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has been used to refer to situations in which …
carries certain connotations in some types of …
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The definition of X has evolved. There are multiple definitions of X. Several definitions of X have been proposed. In the field of X, various definitions of X are found. The term ‘X’ embodies a multitude of concepts which … This term has two overlapping, even slightly confusing meanings. Widely varying definitions of X have emerged (Smith and Jones, 1999). Despite its common usage, X is used in different disciplines to mean different things. Since the definition of X varies among researchers, it is important to clarify how the term is …
The meaning of this term | has evolved. has varied over time. has been extended to refer to … has been broadened in recent years. has not been consistent throughout … has changed somewhat from its original definition … |
X is a contested term. X is a rather nebulous term … X is challenging to define because … A precise definition of X has proved elusive. A generally accepted definition of X is lacking. Unfortunately, X remains a poorly defined term. There is no agreed definition on what constitutes … There is little consensus about what X actually means. There is a degree of uncertainty around the terminology in … These terms are often used interchangeably and without precision. Numerous terms are used to describe X, the most common of which are …. The definition of X varies in the literature and there is terminological confusion. Smith (2001) identified four abilities that might be subsumed under the term ‘X’: a) … ‘X’ is a term frequently used in the literature, but to date there is no consensus about … X is a commonly-used notion in psychology and yet it is a concept difficult to define precisely. Although differences of opinion still exist, there appears to be some agreement that X refers to …
The meaning of this term | has been disputed. has been debated ever since … has proved to be notoriously hard to define. has been an object of major disagreement in … has been a matter of ongoing discussion among … |
The term ‘X’ is used here to refer to … In the present study, X is defined as … The term ‘X’ will be used solely when referring to … In this essay, the term ‘X’ will be used in its broadest sense to refer to all … In this paper, the term that will be used to describe this phenomenon is ‘X’. In this dissertation, the terms ‘X’ and ‘Y’ are used interchangeably to mean … Throughout this thesis, the term ‘X’ is used to refer to informal systems as well as … While a variety of definitions of the term ‘X’ have been suggested, this paper will use the definition first suggested by Smith (1968) who saw it as …
For Smith (2001), X means … Smith (2001) uses the term ‘X’ to refer to … Smith (1954) was apparently the first to use the term … In 1987, psychologist John Smith popularized the term ‘X’ to describe … According to a definition provided by Smith (2001:23), X is ‘the maximally … This definition is close to those of Smith (2012) and Jones (2013) who define X as … Smith, has shown that, as late as 1920, Jones was using the term ‘X’ to refer to particular … One of the first people to define nursing was Florence Nightingale (1860), who wrote: ‘… …’ Chomsky writes that a grammar is a ‘device of some sort for producing the ….’ (1957, p.11). Aristotle defines the imagination as ‘the movement which results upon an actual sensation.’ Smith et al . (2002) have provided a new definition of health: ‘health is a state of being with …
X is defined by Smith (2003: 119) as ‘… …’ The term ‘X’ is used by Smith (2001) to refer to … X is, for Smith (2012), the situation which occurs when … A further definition of X is given by Smith (1982) who describes … The term ‘X’ is used by Aristotle in four overlapping senses. First, it is the underlying … X is the degree to which an assessment process or device measures … (Smith et al ., 1986).
This definition | includes … allows for … highlights the … helps distinguish … takes into account … poses a problem for … will continue to evolve. can vary depending on … was agreed upon after … has been broadened to include … |
The following definition is | intended to … modelled on … too simplistic: useful because … problematic as … inadequate since … in need of revision since … important for what it excludes. the most precise produced so far. |
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Definition of term paper noun from the Oxford Advanced Learner's Dictionary
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Explore the definition of terms in research paper to enhance your understanding of crucial scientific terminology and grow your knowledge.
Have you ever come across a research paper and found yourself scratching your head over complex synonyms and unfamiliar terms? It’s a hassle as you have to fetch a dictionary and then ruffle through it to find the meaning of the terms.
To avoid that, an exclusive section called ‘ Definition of Terms in a Research Paper ’ is introduced which contains the definitions of terms used in the paper. Let us learn more about it in this article.
The definition of terms section in a research paper provides a clear and concise explanation of key concepts, variables, and terminology used throughout the study.
In the definition of terms section, researchers typically provide precise definitions for specific technical terms, acronyms, jargon, and any other domain-specific vocabulary used in their work. This section enhances the overall quality and rigor of the research by establishing a solid foundation for communication and understanding.
This section aims to ensure that readers have a common understanding of the terminology employed in the research, eliminating confusion and promoting clarity. The definitions provided serve as a reference point for readers, enabling them to comprehend the context and scope of the study. It serves several important purposes:
Having a definition of terms section in a research paper offers several benefits that contribute to the overall quality and effectiveness of the study. These benefits include:
Clear definitions enable readers to understand the specific meanings of key terms, concepts, and variables used in the research. This promotes clarity and enhances comprehension, ensuring that readers can follow the study’s arguments, methods, and findings more easily.
