learning
Behaviorism
Strong points: emphasis on practice and behavior, special attention to reinforcers(negative attitude towards errors), impact on teaching and learning Weak points: experiments mainly on animals, no attention to cognitive and affective elements of human beings
Operant Conditioning
Concept 1: Consequences/Reinforcers:the events or stimuli that follow a response and that tend to strengthen behavior or increase the probability of a recurrence of that response and that constitute a powerful force in the control of human behavior. (response ➝ consequences)
Operant Conditioning
Concept 2: Operant: sets of responses that are emitted and governed by the consequences they produce. Respondent: sets of responses that are elicited by identifiable stimuli.
人工智能英文词汇
人工智能英文词汇Artificial Intelligence VocabularyIntroduction:Artificial intelligence (AI) has emerged as a transformative technology, revolutionizing various sectors globally. With its increasing importance, understanding and becoming familiar with the relevant vocabulary is essential. In this article, we will explore a comprehensive list of commonly used English terms related to artificial intelligence.1. Machine Learning:Machine learning is a branch of AI that focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions without explicit programming. It involves the use of training data to build models that can generalize and make accurate predictions on new, unseen data.2. Deep Learning:Deep learning is a subset of machine learning that utilizes artificial neural networks and large-scale computational resources to analyze vast amounts of data. It enables the system to automatically learn and extract complex patterns or features from the data, similar to how the human brain functions.3. Neural Network:A neural network is a network of artificial neurons or nodes that are interconnected in layers. It is designed to mimic human neural networks andprocess complex information. Neural networks play a crucial role in deep learning algorithms, enabling the development of highly accurate predictive models.4. Natural Language Processing (NLP):Natural Language Processing is a subfield of AI that focuses on the interaction between computers and humans through natural language. It involves tasks such as speech recognition, language understanding, and machine translation. NLP enables computers to understand and generate human language, facilitating communication and information processing.5. Computer Vision:Computer vision involves the use of AI and image processing techniques to enable computers to interpret and analyze visual information. It encompasses tasks such as object recognition, image classification, and image generation. Computer vision finds applications in areas like autonomous vehicles, medical imaging, and surveillance systems.6. Robotics:Robotics involves the design, construction, programming, and operation of robots. AI plays a vital role in robotics by enabling autonomous decision-making, learning, and adaptation. Robotics combines various technologies, including AI, to develop intelligent machines that can interact with the physical world and perform human-like tasks.7. Big Data:Big data refers to the massive volume of structured and unstructured data that is generated at an unprecedented rate. AI technologies like machine learning and deep learning can analyze big data to extract meaningful insights, patterns, and trends. The integration of big data and AI has opened up new opportunities and possibilities across industries.8. Algorithm:An algorithm is a step-by-step procedure or set of rules designed to solve a specific problem or perform a particular task. In the context of AI, algorithms are responsible for processing and analyzing data, training machine learning models, and making predictions or decisions. Well-designed algorithms are crucial for achieving accurate and efficient AI systems.9. Predictive Analytics:Predictive analytics involves utilizing historical and current data to forecast future outcomes or trends. AI techniques, such as machine learning, are often used in predictive analytics to analyze large datasets, identify patterns, and make accurate predictions. Predictive analytics finds applications in various domains, including marketing, finance, and healthcare.10. Virtual Assistant:A virtual assistant is an AI-powered software that can perform tasks or services for individuals. It uses natural language processing and speech recognition to understand and respond to users' voice commands or textinputs. Virtual assistants, such as Siri, Alexa, and Google Assistant, have become increasingly popular, enhancing productivity and convenience.Conclusion:As AI continues to evolve and shape our world, having a good understanding of the associated vocabulary is essential. In this article, we have delved into some of the key terms related to artificial intelligence. By familiarizing ourselves with these terms, we can stay informed and effectively engage in discussions and developments within the AI domain.。
