Morten Goodwin

• Grundetjern, Morten; Andersen, Per-Arne & Goodwin, Morten (2025). Synthetic Personas: Enhancing Demographic Response Simulation through Large Language Models and Genetic Algorithms. International Journal on Cybernetics & Informatics. ISSN 2320-8430. 14(2), p. 21–41. doi: 10.5121/ijci.2025.140202. Full text in Research Archive Show summary

  Understanding diverse demographic groups presents a significant challenge in market research. In this paper, we introduce a novel system that integrates large language models with genetic algorithms to create synthetic personas capable of generating feedback that approximates real-world human responses. Our experimental evaluation demonstrates that synthetic personas not only exhibit age-differentiated technology usage patterns consistent with documented trends but also benefit from genetic algorithm optimization, which improves response accuracy from 60.4% to 78.5% on training questions and from 62.6% to 68.8% on hidden questions—outperforming human estimators. Moreover, the optimized personas achieve a 51.1% better correspondence with actual income distributions compared to random profiles. This approach makes it possible to rapidly generate feedback without requiring participants, facilitates iterative follow-ups, and systematically enhances demographic representativeness.

• Voigt, Jakob Michael; Grundetjern, Morten; Andersen, Per-Arne & Goodwin, Morten (2025). DESS: Dimensional-PDFs for Embedding Space Sampling. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 16301, p. 3–17. doi: 10.1007/978-3-032-11402-0_1. Full text in Research Archive Show summary

  Probabilistic sampling in machine learning models is a technique for introducing beneficial randomness and variability during output generation. It enhances output diversity and creativity, particularly in tasks where slight variations can lead to novel and interesting results. Large Language Models (LLMs), for instance, use this technique to generate varied, contextually appropriate responses that mirror the flexibility of human language. This paper presents a novel method for training a neural network to output the defining parameters of one Probability Density Function (PDF) for each dimension in an embedding space. Our approach, Dimensional-PDFs for Embedding Space Sampling (DESS), leverages these PDFs at inference time to sample from an embedding space, offering a new approach to probabilistic sampling in model outputs. Our method employs simple loss functions and a multi-step loss computation to learn both mean and variance parameters without requiring sampling during training, making it computationally efficient and easy to integrate with existing architectures. We validate DESS through controlled experiments on synthetic datasets, demonstrating accurate approximations of ground-truth distribution parameters across varying Gaussian target distributions. The framework has potential applications in large-vocabulary models where it could enable significant parameter reductions while maintaining calibrated uncertainty estimates. We present DESS using normal distributions, though the approach can be extended to other distribution types for specific sampling requirements.

• Brådland, Henrik; Goodwin, Morten; Andersen, Per-Arne; Nossum, Alexander & Gupta, Aditya (2025). A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking. SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. doi: 10.1145/3726302.3729882. Full text in Research Archive Show summary

  Document chunking fundamentally impacts Retrieval-Augmented Generation (RAG) by determining how source materials are segmented before indexing. Despite evidence that Large Language Models (LLMs) are sensitive to the layout and structure of retrieved data, there is currently no framework to analyze the impact of different chunking methods. In this paper, we introduce a novel methodology that defines essential characteristics of the chunking process at three levels: intrinsic passage properties, extrinsic passage properties, and passages-document coherence. We propose HOPE (Holistic Passage Evaluation), a domain-agnostic, automatic evaluation metric that quantifies and aggregates these characteristics. Our empirical evaluations across seven domains demonstrate that the HOPE metric correlates significantly (𝜌 > 0.13) with various RAG performance indicators, revealing contrasts between the importance of extrinsic and intrinsic properties of passages. Semantic independence between passages proves essential for system performance with a performance gain of up to 56.2% in factual correctness and 21.1% in answer correctness. On the contrary, traditional assumptions about maintaining concept unity within passages show minimal impact. These findings provide actionable insights for optimizing chunking strategies, thus improving RAG system design to produce more factually correct responses.

• Holen, Martin; Rogovchenko, Svitlana; Noorsumar, Gulshan & Goodwin, Morten (2025). Predicting Vehicle Impact Severity With Deep Neural Network for Reinforcement Learning Based Autonomous Vehicle Simulators. IEEE Conference on Emerging Technologies and Factory Automation. ISSN 1946-0740. p. 1553–1558. doi: 10.1109/ICMLA61862.2024.00239. Full text in Research Archive Show summary

  Road accidents leads to a significant number of deaths in the world, with human error being attributed as one of the roots of the problem. One of the possible alternatives to address the issue is the development of autonomous vehi-cles. Of the many technological advancements in the field of autonomous vehicles, Reinforcement Learning (RL) has shown progress because its methodology is not reliant on human data. However, one of its challenges is the definition of the reward function, which is critical to RL algorithms. In this context, determining the severity of a crash allows for an improvement in the reward function, especially in simulators. In this study, we present a Deep Neural Network (DNN) based model that predicts the crash impact severity in terms of vehicle deformation during the event, along with the de-formation we create a reward strategy for RL- based vehicle simulators. The model is developed based on data generated from finite element simulations replicating a full-frontal vehicle impact against a rigid barrier. We compare the trained model with a variety of NN methods, and different input data. The Neural Network model performs well, supporting the development of a reward system to improve RL-based autonomous vehicle simulators.