Definitions provide a consistent framework for the use of terminology throughout the research paper. By clearly defining terms, researchers establish a standard vocabulary, reducing ambiguity and potential misunderstandings. This precision enhances the accuracy and reliability of the study’s findings.
The definition of terms section helps establish a shared understanding among readers, including those from different disciplines or with varying levels of familiarity with the subject matter. It ensures that readers approach the research with a common knowledge base, facilitating effective communication and interpretation of the results.
Without clear definitions, readers may interpret terms and concepts differently, leading to misinterpretation of the research findings. By providing explicit definitions, researchers minimize the risk of misunderstandings and ensure that readers grasp the intended meaning of the terminology used in the study.
Research papers are often read by a wide range of individuals, including researchers, students, policymakers, and professionals. Having a definition of terms in a research paper helps the diverse audience understand the concepts better and make appropriate decisions.
There are several types of definitions that researchers can employ in a research paper, depending on the context and nature of the study. Here are some common types of definitions:
Lexical definitions provide the dictionary or commonly accepted meaning of a term. They offer a concise and widely recognized explanation of a word or concept. Lexical definitions are useful for establishing a baseline understanding of a term, especially when dealing with everyday language or non-technical terms.
Operational Definitions
Operational definitions define a term or concept about how it is measured or observed in the study. These definitions specify the procedures, instruments, or criteria used to operationalize an abstract or theoretical concept. Operational definitions help ensure clarity and consistency in data collection and measurement.
Conceptual definitions provide an abstract or theoretical understanding of a term or concept within a specific research context. They often involve a more detailed and nuanced explanation, exploring the underlying principles, theories, or models that inform the concept. Conceptual definitions are useful for establishing a theoretical framework and promoting deeper understanding.
Descriptive definitions describe a term or concept by providing characteristics, features, or attributes associated with it. These definitions focus on outlining the essential qualities or elements that define the term. Descriptive definitions help readers grasp the nature and scope of a concept by painting a detailed picture.
Theoretical definitions explain a term or concept based on established theories or conceptual frameworks. They situate the concept within a broader theoretical context, connecting it to relevant literature and existing knowledge. Theoretical definitions help researchers establish the theoretical underpinnings of their study and provide a foundation for further analysis.
Also read: Understanding What is Theoretical Framework
In research papers, various types of terms can be identified based on their nature and usage. Here are some common types of terms:
A key term is a term that holds significant importance or plays a crucial role within the context of a research paper. It is a term that encapsulates a core concept, idea, or variable that is central to the study. Key terms are often essential for understanding the research objectives, methodology, findings, and conclusions.
Technical terms refer to specialized vocabulary or terminology used within a specific field of study. These terms are often precise and have specific meanings within their respective disciplines. Examples include “allele,” “hypothesis testing,” or “algorithm.”
Legal terms are specific vocabulary used within the legal field to describe concepts, principles, and regulations. These terms have particular meanings within the legal context. Examples include “defendant,” “plaintiff,” “due process,” or “jurisdiction.”
A definitional term refers to a word or phrase that requires an explicit definition to ensure clarity and understanding within a particular context. These terms may be technical, abstract, or have multiple interpretations.
Career privacy term refers to a concept or idea related to the privacy of individuals in the context of their professional or occupational activities. It encompasses the protection of personal information, and confidential data, and the right to control the disclosure of sensitive career-related details.
A broad term is a term that encompasses a wide range of related concepts, ideas, or objects. It has a broader scope and may encompass multiple subcategories or specific examples.
Also read: Keywords In A Research Paper: The Importance Of The Right Choice
When writing the definition of terms section for a research paper, you can follow these steps to ensure clarity and accuracy:
Review your research paper and identify the key terms that require definition. These terms are typically central to your study, specific to your field or topic, or may have different interpretations.
Conduct thorough research on each key term to understand its commonly accepted definition, usage, and any variations or nuances within your specific research context. Consult authoritative sources such as academic journals, books, or reputable online resources.
Based on your research, craft concise definitions for each key term. Aim for clarity, precision, and relevance. Define the term in a manner that reflects its significance within your research and ensures reader comprehension.
Paraphrase the definitions in your own words to avoid plagiarism and maintain academic integrity. While you can draw inspiration from existing definitions, rephrase them to reflect your understanding and writing style. Avoid directly copying from sources.
Consider providing examples, explanations, or context for the defined terms to enhance reader understanding. This can help illustrate how the term is applied within your research or clarify its practical implications.
Decide on the order in which you present the definitions. You can follow alphabetical order or arrange them based on their importance or relevance to your research. Use consistent formatting, such as bold or italics, to distinguish the defined terms from the rest of the text.
Review the definitions for clarity, coherence, and accuracy. Ensure that they align with your research objectives and are tailored to your specific study. Seek feedback from peers, mentors, or experts in your field to further refine and improve the definitions.
If you have drawn ideas or information from external sources, remember to provide proper citations for those sources. This demonstrates academic integrity and acknowledges the original authors.