人工智能专用名词
人工智能专用名词1. 机器学习 (Machine Learning)2. 深度学习 (Deep Learning)3. 神经网络 (Neural Network)4. 自然语言处理 (Natural Language Processing)5. 计算机视觉 (Computer Vision)6. 强化学习 (Reinforcement Learning)7. 数据挖掘 (Data Mining)8. 数据预处理 (Data Preprocessing)9. 特征工程 (Feature Engineering)10. 模型训练 (Model Training)11. 模型评估 (Model Evaluation)12. 监督学习 (Supervised Learning)13. 无监督学习 (Unsupervised Learning)14. 半监督学习 (Semi-Supervised Learning)15. 迁移学习 (Transfer Learning)16. 生成对抗网络 (Generative Adversarial Networks, GANs)17. 强化学习 (Reinforcement Learning)18. 聚类 (Clustering)19. 分类 (Classification)20. 回归 (Regression)21. 泛化能力 (Generalization)22. 正则化 (Regularization)23. 自动编码器 (Autoencoder)24. 支持向量机 (Support Vector Machine, SVM)25. 随机森林 (Random Forest)26. 梯度下降 (Gradient Descent)27. 前向传播 (Forward Propagation)28. 反向传播 (Backpropagation)29. 混淆矩阵 (Confusion Matrix)30. ROC曲线 (Receiver Operating Characteristic Curve, ROC Curve)31. AUC指标 (Area Under Curve, AUC)32. 噪声 (Noise)33. 过拟合 (Overfitting)34. 欠拟合 (Underfitting)35. 超参数 (Hyperparameters)36. 网格搜索 (Grid Search)37. 交叉验证 (Cross Validation)38. 降维 (Dimensionality Reduction)39. 卷积神经网络 (Convolutional Neural Network, CNN)40. 循环神经网络 (Recurrent Neural Network, RNN)。
人工智能专业词汇
人工智能专业词汇1. 机器学习 (Machine Learning): 通过让计算机从数据中学习和改善性能的技术。
2. 深度学习 (Deep Learning): 一种通过构建多层神经网络来模拟人脑神经元结构和处理方式的机器学习方法。
3. 自然语言处理 (Natural Language Processing, NLP): 用于理解和处理人类语言的技术。
4. 机器视觉 (Computer Vision): 计算机处理和解释图像和视频的能力。
5. 数据挖掘 (Data Mining): 从大量数据中发现未知的模式、关系和趋势的过程。
6. 强化学习 (Reinforcement Learning): 通过试错和反馈的方式让机器自主学习和改善性能的方法。
7. 神经网络 (Neural Network): 由多个人工神经元组成的计算模型,用于模拟人脑神经网络的工作方式。
8. 感知 (Perception): 机器通过传感器和处理算法获取并理解环境的能力。
9. 自主决策 (Autonomous Decision Making): 让机器通过学习和分析情况来做出决策的能力。
10. 数据预处理 (Data Preprocessing): 对原始数据进行清洗、转换和规范化的过程,以提高机器学习算法的性能。
11. 模型评估 (Model Evaluation): 使用测试数据来评估机器学习模型的性能和准确度。
12. 聚类 (Clustering): 将数据根据相似性进行分组的过程,用于发现数据集中的隐藏模式。
13. 分类 (Classification): 将数据分为预定义类别的过程,用于预测未知数据的分类。
14. 回归 (Regression): 建立模型来预测连续变量的过程,用于分析变量之间的关系。
15. 优化算法 (Optimization Algorithms): 用于优化模型参数和损失函数的算法,以提高模型性能。
人工智能可以帮助人类学习英语作文
人工智能可以帮助人类学习英语作文 English Answer:Artificial intelligence (AI) has the potential to revolutionize the way we learn English writing. Here are some of the ways AI can be used to help students improve their writing skills:1. AI can provide personalized feedback. AI-powered writing assistants can analyze a student's writing and provide feedback on grammar, vocabulary, and style. This feedback can help students identify areas where they need to improve, and it can also help them develop a more confident writing voice.2. AI can generate writing prompts. AI can be used to generate writing prompts that are tailored to a student's interests and skill level. This can help students get started on writing assignments and it can also help them explore new topics that they might not have otherwiseconsidered.3. AI can help students learn new words and phrases.AI-powered language learning apps can help students learn new words and phrases in a fun and engaging way. These apps can use games, quizzes, and other interactive activities to help students build their vocabulary.4. AI can help students practice writing in different styles. AI-powered writing assistants can help students practice writing in different styles, such as academic writing, business writing, and creative writing. This can help students develop the flexibility to write in a variety of contexts.5. AI can help students get feedback from other students. AI-powered peer review platforms can help students get feedback on their writing from other students. This feedback can help students identify areas where they can improve their writing, and it can also help them develop a better understanding of the writing process.Overall, AI has the potential to be a powerful tool for learning English writing. AI-powered writing assistants can provide personalized feedback, generate writing prompts, help students learn new words and phrases, practice writing in different styles, and get feedback from other students. As AI technology continues to develop, we can expect to see even more innovative ways to use AI to help students learn English writing.中文回答:人工智能(AI)有可能彻底改变我们学习英语写作的方式。
关于劳动的名言英文版加中文
关于劳动的名言英文版加中文1.与劳动有关的名言英语的bour breeds our body, learning breads our soul.劳动教养了身体,学习教养了心灵。
----史密斯2. He that will not to work shall not eat.不劳动者不得食。
bor vanquishes all. 劳动征服一切。
---- 维吉尔4.Work is the true source of human welfare. 劳动是人类的幸福之源。
5. Work is the glorious duty of every able-bodied citizen. 劳动是一切具有劳动能力公民的光荣职责。
2.英语劳动格言英语勤劳与劳动民间谚语,不多说了都是经典的英语谚语1.Few words,many deeds. 少说话,多做事。
2.no song,no supper. 不劳无获。
3.practice makes perfect. 熟能生巧。
bour breeds our body, learning breads our soul. 劳动教养了身体,学习教养了心灵。
----史密斯5.Exercise is to the body what thinking is to the brain. 运动和身体的关系如同思考和大脑的关系。
6. He that will not to work shall not eat. 不劳动者不得食。
3.与劳动有关的名言英语的bour breeds our body, learning breads our soul.劳动教养了身体,学习教养了心灵。
