历届图灵奖获得者(1966-2003)

图灵奖是美国计算机协会在计算机技术方面的最高奖项,以Alan Turing先生命名。本文列出了1966 - 2003年历届图灵奖获得者名单,介绍了他们的获奖原因,涉及编程技术、人工智能、数据库、算法等多个计算机技术领域。

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历届图灵奖获得者(1966-2003)
Rick @ 2004-07-15 01:16

图灵奖最早设立于1966年,是美国计算机协会在计算机技术方面所授予的最高奖项, 被喻为计算机界的诺贝尔奖. 它是以英国数学天才Alan Turing先生的名字命名的, Alan Turing先生对早期计算的理论和实践做出了突出的贡献.图灵奖主要授予在计算机技术领域做出突出贡献的个人. 而这些贡献必须对计算机业有长远而重要的影响.  

历届图灵奖获得者名单:  

1966 A. J. Perlis --- PhD, MIT; Prof, Yale (was Prof at CMU) (deceased)  
因在新一代编程技术和编译架构方面的贡献而获奖.  

1967 Maurice V. Wilkes --- PhD, Cambridge; Prof, Cambridge  
因设计出第一台程序实现完全内存的计算机而获奖.  

1968 Richard W. Hamming --- PhD, UIUC; Prof, Naval Postgraduate School (was at Bell) (deceased)  
因在计数方法、自动编码系统、检测及纠正错码方面的贡献被授予图灵奖.  

1969 Marvin Minsky --- PhD, Princeton, Prof, MIT  
因对人工智能的贡献被授予图灵奖.  

1970 J.H. Wilkinson --- BS, Cambridge; staff, National Physical Laboratory, London  
因在利用数值分析方法来促进高速数字计算机的应用方面的研究而获奖.  

1971 John McCarthy --- PhD, Princeton; Prof, Stanford  
因对人工智能的贡献被授予图灵奖.  

1972 Edsger W. Dijkstra --- PhD, U Amsterdam; Prof, UT Austin  
因在编程语言方面的出众表现而获奖.  

1973 Charles W. Bachman --- staff, Honeywell  
因在数据库方面的杰出贡献而获奖.  

1974 Donald E. Knuth --- PhD, Caltech; Prof, Stanford  
因设计和完成TAOCP(一种创新的具有很高排版质量的文档制作工具)而被授予该奖.  

1975 Allen Newell --- PhD, Stanford; Prof, CMU (deceased)  
和Herbert A. Simon --- PhD, Chicago; Prof, CMU (deceased)  
因在人工智能、人类识别心理和表处理的基础研究而获奖.  

1976 Michael O. Rabin --- PhD, Princeton; Prof, Harvard  
和Dana S. Scott --- PhD, Princeton; Prof, CMU  
因他们的论文"有限自动机与它们的决策问题"中所提出的非决定性机器这一很有价值的概念而获奖.  

1977 John Backus --- BS, Columbia; staff, IBM  
因对可用的高级编程系统设计有深远和重大的影响而获奖.  

1978 Robert W. Floyd --- BS, Chicago; Prof, Stanford  
因其在软件编程的算法方面的影响,并开创了包括剖析理论、编程语言的语义、自动程序检验、自动程序合成和算法分析在内的多项计算机子学科而被授予该奖.  

1979 Kenneth E. Iverson  
因对程序设计语言理论、互动式系统及APL的贡献被授予该奖.  

1980 C. Anthony R. Hoare --- Prof, Oxford(now at Microsoft)  
因对程序设计语言的定义和设计所做的贡献而获奖.  

1981 Edgar F. Codd --- PhD, Michigan; staff, IBM  
因在数椐库管理系统的理论和实践方面的贡献而获奖.  

1982 Steven A. Cook --- PhD, Harvard; Prof, U Toronto  
因奠定了NP-Completeness理论的基础而获奖.  

1983 Ken Thompson --- MS, Berkeley; staff, Bell-Labs  
和Dennis M. Ritchie --- PhD, Harvard; staff, Bell-Labs  
因在类属操作系统理论,特别是UNIX操作系统的推广而获奖.  

1984 Niklaus Wirth --- PhD, Berkeley; Prof, ETH Zurich  
因开发了EULER、 ALGOL-W、 MODULA和PASCAL一系列崭新的计算语言而获奖.  

1985 Richard M. Karp --- PhD, Harvard; Prof, Berkeley  
因对算法理论的贡献而获奖.  

1986 John E. Hopcroft --- PhD, Stanford; Prof, Cornell  
and Robert E. Tarjan --- PhD, Stanford; Prof, Princeton  
因在算法及数据结构的设计和分析中所取得的决定性成果而获奖.  

1987 John Cocke --- staff, IBM  
因在面向对象的编程语言和相关的编程技巧方面的贡献而获奖.  

1988 Ivan E. Sutherland --- PhD, MIT; staff, Sun  
因在计算机图形学方面的贡献而获奖.  

1989 William V. Kahan --- PhD, U Toronto; Prof, Berkeley  
因在数值分析方面的贡献而获奖,他是是浮点计算领域的专家.  

1990 Fernando J. Corbato --- PhD, MIT; Prof, MIT  
因在开发大型多功能、可实现时间和资源共享的计算系统,如CTSS和Multics方面的贡献而获奖.  

1991 Robin Milner --- Prof, Cambridge (was at U Edinburgh)  
因在可计算的函数的逻辑(LCF)、ML和并行理论(CCS)这三个方面的贡献而获奖.  

1992 Butler Lampson --- PhD, Berkeley; staff, Microsoft  
因在个人分布式计算机系统(包括操作系统)方面的贡献而获奖.  

1993 Juris Hartmanis --- PhD, Caltech; Prof, Cornell  
和 Richard E. Stearns --- PhD, Princeton; Prof, SUNY Albany  
因奠定了计算复杂性理论的基础而获奖.  