Integrate the definition of terms section into your research paper, typically as an early section following the introduction. Make sure it flows smoothly with the rest of the paper and provides a solid foundation for understanding the subsequent content.
By following these steps, you can create a well-crafted and informative definition of terms section that enhances the clarity and comprehension of your research paper.
In conclusion, the definition of terms in a research paper plays a critical role by providing clarity, establishing a common understanding, and enhancing communication among readers. The definition of terms section is an essential component that contributes to the overall quality, rigor, and effectiveness of a research paper.
Also read: Beyond The Main Text: The Value Of A Research Paper Appendix
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Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.
Vocabulary
Definitions for term paper term pa·per, this dictionary definitions page includes all the possible meanings, example usage and translations of the word term paper ., princeton's wordnet rate this definition: 2.0 / 1 vote.
a composition intended to indicate a student's progress during a school term
A substantial research paper written by a student over an academic term or semester which accounts for a large amount of a grade and makes up much of the course.
A term paper is a research paper written by students over an academic term, accounting for a large part of a grade. Merriam-Webster defines it as "a major written assignment in a school or college course representative of a student's achievement during a term". Term papers are generally intended to describe an event, a concept, or argue a point. It is a written original work discussing a topic in detail, usually several typed pages in length, and is often due at the end of a semester. There is much overlap between the terms: research paper and term paper. A term paper was originally a written assignment (usually a research based paper) that was due at the end of the "term"—either a semester or quarter, depending on which unit of measure a school used. However, not all term papers involve academic research, and not all research papers are term papers.
A term paper is a lengthy, in-depth research paper written by students over an academic term or semester which accounts for a large part of a grade. It is typically designed to discuss and analyze a topic in detail based on compiled information or data, demonstrating a student's understanding and knowledge on the subject matter. It often requires considerable research and writing skills.
A 'term paper' is a research paper written by students over an academic term, accounting for a large part of a grade. Term papers are generally intended to describe an event, a concept, or argue a point. A term paper is a written original work discussing a topic in detail, usually several typed pages in length and is often due at the end of a semester. There is much overlap between the terms "research paper" and "term paper". The phrase "term paper" was originally used to describe a paper that was due at the end of the "term" - either a semester or quarter, depending on which unit of measure a school used. However, the term has fallen out of favor. Common usage has "term paper" and "research paper" as interchangeable, but this is not completely accurate. Not all term papers involve academic research, and not all research papers are term papers. Term papers date back to the beginning of the 19th century when print could be reproduced cheaply and written texts of all types could be easily produced and disseminated. During the years from 1870 to 1900, Moulton and Holmes write that "American education was transformed as writing became a method of discourse and research the hallmark of learning." Russell writes that in the 1910s, "the research paper began to harden into its familiar form" adding that plagiarism and the sale of research papers both became a problem during this time.
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Chaldean Numerology
The numerical value of term paper in Chaldean Numerology is: 3
Pythagorean Numerology
The numerical value of term paper in Pythagorean Numerology is: 4
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Image credit, the web's largest resource for, definitions & translations, a member of the stands4 network, free, no signup required :, add to chrome, add to firefox, browse definitions.net, are you a words master, a decorative musical accompaniment (often improvised) added above a basic melody, Nearby & related entries:.
Breaking makes its Olympic debut in Paris, and with any new Olympic sport comes new vocabulary for sports fans to learn. If you're not familiar with breaking terminology, not to worry.
Here's a glossary of terms you might hear during the breaking competition.
When you heard breaking was joining the Olympic sport roster, you might have wondered why it's not called breakdancing.
The simple answer is that breakdancing is not the real title of the sport. Breaking originated in the Bronx in the 1970s. DJ Kool Herc realized that people tend to dance with more energy during the instrumental section songs, also known as the "break." He then created the style of producing a song composed entirely of the dancing beat, or the "breakbeat." Herc began hosting parties, and then later competitions, where people would come and dance to his breakbeats without having heard them beforehand. They called the dancing to these beats, "breaking," and the sport was born.
When breaking started to permeate throughout more mainstream media, such as television and movies, people began referring to it as breakdancing, though that was never the term used by the true breaking community.
The terms "B-Boys" and "B-Girls" are titles for the breaking athletes themselves. When DJ Herc began hosting breaking parties and competitions, he called the participants "Break-Boys" and "Break-Girls." Ever since, breaking athletes have often used the title in front of their breaking name. For example, Canadian breaking athlete, Philip Kim , is known as "B-Boy Phil Wizard."
While many breaking athletes use the prefix, not all prefer it, with some competitors electing to just be referred to by their breaking name.
Power head : Someone who loves to practice and perform mostly power moves in their breaking, which are acrobatic moves that require momentum, speed, endurance, strength, flexibility, and control. The breaker is generally supported by their upper body, while the rest of their body creates circular momentum.
Footwork cat : Someone who loves to practice and perform footwork in their breaking.
Popping : A continuous flexing of the muscles to the beat. Some moves include arm and body waves that look like an electric current has passed through the body.
