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REVIEW 5 major objections 8 minor 31 references

AI does not merely assist innovation ecosystems; it redefines them as AIIE, a sustainable AI-centered digital community whose dominant AI roles change systematically across startup, growth, and maturity.

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2026-07-31 11:06 UTC pith:ASM2DXUE

load-bearing objection Readable IE/AI synthesis with a stage-role checklist; the Owkin section illustrates rather than verifies, and novelty is mostly reorganization of known CPSS/IE material. the 5 major comments →

arxiv 2607.24589 v1 pith:ASM2DXUE submitted 2026-07-27 cs.AI

Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions

Zhimin Zhang , Chengzhen Ma , Jia Chai , Rongxin Zhan , Huansheng Ning , Lingfeng Mao , Dan Zhang , Suiping Jiang This is my paper
classification cs.AI
keywords Artificial IntelligenceInnovation EcosystemAIIEInnovative Evolutionary DevelopmentInnovation NetworkValue Co-CreationDecentralizationPrivacy Security
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that once artificial intelligence becomes central to an innovation ecosystem, the ecosystem itself changes form. The authors redefine that form as Artificial Intelligence Innovation Ecosystem (AIIE): a sustainable digital community driven by AI that supplies auxiliary decision-making, overcomes time and space barriers in a decentralized way, and pursues five goals—competitive, benign, dynamic, innovative, and transparent. They break traditional innovation ecosystems into physical space (who participates), social space (how they cooperate and compete), and thinking space (what ultimate goals they share), then show how AI-dominated cyberspace alters each. From an evolutionary angle they assign distinct AI roles to three periods: elastic adaptability and unique creativity in startup; cross-domain ability and collaborative human–AI decision-making in growth; decentralization and privacy-security protection in maturity. They treat the biotechnology firm Owkin’s public trajectory as evidence that the definition and staging are feasible and rational, and close by naming four future pressure points—interpretability, energy use, fairness, and harmony—as the research agenda AIIE must face.

Core claim

AIIE is a sustainable digital community centered on AI technology that assists decisions, overcomes temporal-spatial barriers through decentralized sharing, and pursues five-dimensional value co-creation (competitive, benign, dynamic, innovative, and transparent). AI’s contribution is not static: in the startup period it mainly supplies elastic adaptability and unique creative ability; in growth, cross-domain ability and collaborative decision-making; in maturity, decentralized ability and privacy-security protection. The authors hold that this spatial definition plus three-period role map is feasible, effective, and rational, as illustrated by Owkin’s development.

What carries the argument

Spatial decomposition of innovation ecosystems into physical (participant composition), social (cooperative–competitive relations), and thinking (ultimate goals) spaces, plus an evolutionary staging into startup, growth, and maturity that assigns two primary AI capabilities to each stage. That mapping is the mechanism that turns a general claim about “AI in ecosystems” into a period-specific claim about what AI must do and when.

Load-bearing premise

That one firm’s public development story (Owkin) is enough to verify that the AIIE definition and the three-period AI role assignment are generally feasible, effective, and rational.

What would settle it

Find a mature AI-centered innovation ecosystem whose growth path does not match the assigned roles—for example, a firm or network that reached scale without early emphasis on resilience and generative creativity, without mid-stage cross-domain transfer and human–AI collaborative decision rights, or without late-stage decentralization and privacy tooling—and show that the spatial AIIE definition still fails to describe its participant, relationship, and goal structure.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Ecosystem builders should treat AI as a stage-dependent capability stack, not a single always-on tool: resilience and generative capacity first, then cross-domain and collaborative decision rights, then decentralization and privacy.
  • Responsibility and transparency become a fifth development goal alongside economic, relational, dynamic, and innovative value once AI is inside the ecosystem.
  • Mature AIIE management should shift from centralized oligopoly control toward blockchain- and federated-style decentralized ledgers and privacy-preserving training.
  • Future AIIE research and policy should prioritize four constraint areas the authors flag: interpretability, energy conservation, fairness, and harmonious human–AI employment outcomes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the stage map is right, investors and regulators could score AI-ecosystem health by period-appropriate metrics (e.g., resilience drills early, decision auditability mid-stage, privacy incident rates late) rather than by generic AI adoption rates.
  • The same spatial-plus-evolutionary template could be stress-tested on non-medical sectors (finance, manufacturing, education) to see whether Owkin-like role transitions are sector-specific or general.
  • Conflicts among the five goals—especially transparency versus competitive advantage, or decentralization versus energy cost—are left largely open and would need explicit trade-off rules before the framework can guide design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 8 minor

