手机人工智能：革新或隐患？

一、手机人工智能：革新或隐患？

手机人工智能的发展与应用

随着科技的不断进步，手机人工智能正在成为智能手机的一项重要功能。人工智能技术的快速发展使得手机变得更加智能和便捷，带来了许多改变。手机人工智能通过对数据和算法的分析，能够根据用户的喜好和习惯提供个性化的服务，包括智能助手、语音交互、智能摄影等。这些功能的实现使得手机不仅仅是一个通信工具，更成为智能生活的一部分。

手机人工智能的优势

手机人工智能给我们的生活带来了诸多便利。首先，智能助手可以帮助我们完成许多事务，如安排日程、发送信息、查询资讯等，极大地提高了我们的工作效率。其次，手机语音交互的发展，使得我们能够通过语音指令来操控手机，省去了繁琐的操作过程。另外，手机人工智能还提供了智能摄影功能，通过分析场景和环境来实现更好的拍照效果，给我们带来更好的摄影体验。

手机人工智能的挑战

然而，手机人工智能也面临一些挑战。首先是隐私问题。因为手机人工智能需要收集用户的信息和数据来提供个性化服务，这就可能涉及到用户隐私的泄露。另外，手机人工智能的数据分析和算法可能会带来一些误判和误操作，导致不准确的结果。此外，人工智能的发展也可能引发一些伦理和道德问题，如人工智能对社会的影响和职业的变化等。

结论

手机人工智能的发展为我们的生活带来了诸多便利，但也带来了一些挑战。在享受其带来的便捷的同时，我们需要关注和解决其中的问题。保护用户隐私、提高算法的准确性、加强对人工智能技术的监管和引导等都是重要的工作。只有正确面对和应对这些问题，手机人工智能才能更好地服务于人类，并推动科技的发展。

感谢您阅读此文，希望通过本文对手机人工智能的发展与应用有更深入的了解，同时也能引发您对手机人工智能的思考与讨论。

二、人工智能可取代或超越人的智能？

从理论上来说，计算机人工智能是否有取代人类的可能性？

现在的计算机都还是需要人工手动编程才能实现自己的功能，那么从理论上来讲，在未来会不会出现一种编程语言，它能够自由组合，随机碰撞出新的代码块，然后实现人类无法预知的新功能，外在表现就是机器人具备了"独立人格"、“学习能力”和“人类情感”等特征。他是不会取代人工的。

三、“人工智能”在大学里是什么学科或专业？

大学有专门的人工智能专业。人工智能是计算机科学的一个分支，属于计算机学科。 人工智能专业是中国高校人计划设立的专业，旨在培养中国人工智能产业的应用型人才，推动人工智能一级学科建设。2018年4月，教育部在研究制定《高等学校引领人工智能创新行动计划》，并研究设立人工智能专业，进一步完善中国高校人工智能学科体系。 2019年3月，教育部印发了《教育部关于公布2018年度普通高等学校本科专业备案和审批结果的通知》，根据通知，全国共有35所高校获首批「人工智能」新专业建设资格。 人工智能是一门极富挑战性的科学，从事这项工作的人必须懂得计算机知识，心理学和哲学。人工智能是包括十分广泛的科学，它由不同的领域组成，如机器学习，计算机视觉等等，总的说来，人工智能研究的一个主要目标是使机器能够胜任一些通常需要人类智能才能完成的复杂工作。

四、人工智能的发展和应用目标是模拟或人脑？

人工智能的发展和应用目标是模拟人脑，并且最终为人类一些更高领域的问题和方向提供实际性的推动。我认为是正确的，伴随着互联网领域的发展越来越快，人工智能的概念日益蓬勃发展，从解决一些传统劳动到更高深的领域，推动社会的发展进步

五、机器学习或人工智能论文的主题是什么？

【1】2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance标题：2跳邻居类相似度（2NCS）：表明图形神经网络性能的图形结构指标作者：Andrea Cavallo, Claas Grohnfeldt, Michele Russo, Giulio Lovisotto, Luca Vassio链接：https://arxiv.org/abs/2212.13202

摘要：

图形神经网络（GNNs）在众多领域的图形结构数据上取得了最先进的性能。它们将节点表示为其附近区域的摘要的基本能力已被证明对同亲图特别有效，在同类型的节点倾向于连接。在异亲图中，不同类型的节点很可能相连，GNN的表现不太稳定，因为邻域信息可能不太具有代表性，甚至是误导性。另一方面，GNN在所有的异亲图上的表现并不差，而且对其他影响GNN表现的图的属性缺乏了解。