Locking : Freezing from a fast movement and "locking" into a certain position, holding it, and then continuing at the same speed as before.
Headspin : In a headstand position, the breaker spins by pushing with their hands.
Heelspin : Breaker puts their weight on one heel and initiates a spin by swinging their leg.
Windmill : Breaker rotates continuously on one shoulder with their feet in the air and legs apart.
Backspin : Breaker balances weight on their upper back and goes into a spin by pushing with their hands or swinging the legs across the body.
Throw down : When the B-Girl or B-Boy hits the floor and starts breaking, they are doing a throw down.
Set : A set is a breaker's prepared round or combination of moves.
Repeating : When a breaker reuses a move that they've already done during the competition, they are considered to have been "repeating." Twenty percent of a breaker's score is originality, and repeating can negatively impact that score.
Bite/biter/biting : When a breaker is accused of "biting" or being a "biter," it means that they have either stolen or copied moves/style from another breaker. Similar to "repeating," this can also affect a breaker's originality score.
Crashing : If a breaker "crashes," it means they failed an attempted move and fell during or at the end of their attempt. This may be the most common cause of a breaker losing a battle. The best breakers, however, know how to turn a crash into a move and can control the crash enough to continue their flow into something else.
Crew : A group of breakers who train and compete together. Historically, rival crews have often competed against each other under various sets of rules.
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The European Union’s Artificial Intelligence Act (AI Act) is a groundbreaking regulatory framework that integrates technical concepts and terminology from the rapidly evolving ecosystems of AI research and innovation into the legal domain. Precise definitions accessible to both AI experts and lawyers are crucial for the legislation to be effective. This paper provides an interdisciplinary analysis of the concepts of AI system , general purpose AI system , foundation model and generative AI across the different versions of the legal text (Commission proposal, Parliament position and Council General Approach) before the final political agreement. The goal is to help bridge the understanding of these key terms between the technical and legal communities and contribute to a proper implementation of the AI Act. We provide an analysis of the concept of AI system considering its scientific foundation and the crucial role that it plays in the regulation, which requires a sound definition both from legal and technical standpoints. We connect the outcomes of this discussion with the analysis of the concept of general purpose AI system and its evolution during the negotiations. We also address the distinct conceptual meanings of AI system vs AI model and explore the technical nuances of the term foundation model . We conclude that rooting the definition of foundation model to its general purpose capabilities following standardised evaluation methodologies appears to be most appropriate approach. Lastly, we tackle the concept of generative AI , arguing that definitions of AI system that include “content” as one of the system’s outputs already captures it, and concluding that not all generative AI is based on foundation models .
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After the European Commission tabled the proposal for the Artificial Intelligence Act (AI Act) in April 2021 (European Commission 2021b ), important market and technological developments in the field of AI have taken place, including the emergence of ChatGPT (OpenAI 2022 ) and other similar tools, showcasing the general purpose capabilities of Large Language Models. Those developments have attracted a lot of public attention, prompting EU policy makers to specifically consider them in the course of the negotiations of the AI Act under way (Helberger and Diakopoulos 2023 ; Hacker et al. 2023 ; Boine and Rolnick 2023 ).
In the European Union legal order, which is distinct from the legal order of the Member States belonging to it, most laws are the result of a process where three institutions play an essential role. The European Commission, the executive, has the exclusive power to initiate legislation by preparing and publishing a draft law. This is sent in parallel to the European Parliament, directly elected by the EU citizens, and the Council, representing the government of all 27 Member States. The European Parliament and the Council are known as the ’co-legislators’ insofar as they have the power to adopt the final law by agreeing on the same text. Each of them follows internal procedures to reach its own position (i.e. a set of amendments to the Commission proposal). In order to reach a compromise in a more effective manner, the European Parliament, the Council and the Commission have developed the the practice of holding "trialogues" meetings between their representatives having received a mandate to negotiate a political deal on the basis of their own starting position. Any political deal reached in trialogues must be articulated into a fully fledged legal text, which has to be endorsed by the co-legislators In trialogues the Commission does not have a formal decision-making role, but remains an influential player by providing technical support to the other two institutions (European Parliamentary Research Service 2021 ).
The position expressed by the co-legislators, notably the General Approach of the Council (December 2022) Footnote 1 and the text adopted by the European Parliament (June 2023), Footnote 2 introduce in the AI Act new terms and concepts, notably general purpose AI system , foundation model and generative AI , and foresee accordingly dedicated legal provisions. These terms and concepts add to the already rich and varied ecosystem of specialised jargon used in the research community (Estevez Almenzar et al. 2022 ; Smuha 2021 ).
Terminological choices in law are essential and have far reaching consequences for the practical impact of the rules being introduced, especially in areas permeated by a high-degree of interdisciplinarity like AI (Koivisto et al. 2024 ; Schuett 2023 ).
The objective of this paper is to present and explain those terms and concepts, considering both a technical and legal perspective, with a view to facilitate a correct understanding thereof. To offer a useful context for the reader, we also propose a section on the term AI system , already included in the Commission proposal.
