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🔗 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.

🔗 Computer science 🔗 Science Fiction 🔗 Systems

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence.

🔗 Computer science

In computational learning theory, induction of regular languages refers to the task of learning a formal description (e.g. grammar) of a regular language from a given set of example strings. Although Mark E. Gold has shown that not every regular language can be learned this way (see language identification in the limit), approaches have been investigated for a variety of subclasses. They are sketched in this article. For learning of more general grammars, see Grammar induction.

🔗 Computer science

Bead sort, also called gravity sort, is a natural sorting algorithm, developed by Joshua J. Arulanandham, Cristian S. Calude and Michael J. Dinneen in 2002, and published in The Bulletin of the European Association for Theoretical Computer Science. Both digital and analog hardware implementations of bead sort can achieve a sorting time of O(n); however, the implementation of this algorithm tends to be significantly slower in software and can only be used to sort lists of positive integers. Also, it would seem that even in the best case, the algorithm requires O(n²) space.

🔗 Computer science

The circle–ellipse problem in software development (sometimes called the square–rectangle problem) illustrates several pitfalls which can arise when using subtype polymorphism in object modelling. The issues are most commonly encountered when using object-oriented programming (OOP). By definition, this problem is a violation of the Liskov substitution principle, one of the SOLID principles.

The problem concerns which subtyping or inheritance relationship should exist between classes which represent circles and ellipses (or, similarly, squares and rectangles). More generally, the problem illustrates the difficulties which can occur when a base class contains methods which mutate an object in a manner which may invalidate a (stronger) invariant found in a derived class, causing the Liskov substitution principle to be violated.

The existence of the circle–ellipse problem is sometimes used to criticize object-oriented programming. It may also imply that hierarchical taxonomies are difficult to make universal, implying that situational classification systems may be more practical.

🔗 Computer science 🔗 Computer science/Computing

Lucid is a dataflow programming language designed to experiment with non-von Neumann programming models. It was designed by Bill Wadge and Ed Ashcroft and described in the 1985 book Lucid, the Dataflow Programming Language.

pLucid was the first interpreter for Lucid.