# 2_gb_limit
The 2 GB limit refers to a physical memory barrier for a process running on a 32-bit operating system, which can only use a maximum of 2 GB of memory
The problem mainly affects 32-bit versions of operating systems like Microsoft Windows and Linux, although some variants of the latter can overcome this barrier
It is also found in servers like FTP servers or embedded systems like Xbox
The use of Physical Address Extension (PAE) can overcome this barrier by extending the addresses used to represent mappings between virtual and physical memory
The limit is created by the 32-bit integer limit (2,147,483,647), which is the largest possible integer that can be represented by 32 binary digits
This is known as 4-gigabyte tuning (4GT), or the /3GB switch
Once enabled, executables can have the "large address aware" flag set to increase their memory limit to 3 GB
32-bit processes on 64-bit Windows are also limited to 2 GB
However, they can use the "large address aware" flag as well, except that it doesn't require the /3GB switch and increases the limit to 4 GB
# 2025_present_global_memory_supply_shortage
A global computer memory supply shortage started in 2025 due to supply constraints and rapid price escalation in the semiconductor memory market, particularly affecting DRAM and NAND flash integrated circuits
This shortage has been referred to in media outlets as "RAMmageddon" or the "RAMpocalypse"
According to a 2026 Kearney's PERLab analysis, the shortage is expected to last at least until 2030
In June 2026, Micron Technology CEO Sanjay Mehrotra expected the shortage to last through 2027, and supply to gradually improve by 2028
# 3_gb_barrier
In computing, the term 3 GB barrier refers to a limitation of some 32-bit operating systems running on x86 microprocessors
It prevents the operating systems from using all of 4 GiB (4 × 10243 bytes) of main memory
The exact barrier varies by motherboard and I/O device configuration, particularly the size of video RAM; it may be in the range of 2.75 GB to 3.5 GB
Whatever the actual position of the "barrier", there is no code in operating system software nor any hardware architectural limit that directly imposes it
Rather, the "barrier" is the result of interactions between several aspects of both
# 3d_xpoint
3D XPoint (pronounced three-D cross point) is a discontinued non-volatile memory (NVM) technology developed jointly by Intel and Micron Technology
It was announced in July 2015 and was available on the open market under the brand name Optane (Intel) from April 2017 to July 2022
Bit storage is based on a change of bulk resistance, in conjunction with a stackable cross-grid data access array, using a technology known as Ovonic Threshold Switch (OTS)
Initial prices were less than dynamic random-access memory (DRAM) but more than flash memory
As a non-volatile memory, 3D XPoint had a number of features that distinguish it from other currently available RAM and NVRAM
As the memory was inherently fast and byte-addressable, techniques such as read-modify-write and caching used to enhance traditional SSDs are not needed to obtain high performance
# 6264
The 6264 is a JEDEC-standard static RAM integrated circuit
It is produced by a wide variety of different vendors, including Hitachi, Hynix, and Cypress Semiconductor
It is available in a variety of different configurations, such as DIP, SPDIP, and SOIC
Some versions of the 6264 can run in ultra-low-power mode and retain memory when not in use, thus making them suitable for battery backup applications
# acid
In computer science, ACID (atomicity, consistency, isolation, durability) is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps
For example, a transfer of funds from one bank account to another, involving multiple changes such as debiting one account and crediting another, is a single transaction
These four properties are the major guarantees of the transaction paradigm, which has influenced many aspects of development in database systems
According to Gray and Reuter, the IBM Information Management System supported ACID transactions as early as 1973 (although the acronym was created later)
BASE stands for basically available, soft state, and eventually consistent: the acronym highlights that BASE is opposite of ACID, like their chemical equivalents, acid and base
A database either leans towards ACID or BASE, but it cannot be both (according to CAP theorem)
For example, SQL databases (like MySQL, PostgreSQL, AWS RedShift) are structured over the ACID model, while NoSQL databases (like DynamoDB or MongoDB) use the BASE architecture
However, some NoSQL databases might exhibit certain ACID traits
# altq
ALTQ (ALTernate Queueing) is the network scheduler for Berkeley Software Distribution
ALTQ provides queueing disciplines, and other components related to quality of service (QoS), required to realize resource sharing
It is most commonly implemented on BSD-based routers
With ALTQ, packets can be assigned to queues for the purpose of bandwidth control
The scheduler defines the algorithm used to decide which packets get delayed, dropped or sent out immediately
There are five schedulers currently supported in the FreeBSD implementation of ALTQ: cbq — Class-based Queueing
Queues attached to an interface build a tree, thus each queue can have further child queues
Each queue can have a priority and a bandwidth assigned
Priority mainly controls the time packets take to get sent out, while bandwidth has primarily effects on throughput
Attempts to fairly distribute bandwidth among all connections