The considerations contained in this paper are based solely on the texts adopted separately by the three EU institutions before the final political agreement reached on December 9, 2023. We do not comment on the final text of the AI Act, which is still to be published at the time of writing of this paper. Nonetheless, we believe that the analysis we offer provides relevant background and tools also for a correct understanding of the adopted law, which results from negotiations between the co-legislators on the basis of their own position.
One of the most consequential concepts in the AI Act is the definition of AI system . This determines which systems fall within the scope of the regulation and may therefore be subject to the requirements and obligations established therein depending on their risk level.
Table 1 contains the several versions of the definition of AI system discussed herein. Although certainly important elements to consider, in the interest of brevity, we do not analyse the recitals (of the AI Act) or other explanatory text (of the OECD) accompanying the definition strictly speaking. In EU law the recitals can provide additional interpretative context in case of ambiguity in the operative provisions, but are not binding as such.
The AI Act, which follows the logic of the vast EU product legislation acquis called ’New Legislative Framework’, considers an AI system as a product, as discussed by Mazzini and Scalzo ( 2023 ). In line with other product legislation, the intended purpose of the AI system (i.e., the use for which an AI system is intended by the provider, including the specific context and conditions of use: Art. 3(12) Commission proposal) plays an essential role, including, among others, its classification according to risk levels.
The Commission’s intention was to propose a definition of AI system that would exclude conventional software as this does not present the characteristics or challenges typical of AI systems , as discussed in the Commission’s impact assessment (European Commission 2021a ). On the other hand, the definition was intended to be inclusive enough to cover not only machine learning, but also other AI techniques and approaches such as symbolic ones. In addition, the definition was intended to be capable of adapting to new developments in the field. Because the definition of AI system in the AI Act should serve a regulatory objective and therefore provide legal certainty to operators, other definitions from the scientific literature or other non-binding policy documents would as a matter of principle not be fit for purpose, as discussed by Samoili et al. ( 2021 ).
In order to ensure an alignment of the EU legislative initiative with concepts emerging in international fora, the Commission took inspiration from the definition of AI system proposed by the OECD in its 2019 Recommendation of the Council on Artificial Intelligence (OECD 2019 ). However, because the OECD definition was not designed for regulatory purposes, some refinements were made in the AI Act proposal.
First, the Commission referred to “software” as opposed to “machine-based system” in order to align with already existing references to “software” in other EU legislative acts (e.g. the Medical Devices Regulation regulates "software" as such - which can itself be or can include an AI system - as a medical device). Footnote 3 Second, it highlighted that an AI system “can, for a given set of human-defined objectives, generate outputs” and made it also clear that those outputs can include “content”. Third, the Commission added that the software should be developed “with one or more of the techniques and approaches listed in Annex I”. In order to ensure legal certainty, Annex I listed a set of techniques used in machine learning, logic- and knowledge-based approaches and statistical modelling, which could be updated via delegated acts.
Although there are differences in their choices, both the Council and the Parliament aimed in general to keep the definition of AI system as aligned as possible to the OECD wording.
As regards the Council, it deleted the Annex I with the list of techniques, and incorporated the concepts that the Annex I was meant to clarify and exemplify in the definition itself, with the sentence “infers how to achieve a given set of objectives using machine learning and/or logic- and knowledge-based approaches”. In addition, the Council replaced the term “software” with “system”, added the concept of “elements of autonomy” and emphasised the relevance of data and inputs.
Like the Council, the Parliament also deleted the Annex I, but overall departed much less from the wording proposed by the OECD. Among others, the Parliament deleted the word “content” proposed by the Commission, replaced the term “software” with “machine-based system”, and included the reference to “varying level of autonomy”.
Compared to the definition adopted by the Council, the definition of the Parliament is not clear in making a clear distinction between AI systems and non-AI systems by omitting the reference to AI approaches and the ability of the system to infer how to achieve its own objectives. Other differences between the Parliament approach and that of the Council relate, for instance, to the deletion of the word “content”, the reference to a different concept of autonomy (“varying levels of autonomy” as opposed to "elements of autonomy") and the addition of the words “explicit or implicit" for the objectives of the system.
In parallel to the legislative deliberations in the EU, the OECD ( 2023 ) worked on revising the initially proposed definition of AI system , amended at the 2023 Recommendation of the Council on Artificial Intelligence, followed by an explanatory memorandum in 2024 (OECD 2024 ). It is interesting to note that the revised OECD definition includes elements discussed at EU level - notably in order to clearly differentiate an AI system from other more traditional software systems - such as the elements of "infer [...] how to generate outputs" and the mention of “content” as a possible output of the AI system .
The term general purpose AI system was used for the first time in a regulatory context in the Council General Approach. It emerged as a new concept during the Council debates as regards the role and responsibilities of actors in the AI value chain, i.e., of those actors whose software tools and components can be used by downstream providers for the development of AI systems that are under the scope of the AI Act (e.g. high-risk AI systems ). Most likely, the expression “general purpose” was chosen as a derivation of the key concept of “intended purpose” of an AI system , although the two concepts should not be understood as antonyms.
