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🔗 Computer science

The General Purpose Analog Computer (GPAC) is a mathematical model of analog computers first introduced in 1941 by Claude Shannon. This model consists of circuits where several basic units are interconnected in order to compute some function. The GPAC can be implemented in practice through the use of mechanical devices or analog electronics. Although analog computers have fallen almost into oblivion due to emergence of the digital computer, the GPAC has recently been studied as a way to provide evidence for the physical Church–Turing thesis. This is because the GPAC is also known to model a large class of dynamical systems defined with ordinary differential equations, which appear frequently in the context of physics. In particular it was shown in 2007 that (a deterministic variant of) the GPAC is equivalent, in computability terms, to Turing machines, thereby proving the physical Church–Turing thesis for the class of systems modelled by the GPAC. This was recently strengthened to polynomial time equivalence.

🔗 Computer science 🔗 Mathematics 🔗 Systems 🔗 Cryptography 🔗 Cryptography/Computer science 🔗 Systems/Operations research

The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively.

The knapsack problem has been studied for more than a century, with early works dating as far back as 1897. The name "knapsack problem" dates back to the early works of mathematician Tobias Dantzig (1884–1956), and refers to the commonplace problem of packing the most valuable or useful items without overloading the luggage.

🔗 Computing 🔗 Computer science 🔗 Mathematics

The P versus NP problem is a major unsolved problem in computer science. It asks whether every problem whose solution can be quickly verified can also be solved quickly.

It is one of the seven Millennium Prize Problems selected by the Clay Mathematics Institute, each of which carries a US$1,000,000 prize for the first correct solution.

The informal term quickly, used above, means the existence of an algorithm solving the task that runs in polynomial time, such that the time to complete the task varies as a polynomial function on the size of the input to the algorithm (as opposed to, say, exponential time). The general class of questions for which some algorithm can provide an answer in polynomial time is called "class P" or just "P". For some questions, there is no known way to find an answer quickly, but if one is provided with information showing what the answer is, it is possible to verify the answer quickly. The class of questions for which an answer can be verified in polynomial time is called NP, which stands for "nondeterministic polynomial time".

An answer to the P = NP question would determine whether problems that can be verified in polynomial time can also be solved in polynomial time. If it turned out that P ≠ NP, which is widely believed, it would mean that there are problems in NP that are harder to compute than to verify: they could not be solved in polynomial time, but the answer could be verified in polynomial time.

Aside from being an important problem in computational theory, a proof either way would have profound implications for mathematics, cryptography, algorithm research, artificial intelligence, game theory, multimedia processing, philosophy, economics and many other fields.

🔗 Computer science

HAKMEM, alternatively known as AI Memo 239, is a February 1972 "memo" (technical report) of the MIT AI Lab containing a wide variety of hacks, including useful and clever algorithms for mathematical computation, some number theory and schematic diagrams for hardware — in Guy L. Steele's words, "a bizarre and eclectic potpourri of technical trivia". Contributors included about two dozen members and associates of the AI Lab. The title of the report is short for "hacks memo", abbreviated to six upper case characters that would fit in a single PDP-10 machine word (using a six-bit character set).

🔗 Computing 🔗 Computer science 🔗 Mathematics

A* (pronounced "A-star") is a graph traversal and path search algorithm, which is often used in computer science due to its completeness, optimality, and optimal efficiency. One major practical drawback is its ${\displaystyle O(b^{d})}$ space complexity, as it stores all generated nodes in memory. Thus, in practical travel-routing systems, it is generally outperformed by algorithms which can pre-process the graph to attain better performance, as well as memory-bounded approaches; however, A* is still the best solution in many cases.

Peter Hart, Nils Nilsson and Bertram Raphael of Stanford Research Institute (now SRI International) first published the algorithm in 1968. It can be seen as an extension of Edsger Dijkstra's 1959 algorithm. A* achieves better performance by using heuristics to guide its search.

🔗 International relations 🔗 Technology 🔗 Internet 🔗 Computing 🔗 Computer science 🔗 Law 🔗 Business 🔗 Politics 🔗 Robotics 🔗 International relations/International law 🔗 Futures studies 🔗 European Union 🔗 Science Policy 🔗 Artificial Intelligence

The Artificial Intelligence Act (AI Act) is a European Union regulation concerning artificial intelligence (AI).