Summary. The manuscript proposes a conceptual framework for "Artificial Intelligence Innovation Ecosystems" (AIIE). It (i) decomposes prior definitions of innovation ecosystems into physical, social, and thinking spaces and defines AIIE as a sustainable digital community driven by AI, providing auxiliary decision-making, overcoming temporal-spatial barriers in a decentralized way, and pursuing five value goals (competitive, benign, dynamic, innovative, transparent); (ii) contrasts AIIE with traditional IE along seven literature-derived characteristics (Table 3); (iii) hypothesizes stage-specific dominant AI roles across startup (elastic adaptability, unique creativity), growth (cross-domain ability, collaborative decision-making), and maturity (decentralization, privacy security) periods; (iv) presents a single-case analysis of the medical AI company Owkin (Tables 5-6) that is claimed to "verify the feasibility, effectiveness, and rationality" of the definition and the staging; and (v) discusses four challenge areas (interpretability, energy, fairness, harmony) as future directions. The work is a conceptual review with an illustrative case; there is no formal model, measurement, or comparative design.

Significance. If the framework holds up, the paper offers a useful organizing vocabulary — the spatial decomposition of IE into participants, cooperative-competitive relations, and goals, the addition of transparency/responsibility as a fifth value dimension, and the hypothesis that AI's dominant role shifts across lifecycle stages are all potentially valuable to the innovation-ecosystem literature. The manuscript's strengths are its broad and current literature coverage (the reference base through 2024-2025 is genuinely extensive), the explicit side-by-side comparisons against prior AIIE definitions (Table 2) and against traditional IE (Table 3), and a concrete industrial case that at least shows the vocabulary can be applied to a real firm. However, the empirical component as designed cannot discriminate the framework from alternatives, and the staging hypothesis is asserted rather than tested. The contribution is therefore conceptual and illustrative, not validated; the paper's claims need to be re-scoped accordingly, or the case analysis needs to be redesigned to admit the possibility of failure.