在这项工作中，我们强调了广泛使用的同亲率和最近的跨类邻里相似度（CCNS）指标在估计GNN性能方面的局限性。为了克服这些局限性，我们引入了两跳邻域相似性（2NCS），这是一个新的定量图结构属性，与GNN性能的相关性比其他指标更强更一致。2NCS认为两跳邻域是指导GCN训练-推理过程的两步标签传播过程在理论上的结果。在一个合成图和八个真实世界图数据集上的实验证实，在估计基于GCN和GAT的架构在节点分类任务上的准确性方面，比现有指标有一致的改进。

Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across numerous domains. Their underlying ability to represent nodes as summaries of their vicinities has proven effective for homophilous graphs in particular, in which same-type nodes tend to connect. On heterophilous graphs, in which different-type nodes are likely connected, GNNs perform less consistently, as neighborhood information might be less representative or even misleading. On the other hand, GNN performance is not inferior on all heterophilous graphs, and there is a lack of understanding of what other graph properties affect GNN performance.In this work, we highlight the limitations of the widely used homophily ratio and the recent Cross-Class Neighborhood Similarity (CCNS) metric in estimating GNN performance. To overcome these limitations, we introduce 2-hop Neighbor Class Similarity (2NCS), a new quantitative graph structural property that correlates with GNN performance more strongly and consistently than alternative metrics. 2NCS considers two-hop neighborhoods as a theoretically derived consequence of the two-step label propagation process governing GCN's training-inference process. Experiments on one synthetic and eight real-world graph datasets confirm consistent improvements over existing metrics in estimating the accuracy of GCN- and GAT-based architectures on the node classification task.

【2】Efficient Graph Reconstruction and Representation Using Augmented Persistence Diagrams标题：利用增强的持久性图谱进行高效的图谱重构和表述作者：Brittany Terese Fasy, Samuel Micka, David L. Millman, Anna Schenfisch, Lucia Williams链接：https://arxiv.org/abs/2212.13206

摘要：持久同构是一种工具，可以用来通过量化同构特征来总结数据的形状。当数据是Rd中的一个物体时，（增强的）持久同构变换（(A)PHT）是一个持久图族，由环境空间中的方向作为参数。最近在理解PHT方面的一个进展是使用了重构的框架，以便找到有限的一组方向来忠实地表示形状，这个结果在理论上和实践上都很有意义。在本文中，我们对这一结果进行了改进，并提出了一种改进的图--以及更广泛的单骨架--重建算法。改进之处在于重建边缘，我们使用径向二进制（多）搜索。所采用的二进制搜索利用了这样一个事实，即边缘可以相对于参考平面在径向上排序，这是图形的一个独特特征。

Persistent homology is a tool that can be employed to summarize the shape of data by quantifying homological features. When the data is an object in Rd, the (augmented) persistent homology transform ((A)PHT) is a family of persistence diagrams, parameterized by directions in the ambient space. A recent advance in understanding the PHT used the framework of reconstruction in order to find finite a set of directions to faithfully represent the shape, a result that is of both theoretical and practical interest. In this paper, we improve upon this result and present an improved algorithm for graph -- and, more generally one-skeleton -- reconstruction. The improvement comes in reconstructing the edges, where we use a radial binary (multi-)search. The binary search employed takes advantage of the fact that the edges can be ordered radially with respect to a reference plane, a feature unique to graphs.

【3】A Combined Synchronization Index for Grassroots Activism on Social Media标题：社会媒体上基层活动的综合同步指数作者：Lynnette Hui Xian Ng, Kathleen M. Carley链接：https://arxiv.org/abs/2212.13221