1994 Raj Reddy --- PhD, Stanford; Prof, CMU  
和 Edward Feigenbaum (PhD, CMU; Prof, Stanford)  
因对大型人工智能系统的开拓性研究而获奖.  

1995 Manuel Blum --- PhD, MIT; Prof, Berkeley  
因奠定了计算复杂性理论的基础和在密码术及程序校验方面的贡献而获奖.  

1996 Amir Pnueli --- PhD, Weizmann Institute; Prof, NYU  
因在计算中引入Temporal逻辑和对程序及系统检验的贡献被获奖.  

1997 Douglas Engelbart --- PhD, Berkeley; staff, SRI  
因提出互动式计算概念并创造出实现这一概念的重要技术而获奖.  

1998 James Gray --- PhD, Berkeley; staff, Microsoft  
因在数据库和事务处理方面的突出贡献而获奖.  

1999 Frederick P. Brooks, Jr.--- PhD, Harvard; Prof, UNC  
因对计算机体系结构和操作系统以及软件工程做出了里程碑式的贡献.  

2000 Andrew Chi-Chih Yao --- PhD, UIUC; Prof, Princeton (now at 清华)因对计算理论做出了诸多根本性的重大贡献. (图灵奖自创立以来获得该奖项的首位华裔学者,全球华人的骄傲)  

2001 Ole-Johan Dahl, and Kristen Nygaard --- Profs, U Oslo  
因他们在设计编程语言SIMULA I 和SIMULA 67时产生的基础性想法,这些想法是面向对象技术的肇始.  

2002 Ronald L. Rivest, Adi Shamir, Leonard M. Adelman-Ronald L. Rivest: PhD, Stanford; MIT  Adi Shamir: PhD, Weizmann; Weizmann  
Leonard M. Adelman: PhD, Berkeley; USC  因他们在公共密匙算法上所做的杰出贡献(RSA算法是当前在互联网传输、银行以及信用卡产业中被广泛使用的安全基本机制).  

2003 Alan Kay --- PhD, Utah; HP Labs (was at Xerox PARC)  
因发明第一个完全面向对象的动态计算机程序设计语言Smalltalk.

### MCP in Python Usage and Implementation #### Overview of MCP in Python The Model Context Protocol (MCP) is a protocol designed to facilitate interactions between AI models and external tools, data sources, or APIs[^3]. In the context of Python, MCP can be implemented using the MCP Python SDK, which provides tools for building both servers and clients. This implementation allows developers to interact with MCP bridges or agents effectively. #### Installing MCP Python SDK To integrate MCP into Python projects, the MCP Python SDK can be installed via pip: ```bash pip install mcp ``` This command installs the necessary libraries for interacting with MCP servers or clients[^1]. #### Configuring MCP Server in Python A MCP server can be configured in Python by defining its behavior and endpoints. Below is an example of setting up a basic MCP server using Python: ```python from mcp.server import MCPServer def handle_request(data): # Process incoming request data return {"result": "Processed"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` In this example, the `MCPServer` class initializes a server that listens on port 8080 and processes incoming requests by calling the `handle_request` function[^1]. #### Configuring MCP Client in Python For interacting with an existing MCP server, a client can be set up as follows: ```python from mcp.client import MCPClient client = MCPClient(mcp_url="http://localhost:8080", mcp_port=8080) response = client.send_request({"action": "fetch_data"}) print(response) ``` Here, the `MCPClient` sends a request to the MCP server at the specified URL and port, and retrieves the response[^2]. #### Advanced Configuration Options MCP servers and clients can be further customized with additional parameters such as JSON formatting, logging levels, and security settings. For instance: ```python client = MCPClient( mcp_url="http://localhost:8080", mcp_port=8080, hide_json=True, json_width=120 ) ``` This configuration hides JSON results from tool executions and sets the maximum width for JSON output to 120 characters. #### Integration with Databases MCP can also be integrated with databases to enhance data retrieval and model interaction. This approach offers advantages over traditional RAG methods by providing more efficient and precise data access[^4]. An example of integrating MCP with a database might look like this: ```python from mcp.server import MCPServer import sqlite3 def fetch_data_from_db(query): conn = sqlite3.connect("example.db") cursor = conn.cursor() cursor.execute(query) result = cursor.fetchall() conn.close() return result def handle_request(data): query = data.get("query") if query: return {"data": fetch_data_from_db(query)} return {"error": "No query provided"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` This script sets up an MCP server that executes SQL queries against a SQLite database[^4]. #### Best Practices for MCP Implementation - Ensure secure communication between MCP clients and servers using authentication mechanisms. - Optimize performance by configuring appropriate logging levels and resource limits. - Test the MCP implementation thoroughly to handle edge cases and errors gracefully.
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