major comments (5)
  1. [Section 4, Tables 5-6] Section 4, Tables 5-6, and the Conclusion: the central empirical claim is that the Owkin case 'verifies the feasibility, effectiveness, and rationality' of the AIIE definition and of the three-period role assignment. As constructed, this test is unfalsifiable. The stage definitions and the six AI roles (elasticity+creativity; cross-domain+collaborative decision-making; decentralization+privacy) are fixed in Sections 2-3 from the literature, and Owkin's public milestones are then sorted into these pre-existing boxes. No criterion is stated under which a firm's history would fail to fit, and no date is given for when Owkin crossed the startup-to-growth or growth-to-maturity boundary, so any event can be placed in any period. A successful AI-native firm will contain activities matching all six role categories at most points in time, so a positive mapping is guaranteed by construction. The m
  2. [Table 6; Section 4] Table 6, mature-period rows, and the surrounding text: the stage-locked assignment of federated learning and privacy protection to the mature period is contradicted by the case itself. Owkin's federated-learning research network is not a late-stage development; it is foundational to the company's model and was being built and published on well before the period the paper labels 'mature' (the 2022 blockchain data platform is the only dated maturity-period artifact given). The paper even states in Section 4 that Owkin 'is building a global research network that utilizes federated learning' in present tense when discussing the social space generally. Assigning the firm's signature, long-standing technology to the mature period, while counting early creative publications as startup-period evidence, illustrates that the same firm's history can support any assignment of roles to periods. The a
  3. [Sections 3.1.1, 3.3, 3.3.2] The paper contradicts its own stage-locking premise. Section 3.3.2 states that privacy protection must be prioritized 'throughout the entire lifecycle of AIIE, rather than focusing solely on specific development periods,' and Section 3.1.1 cites (Zamani et al. 2023; Saez et al. 2024; Maier et al. 2024) for the claim that AI-provided resilience 'is not limited to a single phase but continuously follows through various lifecycle stages.' If resilience and privacy are lifecycle-spanning, the claim that they are the distinguishing roles of the startup and mature periods respectively is undermined: the staging reduces to an assertion that all six capabilities matter at all times with shifting emphasis, which is a much weaker and different claim from the one verified in Section 4. The authors should either reconcile these passages with the stage-locked thesis or restate the thesis.
  4. [Section 4; Section 3] The designation of Owkin as being in a 'mature period' is asserted rather than established. The evidence given in Section 4 is that Owkin, founded in 2016, is privately held, announced its first AI pharmaceutical pipeline in 2024, and has raised $254M in venture funding. None of the maturity criteria from the lifecycle literature the paper itself builds on (Moore 1993, 1996: leadership and self-renewal or death; Santos et al. 2022) is operationalized or checked. A still-fundraising, pre-IPO, ~8-year-old firm is a weak instance of maturity, which means the third stage of the claimed three-stage trajectory has not actually been observed in the case, and the maturity-period role claims (decentralization, privacy) are unvalidated even granting the single-case design. The authors should either justify the maturity designation against their own cited criteria or restrict claims to the startup
  5. [Section 4] Case selection is adverse and unaddressed. Owkin is an AI-native, federated-learning-centric healthcare company - close to the ideal type the AIIE definition was constructed to describe - so compatibility between the case and the definition carries almost no information about whether the definition fits innovation ecosystems generally. Section 4 should include an explicit case-selection rationale, and ideally a contrast case (an IE in which AI is peripheral, or a failed AI-centric ecosystem) to show the framework has discriminating power. Without this, the claim in the abstract and Conclusion that the case demonstrates 'application value' should be softened.
minor comments (8)
  1. [Section 2.1.3; Table 2] Internal terminology drift in the core definition: the definition in Section 2.1.3 says 'sustainable digital community,' while Table 2 (AIIE, Ours) says 'sustainable data community'; Table 2 assigns AI the status 'Dominant,' while the definition itself casts AI as providing 'auxiliary decision-making capabilities' - these two characterizations are in tension and should be reconciled.
  2. [Section 3.1.1; Table 4] The term 'elastic adaptability' (Sections 1, 3.1.1) is nonstandard; the literature the paper draws on, including its own Table 4, defines 'resilience.' Using 'elasticity' for what the cited sources call resilience invites confusion with elasticity in the cloud-computing/economics senses. Either justify the terminological choice or adopt the standard term.
  3. [Section 3.2.1] Section 3.2.1 states 'Although AGI can be considered a weak AI, it approaches strong AI due to its universality.' In the standard taxonomy the paper itself sketches, AGI is identified with strong AI; calling it weak AI is at best confusing and should be corrected.
  4. [Sections 1-3] Stage naming is inconsistent: 'mature period' in Section 3 and Table 6 vs. 'maturity period' in Sections 1, 2, and the roadmap paragraph. Pick one.
  5. Typos and grammar: 'the potential impacts in entails' (contributions list); 'which characteristics two primary components' (Section 3.2.2, HLCSM description); 'have significantly changed to the job market' (Section 5.0.4); 'With the addition of AI' capitalized mid-sentence (Section 2.1.3). The manuscript would benefit from a careful English proofread throughout.
  6. [References] Reference-list artifacts that look like OCR/extraction errors: 'V oronkov' (Ponomarev and Voronkov 2017), 'Májovsk`y', 'ˇCern`y'. Please verify these entries against the original sources.
  7. The keywords line contains formatting errors ('Innovative Evolutionary Development· Innovation Network'), and the Acknowledgment contains a placeholder ('XXXX, Grant No. XXXXXXXX') that must be resolved before any publication.
  8. [Figures 1-2] Figures 1 and 2 are referenced as summarizing the spatial impact analysis and the three-period priorities, respectively, but they appear to restate text content; consider whether they add information, and ensure Figure 2 specifies what 'priorities' means operationally.