----史密斯2. He that will not to work shall not eat.不劳动者不得食。
bor vanquishes all.劳动征服一切。
---- 维吉尔4.Work is the true source of human welfare.劳动是人类的幸福之源。
人工智能时代大学生如何有效学习英语作文
人工智能时代大学生如何有效学习英语作文全文共3篇示例,供读者参考篇1How College Students Can Effectively Learn English Writing in the Age of Artificial IntelligenceAs a college student in the era of rapidly advancing artificial intelligence (AI), I often find myself wondering how this disruptive technology will impact the way we learn and approach various academic subjects, particularly in the realm of English writing. While AI tools like language models and writing assistants have the potential to streamline certain aspects of the writing process, it's crucial that we, as students, approach them with caution and develop strategies to harness their capabilities effectively while continuing to hone our own critical thinking and creative writing skills.One of the primary concerns surrounding AI in the context of English writing is the risk of plagiarism and academic dishonesty. With language models capable of generating human-like text on virtually any topic, it becomes tempting for students to rely heavily on these tools, potentially passing offAI-generated content as their own. However, it's essential to recognize that this not only undermines the learning process but also violates academic integrity principles. As students, we must remain vigilant and develop a strong ethical compass that guides our use of AI in a responsible and transparent manner.That being said, AI writing assistants can be incredibly valuable resources when used appropriately. For instance, they can help us brainstorm ideas, overcome writer's block, and refine our writing by suggesting alternative phrasings or identifying areas for improvement. Additionally, AI tools can be instrumental in developing our research skills by helping us quickly sift through vast amounts of information and identify relevant sources.However, it's crucial to remember that AI is a tool, not a replacement for our own critical thinking and creativity. While AI can generate text, it cannot truly understand the deeper nuances of language, context, and human experience that are essential for crafting truly compelling and meaningful pieces of writing.To effectively learn English writing in the age of AI, we must strike a balance between leveraging the capabilities of these tools and actively developing our own skills. One approach could be to use AI writing assistants as a starting point, generatingrough drafts or outlines, and then critically evaluating and refining the content through our own analysis, research, and creative input.Furthermore, it's essential to cultivate a deep understanding of the writing process itself. This includes mastering foundational skills such as grammar, punctuation, and sentence structure, as well as developing a strong grasp of rhetorical techniques, literary devices, and genre conventions. By building a solid foundation in these areas, we can better evaluate and refine the output of AI writing tools, ensuring that our final work is polished, coherent, and reflective of our own unique voices and perspectives.Collaborative writing exercises can also be invaluable in this era of AI. By working together with classmates, we can engage in peer review, critique each other's work, and collectively analyze the strengths and weaknesses of AI-generated content. This collaborative approach not only fosters critical thinking and communication skills but also helps us develop a deeper appreciation for the human elements of writing that AI cannot fully replicate.Moreover, we should embrace the opportunity to explore new forms of writing that leverage the unique capabilities of AI.For instance, we could experiment with interactive narratives, personalized storytelling, or data-driven writing projects that blend human creativity with AI's ability to process and synthesize vast amounts of information.In addition to developing our writing skills, it's equally important to cultivate a strong understanding of the ethical and societal implications of AI. As future professionals and leaders, we must grapple with questions surrounding the responsible development and deployment of AI technologies, particularly in domains like creative writing, where the potential for misuse and unintended consequences is significant.Ultimately, the age of AI presents both challenges and opportunities for college students learning English writing. By adopting a balanced and responsible approach, we can harness the power of AI tools while simultaneously nurturing our own critical thinking, creativity, and ethical grounding. It's a delicate balancing act, but one that is essential for ensuring that we remain authors of our own narratives, even as AI continues to reshape the literary landscape.