It establishes a common regulatory and legal framework for AI in the European Union (EU). Proposed by the European Commission on 21 April 2021, and then passed in the European Parliament on 13 March 2024, it was unanimously approved by the Council of the European Union on 21 May 2024. The Act creates a European Artificial Intelligence Board to promote national cooperation and ensure compliance with the regulation. Like the EU's General Data Protection Regulation, the Act can apply extraterritorially to providers from outside the EU, if they have users within the EU.

It covers all types of AI in a broad range of sectors; exceptions include AI systems used solely for military, national security, research and non-professional purposes. As a piece of product regulation, it would not confer rights on individuals, but would regulate the providers of AI systems and entities using AI in a professional context. The draft Act was revised following the rise in popularity of generative AI systems, such as ChatGPT, whose general-purpose capabilities did not fit the main framework. More restrictive regulations are planned for powerful generative AI systems with systemic impact.

The Act classifies AI applications by their risk of causing harm. There are four levels – unacceptable, high, limited, minimal – plus an additional category for general-purpose AI. Applications with unacceptable risks are banned. High-risk applications must comply with security, transparency and quality obligations and undergo conformity assessments. Limited-risk applications only have transparency obligations and those representing minimal risks are not regulated. For general-purpose AI, transparency requirements are imposed, with additional evaluations when there are high risks.

La Quadrature du Net (LQDN) stated that the adopted version of the AI Act would be ineffective, arguing that the role of self-regulation and exemptions in the act rendered it "largely incapable of standing in the way of the social, political and environmental damage linked to the proliferation of AI".

🔗 Computer science

Hoare logic (also known as Floyd–Hoare logic or Hoare rules) is a formal system with a set of logical rules for reasoning rigorously about the correctness of computer programs. It was proposed in 1969 by the British computer scientist and logician Tony Hoare, and subsequently refined by Hoare and other researchers. The original ideas were seeded by the work of Robert W. Floyd, who had published a similar system for flowcharts.

🔗 Computing 🔗 Computer science 🔗 Lists 🔗 Science Fiction

Computers have often been used as fictional objects in literature, movies and in other forms of media. Fictional computers tend to be considerably more sophisticated than anything yet devised in the real world.

This is a list of computers that have appeared in notable works of fiction. The work may be about the computer, or the computer may be an important element of the story. Only static computers are included. Robots and other fictional computers that are described as existing in a mobile or humanlike form are discussed in a separate list of fictional robots and androids.

🔗 Computing 🔗 Computer science

Ousterhout's dichotomy is computer scientist John Ousterhout's categorization that high-level programming languages tend to fall into two groups, each with distinct properties and uses: system programming languages and scripting languages – compare programming in the large and programming in the small. This distinction underlies the design of his language Tcl.

System programming languages (or applications languages) usually have the following properties:

• They are typed statically
• They support creating complex data structures
• Programs in them are compiled into machine code
• Programs in them are meant to operate largely independently of other programs

System programming languages tend to be used for components and applications with large amounts of internal functionality such as operating systems, database servers, and Web browsers. These applications typically employ complex algorithms and data structures and require high performance. Prototypical examples of system programming languages include C and Modula-2.

By contrast, scripting languages (or glue languages) tend to have the following properties:

• They are typed dynamically
• They have little or no provision for complex data structures
• Programs in them (scripts) are interpreted

Scripting languages tend to be used for applications where most of the functionality comes from other programs (often implemented in system programming languages); the scripts are used to glue together other programs or add additional layers of functionality on top of existing programs. Ousterhout claims that scripts tend to be short and are often written by less sophisticated programmers, so execution efficiency is less important than simplicity and ease of interaction with other programs. Common applications for scripting include Web page generation, report generation, graphical user interfaces, and system administration. Prototypical examples of scripting languages include AppleScript, C shell, DOS batch files, and Tcl.

🔗 Computer science 🔗 Time

In computer science, time formatting and storage bugs are a class of software bugs which may cause time and date calculation or display to be improperly handled. These are most commonly manifestations of arithmetic overflow, but can also be the result of other issues. The most well-known consequence of bugs of this type is the Y2K problem, but many other milestone dates or times exist that have caused or will cause problems depending on various programming deficiencies.