hfsc — Hierarchical Fair Service Curve
Queues attached to an interface build a tree, thus each queue can have further child queues
Each queue can have a priority and a bandwidth assigned
Priority mainly controls the time packets take to get sent out, while bandwidth has primarily effects on throughput
Queues are flat attached to the interface, thus, queues cannot have further child queues
Each queue has a unique priority assigned, ranging from 0 to 15
Packets in the queue with the highest priority are processed first
# avt_statistical_filtering_algorithm
AVT Statistical filtering algorithm is an approach to improving quality of raw data collected from various sources
It is most effective in cases when there is inband noise present
In those cases AVT is better at filtering data then, band-pass filter or any digital filtering based on variation of
Conventional filtering is useful when signal/data has different frequency than noise and signal/data is separated/filtered by frequency discrimination of noise
Frequency discrimination filtering is done using Low Pass, High Pass and Band Pass filtering which refers to relative frequency filtering criteria target for such configuration
Those filters are created using passive and active components and sometimes are implemented using software algorithms based on Fast Fourier transform (FFT)
AVT filtering is implemented in software and its inner working is based on statistical analysis of raw data
When signal frequency/(useful data distribution frequency) coincides with noise frequency/(noisy data distribution frequency) we have inband noise
In this situations frequency discrimination filtering does not work since the noise and useful signal are indistinguishable and where AVT excels
To achieve filtering in such conditions there are several methods/algorithms available which are briefly described below
# abstract_family_of_acceptors
An abstract family of acceptors (AFA) is a grouping of generalized acceptors
Informally, an acceptor is a device with a finite state control, a finite number of input symbols, and an internal store with a read and write function
Each acceptor has a start state and a set of accepting states
The device reads a sequence of symbols, transitioning from state to state for each input symbol
If the device ends in an accepting state, the device is said to accept the sequence of symbols
A family of acceptors is a set of acceptors with the same type of internal store
The study of AFA is part of AFL (abstract families of languages) theory
# abstract_rewriting_system
Despite its simplicity, an ARS is sufficient to describe important properties of rewriting systems like normal forms, termination, and various notions of confluence
Historically, there have been several formalizations of rewriting in an abstract setting, each with its idiosyncrasies
This is due in part to the fact that some notions are equivalent, see below in this article
The formalization that is most commonly encountered in monographs and textbooks, and which is generally followed here, is due to Gérard Huet (1980)
# abstract_semantic_graph
An ASG is at a higher level of abstraction than an abstract syntax tree (or AST), which is used to express the syntactic structure of an expression or program
ASGs are more complex and concise than ASTs because they may contain shared subterms (also known as "common subexpressions")
Abstract semantic graphs are often used as an intermediate representation by compilers to store the results of performing common subexpression elimination upon abstract syntax trees
ASTs are trees and are thus incapable of representing shared terms
ASGs are usually directed acyclic graphs (DAG), although in some applications graphs containing cycles may be permitted
For example, a graph containing a cycle might be used to represent the recursive expressions that are commonly used in functional programming languages as non-looping iteration constructs
The mutability of these types of graphs, is studied in the field of graph rewriting
Abstract syntax trees are not capable of sharing subexpression nodes because it is not possible for a node in a proper tree to have more than one parent
Although this conceptual simplicity is appealing, it may come at the cost of redundant representation and, in turn, possibly inefficiently duplicating the computation of identical terms
For this reason ASGs are often used as an intermediate language at a subsequent compilation stage to abstract syntax tree construction via parsing
An abstract semantic graph is typically constructed from an abstract syntax tree by a process of enrichment and abstraction
The enrichment can for example be the addition of back-pointers, edges from an identifier node (where a variable is being used) to a node representing the declaration of that variable
The abstraction can entail the removal of details which are relevant only in parsing, not for semantics
# abstract_syntax_tree
An abstract syntax tree (AST) is a tree data structure used in computer science to represent the abstract syntactic structure of text, often source code, written in a formal language
Each node of the tree denotes a construct occurring in the text
It is sometimes called simply a syntax tree
The syntax is "abstract" in the sense that it does not represent every detail of the concrete syntax, but instead represents the essential structural information of the text
For example, grouping parentheses need not be represented as separate nodes because their effect is implicit in the tree structure