篇2How College Students Can Effectively Learn English Writing in the Age of AIAs a college student navigating the rapidly evolving landscape of artificial intelligence (AI), the task of mastering English writing skills has taken on a new level of complexity and significance. With AI-powered writing assistants becoming increasingly sophisticated, it's crucial for us to adapt our learning strategies and leverage these tools while maintaining our authenticity and critical thinking abilities.First and foremost, we must acknowledge the potential pitfalls of overreliance on AI writing tools. While they can undoubtedly enhance our productivity and offer valuable suggestions, blindly accepting their outputs without critical evaluation can lead to plagiarism, incoherent writing, and a lack of original thought. It's essential to approach these tools with a healthy dose of skepticism and use them as aids rather than substitutes for our own efforts.One effective strategy is to use AI writing assistants as a brainstorming tool. We can prompt them with broad topics or prompts and analyze the generated content for ideas, structures, and persuasive arguments. This process can stimulate our creative thinking and provide fresh perspectives, which we canthen refine and articulate in our own words. By actively engaging with the AI's output, we reinforce our understanding of the subject matter and develop our critical thinking skills.Additionally, we can leverage AI writing tools to improve our grammar, syntax, and overall writing mechanics. These tools can often identify and suggest corrections for common errors, such as subject-verb agreement, tense consistency, and word choice. However, it's crucial to understand the rationale behind these suggestions and not simply accept them blindly. By actively engaging with the feedback and seeking clarification when needed, we can deepen our understanding of the nuances of the English language.Furthermore, AI writing assistants can be valuable resources for researching and organizing information. We can use them to gather relevant sources, synthesize key points, and structure our arguments more effectively. However, it's essential tocross-reference the information provided by AI tools with reputable academic sources and exercise our own judgment in evaluating the credibility and relevance of the information.Alongside leveraging AI tools, it's imperative that we actively practice writing regularly. Consistent practice is the key to developing our writing skills, and no AI tool can replicate theexperience of crafting our own unique voice and style. We should seek out opportunities to write for diverse audiences and purposes, such as academic essays, creative writing assignments, or even personal journals. By continuously challenging ourselves and receiving feedback from instructors and peers, we can refine our writing abilities and develop a deeper understanding of effective communication.Moreover, we should embrace collaboration and peer review as valuable components of the learning process. Engaging in constructive discussions with classmates, sharing our work, and providing thoughtful feedback can foster a supportive learning environment and enhance our critical thinking abilities. By analyzing and critiquing each other's writing, we can gain insights into different writing styles, identify areas for improvement, and develop a deeper appreciation for the art of effective communication.In the age of AI, it's also crucial to cultivate a strong ethical foundation and understand the implications of our actions. We must be mindful of the potential misuse of AI writing tools, such as generating plagiarized content or spreading misinformation. By adhering to academic integrity principles and acknowledging the limitations of AI, we can maintain our credibility andcontribute to a more responsible and ethical use of these technologies.Ultimately, the path to becoming proficient English writers in the age of AI lies in striking a balance between leveraging these powerful tools and maintaining our autonomy, critical thinking, and creativity. By approaching AI writing assistants as aids rather than replacements, we can harness their potential while preserving our unique voices and perspectives. With commitment, practice, and a willingness to learn, we can navigate this new era and emerge as skilled communicators, capable of conveying our ideas with clarity, depth, and authenticity.