Circularity Check

3 steps flagged

Owkin 'verification' is post-hoc remapping of a pre-chosen AI-native firm onto stages and roles fixed in §§2–3, so feasibility/rationality is largely guaranteed by construction rather than independently tested.

specific steps
  1. fitted input called prediction [§4 Case Analysis; Tables 5–6; Abstract & contributions (verification claim)]
    "The paper then verifies the feasibility, effectiveness, and rationality of the AIIE's definition and analyzes AIIE development from an evolutionary perspective using enterprise development examples. ... On the one hand, the paper analyzes the rationality of the AIIE concept from the perspectives of participant compositions, cooperative-competitive relationships, and ultimate goals. On the other hand, the paper demonstrates the feasibility of describing the unique role of AI in AIIE development's different periods by examining the changes in Owkin's business focus during the development process"

    Stages and six dominant AI roles are fixed in §§2–3 before the case. Owkin’s public milestones are then sorted into those pre-set boxes (startup: elastic adaptability + unique creativity; growth: cross-domain + collaborative decision-making; maturity: decentralization + privacy). Because any successful AI-centric healthcare platform exhibits creativity, multimodal work, clinical collaboration, FL, and privacy tooling somewhere in its history, the mapping succeeds by construction once that firm type is chosen. No independent, pre-registered criterion is given under which Owkin (or another firm) would fail to 'verify' the framework; success of the 'prediction' is narratively forced by the fit.

  2. other [§4 Table 6 (Mature period rows); §3.3 period definitions]
    "Mature period Decentralized ability — Owkin's research network bridges data scientists and international medical researchers, helping to establish and train deep learning AI models on large, dispersed datasets without requiring all participants to pool their resources. Privacy security protection ability — a) Utilizing a pioneering collaborative AI framework, federated learning... c) In collaboration with NVIDIA and King's College London, Owkin experimented with the feasibility of enhancing the security of medical data using differential privacy frameworks. ... Nowadays, Owkin, which has enter"

    Maturity is defined as the period when decentralized leadership and privacy-security protection dominate. The same activities (FL, decentralized research network, DP) are then cited as evidence that Owkin is in the mature period and that the mature-period role assignment is correct. Period label and evidential content are not independently anchored (a still-fundraising ~8-year private firm is simply declared mature), so the period→role→case loop partly re-describes the definition as confirmation.

  3. self citation load bearing [§2.1 Definition of AIIE (spatial framing); cites Ning et al. 2018 GC, Ning/Zhang 2020 PoT]
    "Under the influence of AI technology, system-level concepts such as Cyber-Physical-Social Systems (CPSS) (Zeng et al. [2020]), General Cyberspace (GC) (Ning et al. [2018]), and PhiNet of Things (PoT) (Ning et al. [2020]) have been successively proposed... As a manifestation of the ecosystem, IE can also be represented in three spaces and will be influenced by AI-oriented cyberspace. For IE, its manifestation in physical space is compositions of participants; in social space, it is the manifestation of cooperative-competitive relationships; and in thinking space, it is manifested as ultimate go"

    The three-space decomposition that structures the entire AIIE definition is justified primarily by the authors’ own prior GC/PoT framing (Ning; Ning & Zhang), with overlapping authorship. This is supporting architecture rather than a uniqueness theorem that forbids rivals, so it is only mildly load-bearing and does not by itself force the Owkin or evolutionary claims.

full rationale

This is a conceptual synthesis paper, not a formal derivation: AIIE is defined from a spatial decomposition of IE plus AI-centric attributes, evolutionary stages are adapted from Moore/Santos/Thomas, and six AI roles are assigned to startup/growth/maturity by literature synthesis. None of that is mathematically self-definitional. The load-bearing empirical claim, however, is that Owkin 'verifies the feasibility, effectiveness, and rationality' of both the definition and the period–role assignment (Abstract; contributions bullet 3; §4; Conclusion). That claim is circular in the fitted-input sense: categories and dominant roles are fixed first; Owkin—an AI-biotech firm already built around multimodal models, clinical collaboration, federated learning, and privacy—is then narrated into those boxes (Tables 5–6). No failure criterion is stated under which a firm’s history would disconfirm the staging (e.g., privacy/FL work is labeled 'mature' while early creative publications are 'startup,' though such activities can co-occur). Selecting an ideal-type AI-native platform firm makes compatibility uninformative. Mild author-overlap citations (Ning/Zhang GC and PoT) supply the spatial framing but are not uniqueness theorems forbidding alternatives and do not drive the score. Net: partial circularity confined to the case-as-verification step; the conceptual taxonomy itself is not forced by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 5 axioms · 3 invented entities