摘要：社交媒体提供了公民的声音，催生了基层的集体行动，用户部署了一致的努力来传播网上的叙述，甚至进行线下的抗议。有时，这些集体行动会得到无机同步的帮助，这些同步来自于机器人行为者。因此，识别社交媒体上新出现的话语的同步性以及对话中有机/无机活动的迹象是很重要的。这提供了一种分析事件的方式，以了解线下抗议和暴力的可能性。在这项研究中，我们在过去对社交媒体上同步活动的定义--用户同时行动--的基础上，开发了一个综合同步指数（CSI），该指数在衡量用户同步性时采用了分层方法。我们将这一指数应用于Twitter上的六个政治和社会活动事件，并分析了三种行动类型：通过标签、URL和@mentions的同步性。CSI对一个事件中所有行动类型的同步性进行了整体量化，这使得六个事件的同步性谱系得到了排名。在大多数事件中，人类用户的同步性得分高于机器人用户；与其他配对（即机器人-机器人和人类-人类）相比，机器人和人类在所有事件中表现出最多的同步性活动。我们进一步依靠CSI-网络得分与网络中心性指标的和谐与不和谐来观察有机/无机同步的存在。我们希望这项工作有助于以集体的方式调查社交媒体内的同步行动。

Social media has provided a citizen voice, giving rise to grassroots collective action, where users deploy a concerted effort to disseminate online narratives and even carry out offline protests. Sometimes these collective action are aided by inorganic synchronization, which arise from bot actors. It is thus important to identify the synchronicity of emerging discourse on social media and the indications of organic/inorganic activity within the conversations. This provides a way of profiling an event for possibility of offline protests and violence. In this study, we build on past definitions of synchronous activity on social media -- simultaneous user action -- and develop a Combined Synchronization Index (CSI) which adopts a hierarchical approach in measuring user synchronicity. We apply this index on six political and social activism events on Twitter and analyzed three action types: synchronicity by hashtag, URL and @mentions.The CSI provides an overall quantification of synchronization across all action types within an event, which allows ranking of a spectrum of synchronicity across the six events. Human users have higher synchronous scores than bot users in most events; and bots and humans exhibits the most synchronized activities across all events as compared to other pairs (i.e., bot-bot and human-human). We further rely on the harmony and dissonance of CSI-Network scores with network centrality metrics to observe the presence of organic/inorganic synchronization. We hope this work aids in investigating synchronized action within social media in a collective manner.

【4】Saliency-Augmented Memory Completion for Continual Learning标题：持续学习的显著性增强记忆完成度作者：Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao链接：https://arxiv.org/abs/2212.13242

摘要：持续学习被认为是迈向下一代人工智能的关键一步。在各种方法中，基于重放的方法，保持和重放以前样本的小片段记忆，是对抗灾难性遗忘的最成功的策略之一。然而，由于遗忘是不可避免的，鉴于有界的记忆和无界的任务，如何遗忘是一个持续学习必须解决的问题。因此，除了简单地避免灾难性遗忘之外，一个未被充分探索的问题是如何合理地遗忘，同时确保人类记忆的优点，包括1.存储效率，2.可推广性，以及3.一些可解释性。为了同时实现这些，我们的论文提出了一个新的突出性增强的持续学习的记忆完成框架，其灵感来自认知神经科学中记忆完成分离的最新发现。具体来说，我们创新性地提出通过显著性图的提取和记忆编码，将图像中对任务最重要的部分储存在表象记忆中。当学习新的任务时，以前的数据会被一个自适应的数据生成模块涂抹，这个模块的灵感来自于人类如何完成表象记忆。该模块的参数在所有任务中都是共享的，它可以与持续学习分类器联合训练，作为双级优化。在几个持续学习和图像分类的基准上进行的广泛实验证明了所提出的方法的有效性和效率。

Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.

【5】A Posteriori error estimates for Darcy-Forchheimer's problem coupled with the convection-diffusion-reaction equation标题：耦合对流-扩散-反应方程的Darcy-Forchheimer问题的后验误差估计作者：Toni Sayah, Georges Semaan, Faouzi Triki链接：https://arxiv.org/abs/2212.13247

摘要：在这项工作中，我们推导了对流-扩散-反应方程的后验误差估计，该方程与Darcy-Forchheimer问题相耦合，由一个取决于流体浓度的非线性外部源决定。我们介绍了与该问题相关的变分公式，并使用有限元方法对其进行离散。我们用两类可计算的误差指标证明了最佳后验误差。第一种是与线性化相关的，第二种是与离散化相关的。然后，我们在精确解的附加正则性假设下找到误差的上限和下限。最后，进行数值计算以显示所获得的误差指标的有效性。

In this work we derive a posteriori error estimates for the convection-diffusion-reaction equation coupled with the Darcy-Forchheimer problem by a nonlinear external source depending on the concentration of the fluid. We introduce the variational formulation associated to the problem, and discretize it by using the finite element method. We prove optimal a posteriori errors with two types of calculable error indicators. The first one is linked to the linearization and the second one to the discretization. Then we find upper and lower error bounds under additional regularity assumptions on the exact solutions. Finally, numerical computations are performed to show the effectiveness of the obtained error indicators.