In fact, in the debates surrounding the AI Act too often the legal term “intended purpose” has been incorrectly understood as specific purpose. Most probably this confusion originated because all high-risk AI systems (to which the greatest majority of the AI Act provisions are devoted) have an intended purpose that is specific (e.g. assessing creditworthiness of individuals, selecting resumes of job candidates, etc.) and, as mentioned earlier, the concept of “intended purpose” is key in the logic of the AI Act for the classification of those systems. However, as the definition itself states (Art. 1(12)), the concept of “intended purpose” merely refers to “the use for which an AI system is intended by the provider”, regardless of whether that use is general or specific.
In recital 60 of the proposal, the Commission had acknowledged the complexity of the AI value chain and the importance of the cooperation between relevant third parties and provider of the AI system with a view to enable compliance by the latter of their obligations under the AI Act. Such cooperation was left to the relevant parties’ contractual freedom and, consistently with the product legislation approach, regulatory compliance was identified as a responsibility of the provider of the final (high-risk) AI system .
The Council defines the concept of general purpose AI system as an AI system that “is intended by the provider to perform generally applicable functions such as image and speech recognition, audio and video generation, pattern detection, question-answering, translation and others; a general purpose AI system may be used in a plurality of contexts and be integrated in a plurality of other AI systems ” (Art. 3(1b) General Approach).
The main characteristic of the general purpose AI system emerging from the Council text is that it can be integrated as a component of a downstream AI system covered by the AI Act, but also that it can be used “as such” as an AI system falling under the scope of the AI Act (Art. 4b(1) and recital 12c General Approach).
In the light of the above elements, the Council suggested to impose specific requirements and obligations (very much aligned with those applicable to high-risk AI systems ) on the provider of this category of AI system . On the one hand, such requirements and obligations would lead to an improvement in the balance of responsibilities in the AI value chain (notably when those systems are integrated as components of high-risk AI systems ). The downstream provider would benefit from the compliance activity performed by the upstream provider. On the other hand, the approach proposed by the Council would also serve to ensure compliance with the applicable requirements of the AI Act where these systems, in spite of the ’generality’ of purpose intended by the provider, due to their capabilities could be used by themselves as high-risk AI systems . In order to maintain a close link of the new rules on general purpose AI systems with the risk-based approach of the AI Act, the Council clarified that compliance by the providers of general purpose AI systems with the specific requirements and obligations is not expected if those providers have explicitly excluded in good faith all high-risk uses.
From the examples provided in the Council definition, general purpose AI systems include systems that generate content such as audio, video, and also text (e.g. question answering, translation). Therefore AI systems like ChatGPT, which was launched by OpenAI on 30 November 2022, i.e. just around the time the Council was finalising its General Approach - can certainly be considered an example of the concept of general purpose AI system introduced by the Council.
However, the Council definition of general purpose AI system appears to be more encompassing, in that it also refers to “narrow” AI systems , i.e. AI systems typically constrained to perform a more limited task or function, such as image and speech recognition or pattern detection on a specific domain. While AI systems belonging to this category are different from the previous ones that display broader capabilities and can perform a plurality of tasks, they are part of the AI value chain and can be integrated as components of downstream AI systems such as high-risk AI systems .
The Parliament proposed a different definition for general purpose AI system , referring to an “ AI system that can be used in and adapted to a wide range of applications for which it was not intentionally and specifically designed” (Art. 3(1d)). This definition appears to exclude the category of narrow AI systems , and focuses essentially on systems that are capable of dealing with tasks outside those for which they were specifically trained. Therefore, this definition points more directly to those highly capable AI systems that have become known to the public since the appearance of ChatGPT.
In this paper we focus only on the terminology used by the three EU institutions in the context of the AI Act. It is to note, however, that several other terms have been used often interchangeably by different actors, including outside the scientific communities, to refer to AI tools like ChatGPT and related underlying techniques (e.g. generative pre-trained transformers). Such terms include for instance large pre-trained models, large language models, base models, foundational models, frontier models and also generative AI .
In the light of the terminological variations just mentioned, it is important to understand the difference between AI model and AI system . In short, a system is a broader concept than a model, to the extent the latter is solely a component, among others, of the former.
An AI system comprises various components, including, in addition to the model or models, elements such as interfaces, sensors, conventional software, etc. From a scientific and technical standpoint and in accordance with ISO ( 2022 ) terminology, an AI model is a “physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, process or data” and a machine learning model is a “mathematical construct that generates an inference or prediction based on input data”.
Therefore, the model in itself would not be useful, or rather usable in the first place, unless it is complemented by other software components which, together, constitute a system. In fact, only a system is capable to perform tasks and to function effectively, including of course interacting with users, other machine based systems and the environment more generally, as considered by Junklewitz et al. ( 2023 ). As an example, while ChatGPT is an AI system , a chatbot, GPT 3.5 is the model powering the chatbot. Figure 1 depicts the relationship between AI model and AI system .