The load-bearing structure is definitional and taxonomic rather than parametric. Claims rest on adopting a three-space IE ontology, collapsing prior lifecycle models into three periods, elevating AI from tool to dominant backbone, expanding goals to a fifth ‘responsibility/transparency’ dimension, and treating one firm’s trajectory as confirmatory. No fitted constants; several invented or redefined constructs without independent external tests.

axioms (5)
  • domain assumption Innovation ecosystems are adequately decomposed into physical (participants), social (coopetition), and thinking (ultimate goals) spaces, with AI-centered cyberspace as the transforming layer.
    Section 2.1 and Table 1 impose this cut on heterogeneous IE definitions; alternative decompositions (e.g., value-chain or institutional) are not tested.
  • ad hoc to paper AIIE evolution is usefully partitioned into startup, growth, and maturity with stage-specific primary AI roles (elasticity+creativity; cross-domain+collaborative DM; decentralization+privacy).
    Section 3 collapses Moore/Santos/Thomas–Autio stage lists into three periods and assigns role pairs; assignment is asserted then illustrated, not derived from data.
  • ad hoc to paper Value co-creation goals of IE expand under AI to a fifth dimension of responsibility/transparency alongside competitive, relational/benign, dynamic, and innovative value.
    Section 2.1.3 introduces responsibility capability as fifth-dimensional goal from black-box risk narrative without operational metrics.
  • ad hoc to paper A single enterprise case (Owkin) can verify feasibility, effectiveness, and rationality of the AIIE definition and evolutionary roles.
    Section 4 and contributions bullet list treat qualitative case alignment as verification.
  • domain assumption Prior IE lifecycle and ecosystem theories (Moore; related stage models) and CPSS/general cyberspace framing are acceptable background for the synthesis.
    Cited as standard scaffolding in Sections 2–3; not re-proved here.
invented entities (3)
  • AIIE (Artificial Intelligence Innovation Ecosystem) as defined here no independent evidence
    purpose: Name an AI-dominated sustainable digital community with auxiliary decision-making, decentralized barrier-breaking, and five-dimensional goals.
    Distinguished in Table 2 from other AIIE/entrepreneurial AI ecosystem labels mainly by ‘dominant AI’, decentralization, and transparency goals; independent operational criteria beyond the definition are not supplied.
  • Five-dimensional ultimate goals of AIIE (competitive, benign, dynamic, innovative, transparent/responsible) no independent evidence
    purpose: Extend classical IE value co-creation to include responsibility under AI opacity.
    Introduced in Section 2.1.3/definition; no measurement instrument or external validation study.
  • Stage-locked AI capability pairs (elastic adaptability & unique creativity; cross-domain & collaborative decision-making; decentralized ability & privacy security) no independent evidence
    purpose: Specify what AI uniquely contributes at each AIIE period.
    Organizing device of Section 3 and Figure 2; capabilities are real AI themes but the exclusive pairing to stages is paper-specific and only illustrated on Owkin.

pith-pipeline@v1.2.0-grok45-kimik3 · 46366 in / 3899 out tokens · 95983 ms · 2026-07-31T11:06:34.283580+00:00 · methodology

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Cite this review

Pith. "Pith review of Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions." pith.science (2026). https://pith.science/paper/ASM2DXUE

@misc{pith2026260724589,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ASM2DXUE}},
  note         = {Machine review of arXiv:2607.24589}
}
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read the original abstract

The development of the Innovative Ecosystem (IE) presents a new paradigm for economic integration, collaborative advancement, and shared achievements. The rise of Artificial Intelligence (AI) has significantly accelerated the global processes of digitization, informatization, and intelligence. Exploring how AI can leverage inherent characteristics to influence the development trajectory of IE is a topic that warrants further investigation. Given AI's increasing prominence and role within IE, the paper analyzes this new form, examining both AI's unique contributions to IE and its potential challenges. Firstly, the paper synthesizes the conceptual frameworks surrounding IE, decomposing them into manifestations in physical, social, and thinking spaces. Furthermore, the concept of Artificial Intelligence IE (AIIE) is introduced from a spatial perspective, with an exploration of the characteristics AI contributes to IE. Subsequently, the paper employs an evolutionary perspective to analyze the roles provided by AI during different development periods of AIIE. The paper then verifies the feasibility, effectiveness, and rationality of the AIIE's definition and analyzes AIIE development from an evolutionary perspective using enterprise development examples. Finally, acknowledging AI's inherent limitations, the paper examines potential challenges facing AIIE in the future from four perspectives, aiming to identify new research avenues for the further development of AIIE.