【6】Characterizing and Modeling Control-Plane Traffic for Mobile Core Network标题：移动核心网控制层流量的特征和建模作者：Jiayi Meng, Jingqi Huang, Y. Charlie Hu, Yaron Koral, Xiaojun Lin, Muhammad Shahbaz, Abhigyan Sharma链接：https://arxiv.org/abs/2212.13248

摘要：在本文中，我们首先对控制面流量进行了我们所知的第一个深入的特征分析，使用了一个真实世界的LTE移动核心网（MCN）中37325个UE的控制面跟踪采样。我们的分析表明，控制事件在设备类型和UE的时间上表现出明显的多样性。其次，我们研究了被广泛采用于互联网流量建模的传统概率分布是否能对源自单个UE的控制面流量进行建模。我们的分析表明，控制事件的到达时间以及蜂窝网络中EMM和ECM的UE状态的停留时间不能被建模为泊松过程或其他传统的概率分布。我们进一步表明，这些模型不能捕捉控制面流量的原因是由于其较高的突发性和比传统模型更长的累积分布尾巴。第三，我们为UE集群提出了一个基于半马尔可夫模型的自适应集群方案的两级分层状态机器流量模型，以捕捉移动网络控制面流量的关键特征--特别是每个UE产生的事件之间的依赖性，以及UE之间设备类型和时间的多样性。最后，我们展示了我们的模型如何能够很容易地从LTE调整到5G，以支持5G控制面流量的建模，当5G UE的大量控制面跟踪可用于训练调整后的模型。我们开发的LTE/5G网络控制面流量生成器已向研究界开放，以支持高性能MCN架构设计研发。

In this paper, we first carry out to our knowledge the first in-depth characterization of control-plane traffic, using a real-world control-plane trace for 37,325 UEs sampled at a real-world LTE Mobile Core Network (MCN). Our analysis shows that control events exhibit significant diversity in device types and time-of-day among UEs. Second, we study whether traditional probability distributions that have been widely adopted for modeling Internet traffic can model the control-plane traffic originated from individual UEs. Our analysis shows that the inter-arrival time of the control events as well as the sojourn time in the UE states of EMM and ECM for the cellular network cannot be modeled as Poisson processes or other traditional probability distributions. We further show that the reasons that these models fail to capture the control-plane traffic are due to its higher burstiness and longer tails in the cumulative distribution than the traditional models. Third, we propose a two-level hierarchical state-machine-based traffic model for UE clusters derived from our adaptive clustering scheme based on the Semi-Markov Model to capture key characteristics of mobile network control-plane traffic -- in particular, the dependence among events generated by each UE, and the diversity in device types and time-of-day among UEs. Finally, we show how our model can be easily adjusted from LTE to 5G to support modeling 5G control-plane traffic, when the sizable control-plane trace for 5G UEs becomes available to train the adjusted model. The developed control-plane traffic generator for LTE/5G networks is open-sourced to the research community to support high-performance MCN architecture design R&D.

【7】Robust computation of optimal transport by β-potential regularization标题：通过β-势能正则化对最佳运输进行稳健计算作者：Shintaro Nakamura, Han Bao, Masashi Sugiyama链接：https://arxiv.org/abs/2212.13251

摘要：最佳运输（OT）已经成为机器学习领域中广泛使用的工具，用于衡量概率分布之间的差异。例如，OT是一个流行的损失函数，它量化了经验分布和参数模型之间的差异。最近，一个熵罚项和著名的Sinkhorn算法已被普遍用于以计算效率高的方式近似原始OT。然而，由于Sinkhorn算法运行一个与Kullback-Leibler分歧相关的投影，它经常容易受到异常值的影响。为了克服这个问题，我们建议用与所谓的β发散相关的β-势项来规范OT，这是在稳健统计学中开发的。我们的理论分析显示，β-势可以防止质量被传送到异常值。我们在实验中证明，用我们的算法计算的传输矩阵有助于稳健地估计概率分布，即使在有离群值的情况下。此外，我们提出的方法可以成功地从受污染的数据集中检测出离群值

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called β-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the β-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset

【8】DSI2I: Dense Style for Unpaired Image-to-Image Translation标题：DSI2I: 非配对图像到图像翻译的密集风格作者：Baran Ozaydin, Tong Zhang, Sabine Susstrunk, Mathieu Salzmann链接：https://arxiv.org/abs/2212.13253

摘要：基于非配对典范的图像-图像（UEI2I）翻译旨在将源图像翻译成具有目标图像典范风格的目标图像域，而不需要地面真实的输入-翻译对。现有的UEI2I方法使用一个全局的、图像级别的特征向量来表示风格，或者使用每个物体实例/类别的一个向量来表示风格，但需要对场景语义的了解。相比之下，我们建议将风格表示为密集的特征图，允许对源图像进行更精细的转移，而不需要任何外部语义信息。然后，我们依靠知觉和对抗性损失来分离我们的密集风格和内容表征，并利用无监督的跨领域语义对应关系来将典范风格转变成源内容。我们在两个数据集上证明了我们的方法的有效性，这些数据集使用的是标准指标和新的本地化风格指标，以阶级的方式测量风格相似性。我们的结果证明，与最先进的方法相比，我们的方法所产生的译文更加多样化，更加接近典范，同时也保留了源内容。

Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate a source image to a target image domain with the style of a target image exemplar, without ground-truth input-translation pairs. Existing UEI2I methods represent style using either a global, image-level feature vector, or one vector per object instance/class but requiring knowledge of the scene semantics. Here, by contrast, we propose to represent style as a dense feature map, allowing for a finer-grained transfer to the source image without requiring any external semantic information. We then rely on perceptual and adversarial losses to disentangle our dense style and content representations, and exploit unsupervised cross-domain semantic correspondences to warp the exemplar style to the source content. We demonstrate the effectiveness of our method on two datasets using standard metrics together with a new localized style metric measuring style similarity in a class-wise manner. Our results evidence that the translations produced by our approach are more diverse and closer to the exemplars than those of the state-of-the-art methods while nonetheless preserving the source http://content.In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.

【9】Improved Laguerre Spectral Methods with Less Round-off Errors and Better Stability标题：改进的拉盖尔光谱方法，舍弃误差小，稳定性好作者：Shenghe Huang, Haijun Yu链接：https://arxiv.org/abs/2212.13255

摘要：流量分割是网络中的一个必要功能，例如，在路径或服务器上进行负载平衡，或由源的访问限制。服务器的容量（或具有特定访问限制的用户数量）决定了流量应被分割成的部分的大小。最近的一种方法是在三元内容可寻址存储器（TCAM）内实现流量分割，这在交换机中通常都有。减少分配给这一任务的内存量是很重要的，因为TCAM很耗电，而且通常还需要用于其他任务，如分类和路由。最近的工作提出了在最长前缀匹配（LPM）模型中计算一个给定分区的最小实现的算法。在本文中，我们分析了这种最小表示的属性，并证明了其大小的下限和上限。上界对一般的TCAM来说是成立的，我们还证明了一般TCAM的额外下界。我们还分析了一个表示的预期大小，对于均匀随机的有序分区。我们表明，随机分区的预期表示大小至少是最坏情况下分区大小的一半，并且在部件的数量和地址空间大小的对数中是线性的。

Laguerre polynomials are orthogonal polynomials defined on positive half line with respect to weight e−x. They have wide applications in scientific and engineering computations. However, the exponential growth of Laguerre polynomials of high degree makes it hard to apply them to complicated systems that need to use large numbers of Laguerre bases. In this paper, we introduce modified three-term recurrence formula to reduce the round-off error and to avoid overflow and underflow issues in generating generalized Laguerre polynomials and Laguerre functions. We apply the improved Laguerre methods to solve an elliptic equation defined on the half line. More than one thousand Laguerre bases are used in this application and meanwhile accuracy close to machine precision is achieved. The optimal scaling factor of Laguerre methods are studied and found to be independent of number of quadrature points in two cases that Laguerre methods have better convergence speeds than mapped Jacobi methods.