Often times the terms “system” and “model” are used interchangeably, especially in non-technical discussions, in relation to AI tools such as ChatGPT and similar applications. However, the distinction between the two becomes very important from a legal perspective if a decision is made to establish binding rules not only at the level of an AI system , but also at the level of its components, like the AI model .
In fact, unlike the Council, which established additional rules for the new category of general purpose AI systems , the Parliament opted to establish new rules upstream in the AI value chain and introduced the concept of foundation model defined as “an AI model that is trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasks” (Art. 3(1c). Accordingly, the Parliament established a number of obligations for the providers of the foundation models , including on: risk assessment and management, data governance, performance requirements as regards predictability, interpretability, corrigibility, safety and cybersecurity of the model, energy reduction and energy efficiency, technical documentation, quality management system and registration (Art. 28b).
As any other possible component in the context of the AI value chain, the AI model can be provided by actors other than the provider of the final AI system , who determines the purpose for which the AI system can be used. It is therefore important to duly reflect on any possible obligations at the level of the AI model upstream so that they are appropriate, feasible and calibrated to the position of the actor in question. For instance, as it typically happens in complex engineering products, including software, where components can be sourced from multiple suppliers and integrated by one final manufacturer or developer, the providers of the AI system typically have at their disposal other tools, in addition to those available to providers of the AI models , to handle possible risks (e.g. adapted to concrete applications or scenarios).
This situation may be made even more complex depending on the means by which the AI model can be provided. For instance, if a model is provided as a software library, it may become unfeasible for model providers to monitor the manner in which the model is further deployed downstream or monitor whether the use of the model may have lead to problems or accidents.
The above does not mean however that the provider of the AI model should not have any role for compliance purposes, notably to the extent legal compliance downstream may be dependent on information available only to the provider of the AI model . In this respect an adequate level of transparency vis-a-vis downstream providers of AI systems seems necessary to address the risk of an imbalance of knowledge and ensure a fair allocation of responsibilities overall.
Diagram depicting the relationship between AI model and AI system
The term foundation model was introduced by the Stanford Institute for Human-Centered Artificial Intelligence in August 2021. That concept refers to a new machine learning paradigm in which one large model is pre-trained on a huge amount of data (broad data at scale) and can be used for many downstream tasks and applications (Bommasani et al. 2021 ).
The approach of pre-training a model (a base model) for a particular task and adapting it to a different task or set of tasks is a common approach in the machine learning community. This is traditionally enabled by transfer learning, where, according to Thrun and Mitchell ( 1995 ), “knowledge” learned from one task is adapted and applied to another task via fine-tuning (i.e., the parameters of a pre-trained model are re-computed with new data for another task).
However, when it comes to foundation models , such machine learning approach takes a different dimension. The key element of differentiation is the generality of capabilities of the model, which derive primarily from the architecture and size of the model (e.g. number of parameters in the neural network), the scale of the training data and compute, as well as the training methodology.
Learning objectives of foundation models involved in their training tend to be general and largely focused on the structure of the data itself, that is, learning representations directly from the data attributes without the need of a specific ground truth. Examples of learning objectives are predicting the next word given a sentence, capturing a distribution of images given a text prompt, or capturing and encoding representative features of data (images, audio or text).
At the current stage of technological development and architectures, the capabilities of the model are largely influenced by the size of the model itself and the amount and quality of the data used to train the same. It is worth noting that the relevant orders of magnitude (e.g. as regards model size or amount of data) are dependent on the modality (i.e., text, images, video or audio) and they are also a moving target. For example, in 2019, a language model such as BERT by Devlin et al. ( 2019 ), with 340 million parameters trained on a dataset of about 3.3 billion tokens, was considered a very large model trained on a very large dataset. However, four years later, language models considered large are of the order of tens or hundreds of billions of parameters, trained on datasets of more than 1 trillion tokens (e.g. a state-of-the-art model such as Llama 2 by Touvron et al. ( 2023 ) contains 70 billion parameters and was trained with 2 trillion tokens).
Considering that quantitative elements can significantly vary with technological development, any possible legal definition that would factor in scale (e.g. data, model parameters or merely the training compute as a joint indicator) as a proxy for capabilities to define the boundaries of foundation models should include a mechanism for flexible and timely updates to ensure that the definition remains future-proof. At the same time, it is important to consider the impact of the technological advances (e.g. new neural network architectures) whereby models may be highly capable also if they have a lower number of parameters or have been trained with a lower quantity of data or computational resources. Fig. 2 shows that whilst the compute required for the training of the model is the main factor to predict its performance (in the MMLU benchmark in this case), we see evidence that other factors such as data quality and model architecture also play an important role, which explains why some models like Phi-3 3.8b can outperform much bigger ones (Abdin et al. 2024 ). Finally, all those factors that could be considered relevant for the purpose of regulating foundation models should be closely monitored and documented to ensure they remain fit for purpose and closely linked to the type of capabilities or potential risk that may be relevant from a policy standpoint.