Figures

Figures reproduced from arXiv: 2607.24589 by Chengzhen Ma, Dan Zhang, Huansheng Ning, Jia Chai, Lingfeng Mao, Rongxin Zhan, Suiping Jiang, Zhimin Zhang.

Figure 1
Figure 1. Figure 1: The Impact of AI on IE from Spatial Dimensions [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three Critical Periods and Priorities of AIIE Development [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

31 extracted references · 15 linked inside Pith

  1. [1]

    Platform for knowledge society and innovation ecosystems

    Eunika Mercier-Laurent. Platform for knowledge society and innovation ecosystems. InArtificial Intelligence for Knowledge Management: 6th IFIP WG 12.6 International Workshop, AI4KM 2018, Held at IJCAI 2018, Stockholm, Sweden, July 15, 2018, Revised and Extended Selected Papers 6, pages 34–47. Springer,

    2018

  2. [5]

    Ai in supply chain performance and resilience: a literature review

    Mariam Atwani, Mustapha Hlyal, Jamila El Alami, et al. Ai in supply chain performance and resilience: a literature review. In13ème CONFERENCE INTERNATIONALE DE MODELISATION, OPTIMISATION ET SIMULATION (MOSIM2020), 12-14 Nov 2020, AGADIR, Maroc,

    2020

  3. [7]

    State of the art and future trends of optimality and adaptability articulated mechanisms for manufacturing control systems

    Jose-Fernando Jimenez, Abdhelghani Bekrar, Damien Trentesaux, Jairo R Montoya-Torres, and Paulo Leitão. State of the art and future trends of optimality and adaptability articulated mechanisms for manufacturing control systems. In 2013 IEEE International Conference on Systems, Man, and Cybernetics, pages 1265–1270. IEEE,

    2013

  4. [9]

    a good algorithm does not steal–it imitates

    Zongyu Yin, Federico Reuben, Susan Stepney, and Tom Collins. “a good algorithm does not steal–it imitates”: The originality report as a means of measuring when a music generation algorithm copies too much. InArtificial Intelligence in Music, Sound, Art and Design: 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, ...

    2021

  5. [10]

    Intellectual properties of artificial creativity: dismantling originality in european’s legal framework

    Beatriz A Ribeiro. Intellectual properties of artificial creativity: dismantling originality in european’s legal framework. InIntelligent Data Engineering and Automated Learning–IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II 21, pages 379–389. Springer,

    2020

  6. [11]

    David Linke and David Petrlík. ‘copyright work and its definition with regard to originality and ai’–conference report on the fourth binational seminar of tu dresden and charles university in prague, 27 june 2019.GRUR International, 69(1):39–45,

    2019

  7. [12]

    Humans as creativity gatekeepers: Are we biased against ai creativity?Journal of Business and Psychology, 39(3):643–656, 2024a

    Federico Magni, Jiyoung Park, and Melody Manchi Chao. Humans as creativity gatekeepers: Are we biased against ai creativity?Journal of Business and Psychology, 39(3):643–656, 2024a. Chiara Longoni, Andrey Fradkin, Luca Cian, and Gordon Pennycook. News from generative artificial intelligence is believed less. InProceedings of the 2022 ACM Conference on Fai...

