IT界如雷贯耳的贝尔实验室的历史

贝尔实验室
  贝尔实验室是晶体管、激光器、太阳能电池、发光二极管、数字交换机、通信卫星、电子数字计算机、蜂窝移动通信设备、长途电视传送、仿真语言、有声电影、立体声录音,以及通信网的许多重大发明的诞生地。自1925年以来,贝尔实验室共获得两万五千多项专利,现在,平均每个工作日获得三项多专利。 贝尔实验室的使命是为客户创造、生产和提供富有创新性的技术,这些技术使朗讯科技(Lucent Technologies)公司在通信系统、产品、元件和网络软件方面处于全球领先地位。
  贝尔电话实验室或贝尔实验室,最初是贝尔系统内从事包括电话交换机,电话电缆,半导体等电信相关技术的研究开发机构。
  1925年,当时AT&T总裁,华特·基佛德(Walter Gifford)。收购了西方电子公司的研究部门,成立了一个叫做“贝尔电话实验室公司”的独立实体。AT&T和西方电子各拥有该公司的50%。
  贝尔实验室的工作可以大致分为三个类别:基础研究,系统工程和应用开发。在基础研究方面主要从事电信技术的基础理论研究,包括数学,物理学,材料科学,行为科学和计算机编程理论。系统工程主要研究构成电信网络的高度复杂系统。开发部门是贝尔实验室最大的部门,负责设计构成贝尔系统电信网络的设备和软件。
  1984年以后,按照美国政府分拆AT&T的协议,从贝尔实验室中分割成立了Bellcore。Bellcore 为分拆后的一系列小贝尔公司统一提供研究开发的服务。
  1996年,贝尔实验室以及 AT&T 的设备制造部门脱离 AT&T 成为朗讯科技。 AT&T保留了少数研究人员成为其研究机构——AT&T实验室。
  贝尔实验室的重要研究成果包括:
  1933年,卡尔·央斯基(Karl Jansky)通过研究长途通讯中的静电噪声发现银河中心在持续发射无线电波,此电波称为3K背景辐射。透过此研究而建立了射电天文学。
  1947年,贝尔实验室发明晶体管。参与这项研究的约翰·巴丁(John Bardeen)、威廉·萧克利(William Shockley)、华特·豪舍·布拉顿(Walter Houser Brattain)于1956年获诺贝尔物理学奖。
  香农(Claude Shannon)于1948年发表论文《通讯的数学原理》,奠定了现代通信理论的基础。他的成果是部分基于奈奎斯特和哈特利先前在贝尔实验室的成果。
  贝尔实验室发明光电池。
  贝尔实验室也是UNIX操作系统和C语言的发源地。C语言是由Brian Kernighan、Dennis Ritchie 和 Ken Thompson 在1970年代早期开发的。在1980年代,又由比加尼·斯楚士舒普发展为C++语言。
  2008年8月7日,由于其所有者阿尔卡特朗讯连续6个季度决亏损,自阿尔卡特和朗讯科技合并以来从未盈利,市值已经蒸发了62%,阿尔卡特朗讯不得不出售已经拥有46年历史的贝尔实验室大楼,由美国新泽西的Somerset房地产开发公司购得,并打算将其改建为商场和住宅楼。
  贝尔实验室的母公司阿尔卡特朗讯(Alcatel-Lucent)正在退出基础科学,物理科学和半导体科学研究,将集中于能更快进入市场的领域,如网络、高速电子学、无线技术、纳米技术和软件。正如贝尔实验室发言人说,“在新的创新模式下,研究应该致力于解决母公司的需求”。
  More than any other institution, Bell Labs has helped weave the technological fabric of modern society. Its scientists and engineers have made seminal scientific discoveries, have launched technological revolutions that have reshaped the way people live, work and play, and have built the most advanced and reliable communications networks in the world.
  Today, as the innovation engine behind Lucent Technologies, Bell Labs designs products and services that are at the forefront of communications technology, and conducts fundamental research in fields important to communications. Guided by both experience and vision, Bell Labs is taking the lead in shaping tomorrow's broadband networks powered with service intelligence at every network layer.
  The Bell Labs Difference
  The new Bell Labs is spread across more than 10 countries - the largest R&D organization focused on the needs of large service providers, the leading source of new communications technologies and the most creative force in communications networking today.
  The technology needs of leading service providers now drive our R&D efforts. For example, we have increased our investment in R&D that will enable simpler, more "service-friendly" networks with more intelligence in every layer. Bell Labs R&D also includes optical network technologies, packet data solutions, circuit-to-packet network migrations, spread-spectrum wireless technologies, and network operation and management software.
  The breadth and depth of experience that the people of Bell Labs bring to the table are unrivaled in the industry. Perhaps that is why - more often than not - Bell Labs provides the vision and sets the pace for the entire communications industry. Our scientists and engineers are constantly pushing the envelope of what's possible in communications.