Similarly, punctuation and delimiters such as braces, semicolons, and parentheses may be omitted from an AST
This distinguishes an abstract syntax tree from a concrete syntax tree, traditionally called a parse tree
During syntax analysis, a parser may construct an AST representing the syntactic structure of the input program
The AST can then serve as an intermediate representation for subsequent compiler phases, which may use information in the AST, annotate it with additional information, or transform it
Abstract syntax trees are also used in program analysis and program transformation systems
# action_algebra
In algebraic logic, an action algebra is an algebraic structure which is both a residuated semilattice and a Kleene algebra
Unlike dynamic logic and other modal logics of programs, for which programs and propositions form two distinct sorts, action algebra combines the two into a single sort
It can be thought of as a variant of intuitionistic logic with star and with a noncommutative conjunction whose identity need not be the top element
Unlike Kleene algebras, action algebras form a variety, which furthermore is finitely axiomatizable, the crucial axiom being a•(a → a)* ≤ a
Unlike models of the equational theory of Kleene algebras (the regular expression equations), the star operation of action algebras is reflexive transitive closure in every model of the equations
Action algebras were introduced by Vaughan Pratt in the European Workshop JELIA'90
# active_data_structure
An active data structure is a data structure with an associated thread or process that performs internal operations
More specifically, an active data structure is associated with a computing resource, which contains one or more concurrently executing processes, and data associated with those processes
Communication is modeled using remote procedure calls, as opposed to shared memory or message passing
The active data structure's internals are hidden behind its RPC interface, and may be accessed concurrently
Common examples include databases and file systems
Active data structures can perform maintenance when resources would otherwise be idle, and present multiple views of the data
# adaptive_algorithm
An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and on a priori defined reward mechanism (or criterion)
Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning
For example, stable partition, using no additional memory is O(n lg n) but given O(n) memory, it can be O(n) in time
Another example is adaptive sort, whose behavior changes upon the presortedness of its input
An example of an adaptive algorithm in radar systems is the constant false alarm rate (CFAR) detector
Examples include adaptive simulated annealing, adaptive coordinate descent, adaptive quadrature, AdaBoost, Adagrad, Adadelta, RMSprop, and Adam
# affix_grammar
An affix grammar is a two-level grammar formalism used to describe the syntax of languages, mainly computer languages, using an approach based on how natural language is typically described
The formalism was invented in 1962 by Lambert Meertens while developing a grammar for generating English sentences
1 in C major for 2 violins, viola and violoncello, based on the first non-context-free affix grammar
The string quartet was published in 1968, as Mathematical Centre Report MR 96
The grammatical rules of an affix grammar are those of a context-free grammar, except that certain parts in the nonterminals (the affixes) are used as arguments
If the same affix occurs multiple times in a rule, its value must agree, i.e
it must be the same everywhere
In some types of affix grammar, more complex relationships between affix values are possible
# agent_communications_language
Agent Communication Language (ACL) consists of computer communication protocols that are intended for AI agents to communicate with each other
A new protocol to emerge in this area is Natural Language Interaction Protocol (NLIP)
NLIP is an application-level communication protocol defined between AI Agents or between a human and an AI agent
As a result, we can define agent communication protocols into two categories: ontology based agent communication protocols and generative AI based agent communication protocols
Ontology based agent communication protocols use a common ontology to be used between agents
An ontology is a part of the agent's knowledge base that describes what kind of things an agent can deal with and how they are related to each other
FIPA-ACL and KQML are examples of such protocols
These protocols rely on speech act theory developed by Searle in the 1960s and enhanced by Winograd and Flores in the 1970s
They define a set of performatives, also called Communicative Acts, and their meaning (e.g
The content of the performative is not standardized, but varies from system to system
Implementation support of FIPA-ACL is included in FIPA-OS and Jade
Generative AI based agent communication protocols such as NLIP do not require a shared ontology among communicating agents
In its stead, they use generative AI models to translate natural language text, images, videos or other modalities of data into a local ontology
This provides for hot-extensibility where the same protocol can be used for multiple communication needs, and simplifies version control since different agents can use different versions of a shared ontology
NLIP has been designed with security considerations in mind
The specification and standards comprising NLIP are developed and maintained by Ecma Technical Community 56