篇3How College Students Can Effectively Learn English Writing in the Age of AIAs a college student in the era of artificial intelligence (AI), mastering English writing skills has become more crucial than ever. With AI-powered tools like language models and writing assistants, the way we approach learning and practicing writing has undergone a substantial transformation. While these technologies offer immense potential, they also presentchallenges that require us to adapt our strategies for effective learning. In this essay, I will explore various approaches that can help college students harness the power of AI while developing their English writing abilities.Embrace AI as a Supplementary Tool, Not a ReplacementIt's important to understand that AI should be viewed as a complementary tool rather than a replacement for our own writing skills. Language models like ChatGPT can undoubtedly assist with tasks such as generating ideas, providing writing suggestions, and improving grammar and style. However, relying solely on these tools can hinder our ability to think critically, express our unique perspectives, and develop our own writing voices.Instead, we should leverage AI as a supportive resource while maintaining our agency as writers. For instance, we can use AI to generate outlines or rough drafts, which we can then refine and polish with our own critical thinking and creative expression. By striking a balance between AI assistance and our own efforts, we can enhance our writing skills while retaining our authenticity.Engage in Deliberate Practice and Feedback LoopsWhile AI can provide valuable insights and suggestions, true mastery of English writing comes through deliberate practice and continuous feedback. One effective approach is to regularly engage in writing exercises, such as freewriting, journaling, or crafting short stories or essays on diverse topics. By consistently putting pen to paper (or fingers to keyboard), we can develop fluency, refine our writing styles, and internalize language patterns.Furthermore, seeking feedback from instructors, peers, and even AI writing assistants can be invaluable. Constructive criticism can help us identify areas for improvement, recognize our strengths, and gain fresh perspectives on our writing. By incorporating feedback into our writing process, we can continuously refine our skills and adapt to the ever-evolving demands of effective communication.Cultivate Critical Thinking and CreativityWhile AI excels at generating coherent and grammatically correct text, it often struggles to match the depth of human critical thinking and creativity. As students, we must actively cultivate these essential skills to produce truly compelling and impactful writing.One way to develop critical thinking is by engaging in deep analysis and interpretation of diverse texts, from literary works to academic papers. By examining the rhetorical strategies, arguments, and stylistic choices employed by skilled writers, we can enhance our own ability to construct well-reasoned and persuasive pieces.Additionally, embracing creative writing exercises can unleash our imaginative potential and help us find our unique voices as writers. Exploring different genres, experimenting with narrative techniques, and pushing the boundaries of language can foster originality and prevent our writing from becoming formulaic or predictable.Understand the Ethical Considerations of AI in WritingAs AI writing tools become more advanced and accessible, it's crucial to understand the ethical implications of their use. Plagiarism, academic integrity, and intellectual property rights are just a few of the concerns that arise when incorporatingAI-generated content into our writing.We must develop a clear understanding of ethical guidelines and best practices for responsible AI use in academic and professional contexts. This may involve citing AI-assisted portions of our work, adhering to institutional policies, andmaintaining transparency about the extent of AI involvement in our writing process.By approaching AI writing tools with ethical awareness, we can leverage their benefits while upholding the principles of academic integrity and respect for intellectual property.Embrace Continuous Learning and AdaptabilityThe field of AI is rapidly evolving, and the tools and technologies available to us as writers will continue to change and advance. To stay ahead of the curve, we must cultivate a mindset of continuous learning and adaptability.This means regularly updating our knowledge and skills, exploring new AI writing tools as they emerge, and being willing to modify our approaches as needed. Attending workshops, online courses, or engaging with AI writing communities can help us stay informed about the latest developments and best practices.Additionally, maintaining an open and curious mindset will enable us to embrace new opportunities for learning and growth. By remaining flexible and embracing change, we can ensure that our English writing skills remain relevant and effective in the ever-changing landscape of AI-assisted writing.ConclusionIn the age of artificial intelligence, mastering English writing skills as college students requires a balanced and strategic approach. By embracing AI as a supplementary tool, engaging in deliberate practice and feedback loops, cultivating critical thinking and creativity, understanding ethical considerations, and embracing continuous learning and adaptability, we can harness the power of AI while developing our own unique writing abilities.Ultimately, the key to effective learning lies in our willingness to adapt, our commitment to constant improvement, and our ability to blend the strengths of AI with our own human ingenuity and creativity. By striking this balance, we can not only succeed as English writers in the age of AI but also contribute to the ongoing evolution of written communication in meaningful and impactful ways.。