  Bell Labs is so productive it receives about two patents per working day. Yet what Bell Labs brings to Lucent and its customers is more than a knack for creating new technologies. Customers' needs for technology integration, for network planning, optimization, and management have never been greater. And Bell Labs, which pioneered systems engineering and many areas of operations research, answers this call with its deep understanding of how large, complex networks fit together.
  Shaping the Future
  Past Bell Labs breakthroughs - like transistors, lasers and digital encryption - are the basis of today's communications industry. The innovations coming out of Bell Labs today are laying the foundation for tomorrow's networks. Examples include:
  Softswitches, the "brains" of a new network architecture that enables service providers to quickly introduce new services and manage the convergence of voice and data traffic on their networks.
  Smart antennas and other wireless technologies that can reduce equipment size, cost, and power requirements.
  Raman and L-band amplifiers, which expand the capacity of optical networks.
  Software and technology that can shorten service-creation intervals, improve customer relationships, reduce costs, and optimize networks.
  Research
  While the vast majority - more than 90 percent - of the scientists and engineers at Bell Labs are applying their considerable talents to the needs of service providers, it is prudent to maintain a broader technical capability than what is currently required by our customers. For this reason, Bell Labs maintains a small, but prolific, long-term research program. That research explores such areas as the future of wireless and optical networking, the Internet, multimedia communications, physics and mathematics. Lucent's investment in long-term research provides the "seed corn" to ensure that we maintain a leading position in technologies critical to our future.
  In the last few months, this research program has produced:
  The world's first semiconductor laser that emits light continuously and reliably over a broad spectrum of infrared wavelengths.
  The discovery that crystals in the skeletons of marine creatures called brittlestars act as optical receptors. By studying them, we might learn how to design better optical elements for telecommunications networks.
  Improved superconducting materials.
资源下载链接为: https://pan.quark.cn/s/140386800631 通用大模型文本分类实践的基本原理是,借助大模型自身较强的理解和推理能力,在使用时需在prompt中明确分类任务目标,并详细解释每个类目概念,尤其要突出类目间的差别。 结合in-context learning思想,有效的prompt应包含分类任务介绍及细节、类目概念解释、每个类目对应的例子和待分类文本。但实际应用中,类目和样本较多易导致prompt过长,影响大模型推理效果,因此可先通过向量检索缩小范围,再由大模型做最终决策。 具体方案为:离线时提前配置好每个类目的概念及对应样本;在线时先对给定query进行向量召回,再将召回结果交给大模型决策。 该方法不更新任何模型参数,直接使用开源模型参数。其架构参考GPT-RE并结合相关实践改写,加入上下文学习以提高准确度,还使用BGE作为向量模型,K-BERT提取文本关键词,拼接召回的相似例子作为上下文输入大模型。 代码实现上,大模型用Qwen2-7B-Instruct,Embedding采用bge-base-zh-v1.5,向量库选择milvus。分类主函数的作用是在向量库中召回相似案例,拼接prompt后输入大模型。 结果方面,使用ICL时accuracy达0.94,比bert文本分类的0.98低0.04,错误类别6个,处理时添加“家居”类别,影响不大;不使用ICL时accuracy为0.88,错误58项,可能与未修改prompt有关。 优点是无需训练即可有较好结果,例子优质、类目限清晰时效果更佳,适合围绕通用大模型api打造工具;缺点是上限不高,仅针对一个分类任务部署大模型不划算,推理速度慢,icl的token使用多,用收费api会有额外开销。 后续可优化的点是利用key-bert提取的关键词,因为核心词语有时比语意更重要。 参考资料包括
内容概要:本文详细介绍了哈希表及其相关概念和技术细节,包括哈希表的引入、哈希函数的设计、冲突处理机制、字符串哈希的基础、哈希错误率分析以及哈希的改进与应用。哈希表作为一种高效的数据结构,通过键值对存储数据,能够快速定位和检索。文中讨论了整数键值和字符串键值的哈希方法,特别是字符串哈希中的多项式哈希及其优化方法,如双哈希和子串哈希的快速计算。此外,还探讨了常见的冲突处理方法——拉链法和闭散列法,并提供了C++实现示例。最后,文章列举了哈希在字符串匹配、最长回文子串、最长公共子字符串等问题中的具体应用。 适合人群:计算机科学专业的学生、算法竞赛选手以及有一定编程基础并对数据结构和算法感兴趣的开发者。 使用场景及目标:①理解哈希表的工作原理及其在各种编程任务中的应用;②掌握哈希函数的设计原则,包括如何选择合适的模数和基数;③学会处理哈希冲突的方法,如拉链法和闭散列法;④了解并能运用字符串哈希解决实际问题,如字符串匹配、回文检测等。 阅读建议:由于哈希涉及较多数学知识和编程技巧,建议读者先熟悉基本的数据结构和算法理论,再结合代码实例进行深入理解。同时,在实践中不断尝试不同的哈希策略,对比性能差异,从而更好地掌握哈希技术。
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