Another emerging approach is Inthon, a domain-specific programming language layer designed for AI-native workflows and agent orchestration
Unlike traditional communication protocols that focus on inter-agent messaging, Inthon provides a grammar for expressing agent execution intent, including tool calls, memory operations, and policy constraints, within a sandboxed environment
It is implemented as a Python-hosted language and includes features such as a capability-based security model and a bytecode virtual machine for executing agent logic
While not a communication protocol per se, Inthon's design addresses challenges in how agents specify and execute their plans, which includes interactions with external tools and services
# algorithm
Algorithms are used as specifications for performing calculations and data processing
More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning)
In contrast, a heuristic is an approach to solving problems without well-defined correct or optimal results
For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation
As an effective method, an algorithm can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function
Starting from an initial state and input, a computation occurs at each step, eventually producing output and terminating
The transition between states can be non-deterministic; randomized algorithms incorporate random input
# algorithmic_amplification
Algorithmic amplification describes increases in the distribution or visibility of content on digital platforms, arising from a combination of automated ranking by recommendation systems and users' own sharing
Definitions differ over the baseline used to determine whether content has been amplified, and separating the contribution of ranking from that of users is a recurring difficulty in the research
Major platforms, including Facebook, YouTube, TikTok, and X (formerly Twitter), use such systems to determine what appears in users' feeds and search results
The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information
A feed can be ordered by time, with the newest post at the top
Algorithmic systems order it by prediction instead
Material that scores well may be shown to progressively larger audiences through feeds, suggested videos, and search results
A video that many people watch to the end, for instance, can reach users who do not follow the account that posted it
That early attention can feed back into the model that ranks what users are shown next
Recommendation systems also support content discovery and public-interest communication, and their ranking decisions shape the visibility and income of creators and news organisations
Algorithmic amplification has been linked to the spread of misinformation, the circulation of extremist and other harmful material, and to concerns about young users' mental health
It also features in debates about filter bubbles and echo chambers, and about political polarisation
A systematic review of digital media use classified most reported associations with polarisation as detrimental to democracy
Research isolating the effect of ranking has varied across platforms and methods
Studies of X have found uneven amplification of political content, while large experiments on Facebook and Instagram found limited effects on users' attitudes
A study of YouTube that used automated accounts to isolate the effect of its recommendation system found that user preferences played a larger role than recommendations in determining partisan consumption
Researchers have also examined how state actors and automated accounts use recommendation systems to shape what is seen
The scale and direction of these effects remain debated, in part because independent researchers have limited access to platform systems
News organisations reported that internal Meta documents released by former employees indicated that engagement-based ranking rewarded divisive content; Meta disputed the claims
Governments in the European Union, United Kingdom, United States, and China have taken differing approaches to regulating recommendation systems
The UK's Online Safety Act 2023 requires services to assess risks arising from algorithms and, under Ofcom's child-safety codes, requires some providers to filter harmful content from children's feeds
A House of Commons committee concluded in 2025 that the Act did not adequately address the amplification of legal but harmful content
# algorithmic_game_theory
Algorithmic game theory (AGT) is an interdisciplinary field at the intersection of game theory and computer science, focused on understanding and designing algorithms for environments where multiple strategic agents interact
This research area combines computational thinking with economic principles to address challenges that emerge when algorithmic inputs come from self-interested participants
In traditional algorithm design, inputs are assumed to be fixed and reliable
AGT provides frameworks to analyze and design systems that remain effective despite such strategic behavior
The field can be approached from two complementary perspectives: Analysis: Evaluating existing algorithms and systems through game-theoretic tools to understand their strategic properties
Design: Creating mechanisms and algorithms with both desirable computational properties and game-theoretic robustness
This sub-field, known as algorithmic mechanism design, develops systems that incentivize truthful behavior while maintaining computational efficiency
# algorithmic_learning_theory

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