Human Learning
Skinner’s Operant Conditioning
Operants are classes of responses. Crying, sitting down, walking, and batting a baseball are operants. They are sets of responses that are emitted and governed by the consequences they Of, relating to, orcontrast, respondents Psychology produce. In being a response that occurs spontaneously and is are sets of responses that are elicited by identifiable identified by its reinforcing or inhibiting stimuli. effects. Operant crying depends on its effect on the parents and is maintained or changed according to their response to it.
Meaningful Learning Theory
David Ausubel contends that learning takes place in the human organism through a meaningful process of relating new events or items to already existing cognitive concepts or propositions–hanging new items on existing cognitive pegs. Meaning is a “clearly articulated or precisely differentiated conscious experience that emerges when potentially meaningful signs, symbols, concepts, or propositions are related to and incorporated within a given individual’s cognitive structure on a nonarbitrary and substantive basis.” (Anderson and Ausubel 1956:8)
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Topic 4 Human learningI.Behavioristic learning theoriesII.Ausubel’s meaningful learning theoryRote and meaningful learningThe cognitive theory of learning as put forward by Ausubel is perhaps best understood by contrasting rote and meaningful learningRote learning: the process of acquiring material as discrete and relatively isolated entities that are relatable to cognitive structure only in an arbitrary and verbatim fashion, not permitting the establishment of meaningful relations, for example, learning a few telephone numbers or zip codes.Meaningful learning: a process of relating and anchoring new materials to relevant established entities in cognitive structures. As new material enters the cognitive field, it interacts with, and is appropriately subsumed under, a more inclusive conceptual system. The very fact that material is subsumed or relatable to stable elements in cognitive structure, account for its meaningfulness. Any learning situation can be meaningful if1)learners have a meaningful learning set, that is, a disposition to relate the new learning task to what they already know; and2)the learning task itself is potentially meaningful to the learner, that is, relatable to the learner’s structure of knowledge.The significance of the distinction between rote and meaningful learning becomes clear when we consider the relative efficiency of the two types of learning in terms of retention, or long term memory. Meaningfully learned material has far greater potential for retention than the material learned in a rote fashion.Systematic forgetting: we cannot say that what is meaningfully learned is never forgotten. However, in meaningful learning, forgetting takes place in a much more intentional and purposeful manner because it is a continuation of the very process of subsumption. It is more economical and less burdensome to retain a single inclusive concept than to remember a large number of more specific items.In recent years the process of language attrition has garnered some attention. There are a variety of possible causes of the loss of second language skills: the strength and condition of initial learning, the kind of use that a second language has been put to, the motivational factors, the context that lacks an integrative orientation, etc.III.Rogers’s humanistic psychologyCarl Rogers is a psychologist, working in an attempt to be of therapeutic help to individuals. His humanistic psychology has more of an affective focus than a cognitive one. He is not traditionally thought of as a learning psychologist, yet he and his colleagues and followers have had a significant impact on our present understanding of learning. Rogers studied the “whole person” as a physical and cognitive, but primarily emotional being. Given a non-threatening environment, a person will form a picture of reality that is indeed congruent with reality and will grow and learn. Rogers’s position has important implications for education. The focus is away from “teaching”and toward “learning”. The goal of education is the facilitation of change and learning. Learning how to learn is more important than being taught something from the superior vantage point of a teacher who unilaterally decides what shall be taught.Rogers’ theory is not without its flaws. The educator may be tempted to take the nondirective approach too far, to the point that valuable time is lost in the process of allowing students to “discover” facts and principles for themselves. Also, a