An alternative definitional approach for foundation models that does not rely on factors that are highly variable depending on modality and technological evolution could be to focus on the ability of the system to be inherently capable to competently perform a wide range of distinctive computational tasks. Such an approach would require establishing a taxonomy of tasks, as well as certain thresholds regarding the minimum number of tasks and the level of performance (Uuk et al. 2023 ).
Considering that the advanced capabilities of the foundation models , notably those capabilities that may generate certain risks, are what could justify attention by policymakers, the soundest definitional approach would be to attach regulation to the existence of those advanced capabilities, rather than other elements. From a technical point of view, standardised evaluation protocols or tools (i.e. benchmarks) are the key tools to actually identify the existence of the capabilities and limitations of the foundation model .
The evaluation of the model against standardised benchmarks would also be the basis for an informed appreciation of the risks that may possibly be associated with the specific context of the AI system in which the model is integrated. Evaluation protocols and tools for foundation models are the subject of intense research by the AI community and still in the process of being developed and validated.
Finally, it is important to note that foundation models can be used for generating content in the form of text, images, video or audio. Therefore, foundation models can be at the basis of what is colloquially referred to most recently as generative AI , described in the following section. However, it should be noted that foundation models can also be used for “non-generative” purposes. These would typically imply a more limited output (e.g. a numeric or discrete value), rather than generating a longer free-form output. Some examples are text or image classification.
Graphical representation of estimated compute required to train the model vs MMLU performance for several popular LLMs. Adapted from https://www.interconnects.ai/p/llama-3-and-scaling-open-llms .
The concept of generative learning is well known in the machine learning domain. For example, the distinction between discriminative models, focused on predicting the labels of the data, and generative models, focused on learning how the data are distributed, is a classic topic in this field as observed in Jebara ( 2004 ). However, most recently the term generative AI has become widely used, notably after the emergence of consumer facing products such as ChatGPT or Midjourney, to refer to AI models or AI systems that generate content such as text, images, video or audio. The content generated by these tools is usually conditioned to an input prompt provided by the user (e.g. question-answering, text-to-image, text-to-video or text-to-audio). However, from a technical point of view, the generation capability does not necessarily depend on a prompt (e.g. automatic generation systems).
Generative models have been around for quite some time. Some architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs) or Recurrent Neural Networks (RNNs) have been widely used to develop generative models since at least 2014. These architectures are less scalable than other more recent ones, so they have traditionally been developed at smaller scales in relation to the number of parameters, data and computational resources. For this reason, models based on VAEs, GANs or RNNs are not usually considered as foundation models . Therefore, generative AI does not necessarily imply the use of foundation models . However, the more powerful generative models, based on other architectures such as the Generative Pre-trained Transformer (GPT) or the diffusion models, which have typically been associated most recently to the concept of generative AI , are considered as foundational models.
It has to be noted that the concept of generative AI is already captured in the Commission and Council definitions of AI system , as they refer to the generation of “outputs such as content”. Moreover, specific transparency obligations are introduced for this type of AI system outputs addressed to “users of AI systems that generate image, audio or video content that appreciably resembles existing persons, objects, places or other entities or events and would falsely appear to a person to be authentic or truthful (’deep fake’)” (Art. 52(3)).
The position of the Parliament (Art. 28b(4)) includes specific obligations for providers of foundation models used in AI systems intended to generate content and for providers of foundation models specialised into a generative AI system. This approach appears to link the concept of generative AI exclusively to foundation models , excluding therefore other types of AI models capable of generating content.
Considering the importance and complexity of key legal concepts of the AI Act (notably AI system , general purpose AI system , foundation model and generative AI ), this paper seeks to bring clarity to them from a technical and legal point of view.
After highlighting the need to define the concept of AI system in such a way to duly distinguish it from conventional software/non-AI based systems, we observe that the concept of general purpose AI system - not included in the Commission proposal - is not the same in the Council and in the European Parliament versions of the AI Act. We also address the conceptual differences between the term AI system vs the term AI model and analyse the concept of foundation model in the light of its particular technical properties. While different definitional approaches of this concept from a legal point of view are possible and none is without challenges, we consider that the identification of the relevant capabilities on the basis of standardised evaluation protocols or tools appears to be most appropriate approach. The AI community is making great efforts to develop holistic evaluation frameworks to establish the capabilities of these models. Finally, we argue that the concept of generative AI is already captured by any definition of the term AI system that includes ’content’ as one of the system’s outputs, and that not all generative AI is based on foundation models . We hope that our considerations on the specific terminological choices made and wording used by the three institutions in their version of the AI Act can provide useful context and background to better understand the origin and evolution of the text of the AI Act, including in its final form.
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Fernández-Llorca, D., Gómez, E., Sánchez, I. et al. An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI. Artif Intell Law (2024). https://doi.org/10.1007/s10506-024-09412-y
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