【10】Codes for Load Balancing in TCAMs: Size Analysis标题：TCAM中负载平衡的代码：尺寸分析作者：Yaniv Sadeh, Ori Rottenstreich, Haim Kaplan链接：https://arxiv.org/abs/2212.13256

摘要：这篇短文讨论了不断更新的因果抽象作为未来研究的一个潜在方向。关键的想法是将现有的因果抽象水平修改为不同的细节水平，既与观察数据的历史相一致，又能更有效地解决特定的任务。

Traffic splitting is a required functionality in networks, for example for load balancing over paths or servers, or by the source's access restrictions. The capacities of the servers (or the number of users with particular access restrictions) determine the sizes of the parts into which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Recent works suggested algorithms to compute a smallest implementation of a given partition in the longest prefix match (LPM) model. In this paper we analyze properties of such minimal representations and prove lower and upper bounds on their size. The upper bounds hold for general TCAMs, and we also prove an additional lower-bound for general TCAMs. We also analyze the expected size of a representation, for uniformly random ordered partitions. We show that the expected representation size of a random partition is at least half the size for the worst-case partition, and is linear in the number of parts and in the logarithm of the size of the address space.

【11】Prototype-guided Cross-task Knowledge Distillation for Large-scale Models

标题：原型指导下的大规模模型的跨任务知识提炼作者：Deng Li, Aming Wu, Yahong Han, Qi Tian

链接：https://arxiv.org/abs/2212.13180

摘要：最近，大规模的预训练模型在许多任务中显示出它们的优势。然而，由于巨大的计算复杂性和存储要求，将大规模模型应用于真实场景是具有挑战性的。一个常见的解决方案是知识提炼，它将大规模模型视为教师模型，并帮助训练一个小型学生模型以获得有竞争力的性能。跨任务知识蒸馏法扩大了大规模预训练模型的应用场景。现有的知识提炼工作主要是直接模仿教师模型的最终预测或中间层，这代表了全局层面的特征，是特定任务的。为了缓解不同标签空间的约束，捕捉不变的内在物体特征（如牛和马的腿和尾巴的形状特征）起到了关键作用。考虑到真实场景任务的复杂性和可变性，我们提出了一种原型引导的跨任务知识提炼（ProC-KD）方法，将大规模教师网络的内在本地级对象知识转移到各种任务场景中。首先，为了在跨任务场景中更好地转移教师模型中的泛化知识，我们提出了一个原型学习模块，从教师模型中物体的基本特征表示中学习。其次，针对不同的下游任务，我们提出了一个任务自适应特征增强模块，用学到的泛化原型特征增强学生模型的特征，并指导学生模型的训练以提高其泛化能力。在各种视觉任务上的实验结果证明了我们的方法在大规模模型跨任务知识提炼场景中的有效性。

Recently, large-scale pre-trained models have shown their advantages in many tasks. However, due to the huge computational complexity and storage requirements, it is challenging to apply the large-scale model to real scenes. A common solution is knowledge distillation which regards the large-scale model as a teacher model and helps to train a small student model to obtain a competitive performance. Cross-task Knowledge distillation expands the application scenarios of the large-scale pre-trained model. Existing knowledge distillation works focus on directly mimicking the final prediction or the intermediate layers of the teacher model, which represent the global-level characteristics and are task-specific. To alleviate the constraint of different label spaces, capturing invariant intrinsic local object characteristics (such as the shape characteristics of the leg and tail of the cattle and horse) plays a key role. Considering the complexity and variability of real scene tasks, we propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios. First, to better transfer the generalized knowledge in the teacher model in cross-task scenarios, we propose a prototype learning module to learn from the essential feature representation of objects in the teacher model. Secondly, for diverse downstream tasks, we propose a task-adaptive feature augmentation module to enhance the features of the student model with the learned generalization prototype features and guide the training of the student model to improve its generalization ability. The experimental results on various visual tasks demonstrate the effectiveness of our approach for large-scale model cross-task knowledge distillation scenes.

【12】Advancements in Biometric Technology with Artificial Intelligence

标题：人工智能在生物识别技术方面的进步作者：Lakshmipathi Devaraj, Konark Modi

链接：https://arxiv.org/abs/2212.13187

摘要：认证在处理公共和私人部门的安全方面发挥着重要作用，如医疗系统、银行系统、运输系统和法律与安全。生物识别技术最近发展迅速，特别是在人工智能和身份识别领域。以前，认证过程依赖于密码、身份卡和指纹等安全措施。另一方面，作为这些预防措施的结果，盗窃行为的频率也在增加。作为回应，生物识别安全应运而生，其中，对一个人的识别是基于使用生物识别系统从人体的生理和行为特征得出的特征。生物识别技术小工具被嵌入到计算机系统、电子设备、移动电话和其他消费电子产品中，因此公众可以使用。随着欺诈行为的增加，对生物识别电子设备的需求和使用也在增加。因此，有可能确认一个人的独特身份。本研究的目的是研究生物识别系统在医学和工程学科中的发展。该研究将介绍二手数据的观点和不同的观点，强调需要更深入地了解和应用生物识别技术，以促进其在数字时代的发展。该研究的结果可能会激励人们和企业更有效地采用生物识别技术，以减少数据和身份安全的风险。