non-threatening environment might become so non-threatening that the facilitating tension needed for learning is removed.IV.Transfer and interferenceTransfer is a general term describing the carryover of previous performance or knowledge to subsequent learning. Positive transfer occurs when the prior knowledge benefits the learning task, that is, when a previous item is correctly applied to present subject matter. Negative transfer occurs when the previous performance disrupts the performance on a second task. The latter is also referred to as interference.It has been common in second language teaching to stress the role of interference, that is, the interfering effects of the native language on the target language. It is of course not surprising that this process has been singled out, for native language interference is surely the most immediately noticeable source of error among second language learners. However, it is exceedingly important to remember that the native language of a second language learner is often positively transferred, in which case the learner benefits from the facilitating effects of the first language.V.Generalization and overgeneralizationGeneralization is a crucially important and pervading strategy in human learning. To generalize means to infer or derive a law, rule, or conclusion, usually from the observation of particular instances. The principle of generalization can be explained by Ausubel’s concept of meaningful learning. Meaningful learning is in fact generalization. The items to be learned are subsumed (generalized) under higher-order categories for meaningful retention. Much of human learning involves generalization.In second language acquisition it has been common to refer to overgeneralization as a process that occurs as the second language learner acts within the target language, generalizing a particular rule or item in the second language (irrespective of the native language) beyond legitimate bounds. Typical examples of overgeneralization in learning English as a second language are past tense regularization and utterances like John doesn’t can study or He told me when should I get off the train.VI.Aptitude and intelligence1.Aptitude: the natural ability to learn a task:The MLAT (Modern Language Aptitude Test) measures the following abilities:1)Phonetic coding ability: the ability to code auditory phonetic material in such a way thatthis can be recognized, identified, and remembered over something longer than a fewseconds2)Grammatical sensitivity: the ability to recognize the grammatical function of words insentence contexts.3)Inductive language learning ability: the ability to infer linguistic forms, rules and patternsfrom new linguistic content itself with a minimum of supervision or guidance.4)Rote memorization ability: the ability to learn a large number of associations in arelatively short time. (not mentioned in Carroll’s later publications)The MLAT consists of five subsets:1) Number learning: examinees are asked to memorize names for certain numbers in aninvented language and then to write the numbers down for novel combinations they hear.2) Phonetic script: examinees associate graphic symbols and English speech sounds3) Spelling clues: examinees must detect an English word when given a phonetic reading of it.4) Words in sentences: examinees identify the word or phrase in one sentence that functions thesame way as a word / phrase in another sentence:(1) He spoke VERY well of you.(2) Suddenly the music became quite loud.5) Paired associates: examinees study foreign-language translations for native-language wordsfor a short time and then take a multiple-choice test in which they must recognize thetranslations.The PLAB (Pimsleur Language Aptitude Battery) measures three components of language aptitude:1)Verbal intelligence, by which is meant both familiarity with words and the ability toreason analytically about verbal materials2)Motivation3)Auditory abilityThe PLAB consists of 6 subtests:1)Grade Point Average2)Interest3)Vocabulary4)Language analysis5)Sound discrimination6)Sound-symbol correspondence2.Intelligence: traditionally linguistic and logical-mathematical abilities.The role of intelligence in language learning.1)it does not play any role in language learning2)it does not play any role in the learning of the lower-level linguistic abilities but plays a rolein the learning of the higher-level language-related abilitiesGardner’s more comprehensive picture of intelligence:1)linguistic2)logical-mathematical3)spatial: the ability to find your way in an environment, to form mental images of realty andto transform them readily4)musical: the ability to perceive and create pitch and rhythmic patterns5)bodily-kinesthetic: fine motor movement, athletic prowess6)interpersonal: the ability to understand others, how they feel, what motivates them, how theyinteract with one another7)intrapersonal: the ability to see oneself, to develop a sense of self-identity。