    2022

  8. [14]

    Can machines design? an artificial general intelligence approach

    Andreas M Hein and Hélene Condat. Can machines design? an artificial general intelligence approach. InArtificial General Intelligence: 11th International Conference, AGI 2018, Prague, Czech Republic, August 22-25, 2018, Proceedings 11, pages 87–99. Springer,

    2018

  9. [15]

    The conditions of artificial general intelligence: logic, autonomy, resilience, integrity, morality, emotion, embodiment, and embeddedness

    Yoshihiro Maruyama. The conditions of artificial general intelligence: logic, autonomy, resilience, integrity, morality, emotion, embodiment, and embeddedness. InArtificial General Intelligence: 13th International Conference, AGI 2020, St. Petersburg, Russia, September 16–19, 2020, Proceedings 13, pages 242–251. Springer,

    2020

  10. [16]

    A framework for searching for general artificial intelligence

    Marek Rosa, Jan Feyereisl, and The GoodAI Collective. A framework for searching for general artificial intelligence. arXiv preprint arXiv:1611.00685,

    Pith/arXiv arXiv

  11. [17]

    2020 survey of artificial general intelligence projects for ethics, risk, and policy.Global Catastrophic Risk Institute Technical Report, pages 20–1,

    McKenna Fitzgerald, Aaron Boddy, and Seth D Baum. 2020 survey of artificial general intelligence projects for ethics, risk, and policy.Global Catastrophic Risk Institute Technical Report, pages 20–1,

    2020

  12. [19]

    Opendatalab: Empowering general artificial intelligence with open datasets.arXiv preprint arXiv:2407.13773,

    Conghui He, Wei Li, Zhenjiang Jin, Chao Xu, Bin Wang, and Dahua Lin. Opendatalab: Empowering general artificial intelligence with open datasets.arXiv preprint arXiv:2407.13773,

    Pith/arXiv arXiv

  13. [20]

    Defining functional models of artificial intelligence solutions to create a library that an artificial general intelligence can use to increase general problem solving ability

    Andy E Williams. Defining functional models of artificial intelligence solutions to create a library that an artificial general intelligence can use to increase general problem solving ability. 2020b. Jeff Clune. Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence.arXiv preprint arXiv:1905.10985,

    Pith/arXiv arXiv 1905

  14. [22]

    From machine learning to artificial general intelligence: A roadmap and implications

    Omar Ibrahim Obaid. From machine learning to artificial general intelligence: A roadmap and implications. Mesopotamian Journal of Big Data, 2023:81–91,

    2023

  15. [23]

    The future of human-ai collaboration: a taxonomy of design knowledge for hybrid intelligence systems.arXiv preprint arXiv:2105.03354,

    Dominik Dellermann, Adrian Calma, Nikolaus Lipusch, Thorsten Weber, Sascha Weigel, and Philipp Ebel. The future of human-ai collaboration: a taxonomy of design knowledge for hybrid intelligence systems.arXiv preprint arXiv:2105.03354,

    Pith/arXiv arXiv

  16. [24]

    Human digital twins: Two-layer machine learning architecture for intelligent human-machine collaboration

    Wael Hafez. Human digital twins: Two-layer machine learning architecture for intelligent human-machine collaboration. InIntelligent Human Systems Integration 2020: Proceedings of the 3rd International Conference on Intelligent Human Systems Integration (IHSI 2020): Integrating People and Intelligent Systems, February 19-21, 2020, Modena, Italy, pages 627–...

    2020

  17. [25]

    A human-cyber-physical system toward intelligent wind turbine operation and maintenance.Sustainability, 13(2):561, 2021a

    Xiao Chen, Martin A Eder, ASM Shihavuddin, and Dan Zheng. A human-cyber-physical system toward intelligent wind turbine operation and maintenance.Sustainability, 13(2):561, 2021a. 35 Artificial Intelligence and Innovation EcosystemA PREPRINT Yanyan Dong, Jie Hou, Ning Zhang, and Maocong Zhang. Research on how human intelligence, consciousness, and cogniti...

    2020

  18. [27]

    A review on building blocks of decentralized artificial intelligence.arXiv preprint arXiv:2402.02885,

    Vid Kersic and Muhamed Turkanovic. A review on building blocks of decentralized artificial intelligence.arXiv preprint arXiv:2402.02885,

    Pith/arXiv arXiv

  19. [28]

    Multi-agent systems and decentralized artificial superintelligence.arXiv preprint arXiv:1702.08529,

    S Ponomarev and AE V oronkov. Multi-agent systems and decentralized artificial superintelligence.arXiv preprint arXiv:1702.08529,

    Pith/arXiv arXiv

  20. [29]

    Security and privacy issues in deep learning.arXiv preprint arXiv:1807.11655,

    Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, and Sungroh Yoon. Security and privacy issues in deep learning.arXiv preprint arXiv:1807.11655,