Authentication plays a significant part in dealing with security in public and private sectors such as healthcare systems, banking system, transportation system and law and security. Biometric technology has grown quickly recently, especially in the areas of artificial intelligence and identity. Formerly, authentication process has depended on security measures like passcodes, identity fobs, and fingerprints. On the other hand, as just a consequence of these precautions, theft has increased in frequency. In response, biometric security was created, in which the identification of a person is based on features derived from the physiological and behavioral traits of a human body using biometric system. Biometric technology gadgets are available to the public as they are embedded on computer systems, electronic devices, mobile phones, and other consumer electronics. As the fraudulent is increasing demand and use of biometric electronic devices has increased. As a consequence, it may be possible to confirm a person's distinct identification. The goal of this study is to examine developments in biometric systems in the disciplines of medicine and engineering. The study will present the perspectives and different points of view of the secondary data, highlighting the need for more in-depth understanding and application of biometric technology to promote its development in the digital era. The study's findings may inspire people and businesses to more effectively incorporate biometric technologies in order to reduce the risks to data and identity security.

六、人工智能不能取代或超越人类智能的原因？

  人工智能没有意义的概念，没有价值观，只是一具冰冷无情的机械体，没有人类所拥有的丰富的情感和敏锐的感官神经，终究只能沦为人类的工具，而不可能超越人类。

  近年来，人工智能技术发展极其迅速，各种智能设备、智能软件已走进千家万户，改变了我们的生活方式和工作方式。因此，不少人认为，在不久的将来，人工智能将会全面代替人类智能，甚至超越人类智能。不过，这种观点过于悲观，人类的思想和行为中最重要最独特的部分，是人工智能无法实现，更无法替代的。

  然而，人工智能不管多么发达，归根结底，都是在人类给定的框架下解决问题。

七、人类意识是否有被人工智能取代或超越的危险？

对于人工智能的认识，人类再一次欺骗自己，认为这将是人类的末日。这场隐约来临的技术革命将夺走我们的工作，将我们从地球上抹去。其实，这并不令人感到惊讶。

技术恐惧（指对技术对社会及环境造成不良影响的恐惧）从来都不是个新现象。它是人类在经历现代社会每一次技术转变时的一个显著特征。人类总是恐惧会被机器取代。然而，人们在集体性的过度恐惧中幻想出的种种地狱般处境，却从未变成现实。

实际上，每一次技术革命都带来了经济繁荣、生活水平提高、社会平等有所进步，以及其他方面的积极影响。那么这一次的技术革命和以往相比，会大不相同吗？没错，是会不同，只是带来进步的方式不同而已。

人工智能不会导致人类变成“无用阶层”，也不会像一些未来学家所说的那样，造成社会混乱。这一次，AI将彻底改变以往技术革命从未改变的东西——人类自身。

八、或，或，或，或，或，或，有的，有的造句？

十二生肖站在云彩上，它们望着自己的家乡，或哭，或笑，或忧，或虑，或愁，或伤，有的摆摆手，有的摇摇头……

九、跪求请问人工智能在大学里是什么学科或专业？

大学有专门的人工智能专业。人工智能是计算机科学的一个分支，属于计算机学科。 人工智能专业是中国高校人计划设立的专业，旨在培养中国人工智能产业的应用型人才，推动人工智能一级学科建设。2018年4月，教育部在研究制定《高等学校引领人工智能创新行动计划》，并研究设立人工智能专业，进一步完善中国高校人工智能学科体系。

十、人工智能能否取代或超越人类的意识具有自我意识？

人工智能不能取代或超越人类的意识，而具有自我意识。因为人工智能这门科学其实就是属于计算机科学之中的一部分，而且人工智能在不断的发展和进步，不过人工智能其实本质来说应该是对人的思维信息过程的一个模拟处理，人的思维可以分为结构模拟，就是仿照我们大脑的结构机制来照出一种类似于我们人脑的智能机器，这种机器可以模拟我们人类大脑的分析过程。