    Pith/arXiv arXiv

  21. [30]

    Security and privacy for artificial intelligence: Opportunities and challenges.arXiv preprint arXiv:2102.04661,

    Ayodeji Oseni, Nour Moustafa, Helge Janicke, Peng Liu, Zahir Tari, and Athanasios Vasilakos. Security and privacy for artificial intelligence: Opportunities and challenges.arXiv preprint arXiv:2102.04661,

    Pith/arXiv arXiv

  22. [31]

    Privacy-preserving ai services through data decentralization

    Christian Meurisch, Bekir Bayrak, and Max Mühlhäuser. Privacy-preserving ai services through data decentralization. InProceedings of The Web Conference 2020, pages 190–200,

    2020

  23. [32]

    Phikon-v2, a large and public feature extractor for biomarker prediction.arXiv preprint arXiv:2409.09173,

    Alexandre Filiot, Paul Jacob, Alice Mac Kain, and Charlie Saillard. Phikon-v2, a large and public feature extractor for biomarker prediction.arXiv preprint arXiv:2409.09173,

    Pith/arXiv arXiv

  24. [2016]

    A promising path towards autoformalization and general artificial intelligence

    Christian Szegedy. A promising path towards autoformalization and general artificial intelligence. InIntelligent Computer Mathematics: 13th International Conference, CICM 2020, Bertinoro, Italy, July 26–31, 2020, Proceedings 13, pages 3–20. Springer,

    2020

  25. [2018]

    A conceptual bio-inspired framework for the evolution of artificial general intelligence.arXiv preprint arXiv:1903.10410,

    Sidney Pontes-Filho and Stefano Nichele. A conceptual bio-inspired framework for the evolution of artificial general intelligence.arXiv preprint arXiv:1903.10410,

    Pith/arXiv arXiv 1903

  26. [2019]

    Decentralization of artificial intelligence: analyzing developments in decentralized learning and distributed ai networks.arXiv preprint arXiv:1603.04467,

    Ishan Gupta. Decentralization of artificial intelligence: analyzing developments in decentralized learning and distributed ai networks.arXiv preprint arXiv:1603.04467,

    Pith/arXiv arXiv

  27. [2020]

    The innovation paradox: Concept space expansion with diminishing originality and the promise of creative ai.arXiv preprint arXiv:2303.13300,

    Serhad Sarica and Jianxi Luo. The innovation paradox: Concept space expansion with diminishing originality and the promise of creative ai.arXiv preprint arXiv:2303.13300,

    Pith/arXiv arXiv

  28. [2021]

    Agile, antifragile, artificial-intelligence- enabled, command and control.arXiv preprint arXiv:2109.06874,

    Jacob Simpson, Rudolph Oosthuizen, Sondoss El Sawah, and Hussein Abbass. Agile, antifragile, artificial-intelligence- enabled, command and control.arXiv preprint arXiv:2109.06874,

    Pith/arXiv arXiv

  29. [2022]

    The processes of ecosystem emergence

    Llewellyn DW Thomas and Erkko Autio. The processes of ecosystem emergence. InAcademy of management proceedings, volume 2015, page 10453. Academy of Management Briarcliff Manor, NY 10510,

    2015

  30. [2023]

    Evaluation of key impression of resilient supply chain based on artificial intelligence of things (aiot).arXiv preprint arXiv:2207.13174,

    Alireza Aliahmadi, Hamed Nozari, Javid Ghahremani-Nahr, and Agnieszka Szmelter-Jarosz. Evaluation of key impression of resilient supply chain based on artificial intelligence of things (aiot).arXiv preprint arXiv:2207.13174,

    Pith/arXiv arXiv

  31. [2024]

    Cooperative resilience in artificial intelligence multiagent systems.arXiv preprint arXiv:2409.13187,

    Manuela Chacon-Chamorro, Luis Felipe Giraldo, Nicanor Quijano, Vicente Vargas-Panesso, César González, Juan Se- bastián Pinzón, Rubén Manrrique, Manuel Ríos, Yesid Fonseca, Daniel Gómez-Barrera, et al. Cooperative resilience in artificial intelligence multiagent systems.arXiv preprint arXiv:2409.13187,

    Pith/arXiv arXiv