• Grundetjern, Morten; Andersen, Per-Arne; Goodwin, Morten & Borgersen, Karl Audun (2024). NER Explainability Framework: Utilizing LIME to Enhance Clarity and Robustness in Named Entity Recognition. In Bramer, Max & Stahl, Frederic (Ed.), Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024. Springer. ISSN 9783031779145. p. 3–15. doi: 10.1007/978-3-031-77915-2_1. Full text in Research Archive Show summary

  Abstract. Named Entity Recognition (NER) is crucial for many Nat- ural Language Processing (NLP) applications, yet current models of- ten lack explainability and robustness, particularly against lexical varia- tions. While methods like Local Interpretable Model-agnostic Explana- tions (LIME) have enhanced explainability in text classification, their application to sequence-based tasks like NER remains an open chal- lenge. This paper introduces the NER Explainability Framework (NEF), a novel approach that transforms the sequence-based NER task into a multi-classification problem. This transformation enables the application of LIME to any NER architecture. NEF provides precise, interpretable insights into model decisions by highlighting which parts of the input text most influence tagging decisions, significantly advancing explainability in NER. To address the critical gap in assessing model resilience, we also present the Misspelling Robustness Score (MRS), a new metric designed to quantify NER model resilience against lexical variations, particularly misspellings. MRS offers a comprehensive evaluation of model perfor- mance in the face of spelling variations and errors. We apply NEF to four widely used NER models (SpaCy, Flair, Stanza, and RoBERTa) using the CoNLL-2003 dataset, revealing critical patterns and weaknesses in model behavior. Our method enables targeted improvements, demonstrated by fine-tuning the SpaCy model to achieve a significant 17.5% increase in robustness against misspellings. These findings lay the groundwork for developing more robust, accurate, and interpretable NER systems, pro- viding practical strategies to address existing challenges in NLP. The universal applicability of NEF across NER architectures and the insights provided by MRS offers valuable tools for researchers and practitioners in advancing the field of NER.

• Brådland, Henrik; Goodwin, Morten; Andersen, Per-Arne & Nossum, Alexander (2024). PlanBERT: From Messy Zonal Plans to Informative Vector Embeddings. In Bramer, Max & Stahl, Frederic (Ed.), Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024. Springer. ISSN 9783031779145. p. 175–188. doi: 10.1007/978-3-031-77915-2_13. Full text in Research Archive Show summary

  Text embedding models trained on vast web-scraped corpus generalize well to daily language. However, they often fall short when applied in specialized domains that require precise language and foreign terms, like law and medicine. This gap highlights the necessity for data-efficient methodologies to fine-tune these models for narrow-domain applications. This paper introduces PlanBERT, a new approach for enabling data-efficient domain adaptation and fine-tuning of embedding models. The approach builds on self-supervised contrastive pre-training, synthetic training data generated by large language models (LLMs), and decorrelation of embedding features. The paper also introduces the term “informative vector embeddings” to adjust the training objectives to incentivise more analytics-friendly embeddings and demonstrate that PlanBERT can learn the domain language of zonal plans and outperform larger and more complex state-of-the-art models in challenging real-world zonal plan tasks.

• Biermann, Daniel; Palumbo, Fabrizio; Goodwin, Morten & Granmo, Ole-Christoffer (2024). Streamlining Attention for Text Classification: Sequence Length Reduction with Pooling Attention. In Bramer, Max & Stahl, Frederic (Ed.), Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024. Springer. ISSN 9783031779145. p. 61–73. doi: 10.1007/978-3-031-77915-2_5. Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Granmo, Ole-Christoffer (2024). Towards safe and sustainable reinforcement learning for real-time strategy games. Information Sciences. ISSN 0020-0255. 679. doi: 10.1016/j.ins.2024.120980. Full text in Research Archive

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• Følstad, Asbjørn; Araujo, Theo; Papadopoulos, Symeon; Law, Effie L.-C.; Luger, Ewa & Goodwin, Morten [Show all 8 contributors for this article] (2024). Chatbot Research and Design: 7th International Workshop, CONVERSATIONS 2023, Oslo, Norway, November 22–23, 2023, Revised Selected Papers. Springer. ISBN 9783031549755. 221 p. Full text in Research Archive

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• Løvvik, Ole Martin; Bratlie, Sigrid; Goodwin, Morten & Vidnes, Aksel Kjær (2026). Årets forskningsgjennombrudd - Abels tårn - NRK Radio. [Radio]. NRK P2. Full text in Research Archive Show summary

  Torkild Jemterud og hans panel ser på 2025s forskningsresultater. Har datamaskiner bestått Turing-testen? Lever nakenrotter evig?

• Goodwin, Morten; Mørch, Hedda Hassel; Malthe-Sørenssen, Anders & Inntjore, Hilde (2025). Panelsamtale: Hva gjør KI med høyere utdanning, og hva gjør høyere utdanning med KI? Full text in Research Archive
• Goodwin, Morten; Borgersen, Karl Audun Kagnes & Omslandseter, Rebekka Olsson (2025). Forsket på KI og strømforbruk: – Skremselspropaganda vi må få bukt med – NRK Sørlandet – Lokale nyheter, TV og radio. Full text in Research Archive Show summary

  Professor Morten Goodwin har undersøkt strømforbruket til vanlige KI-søk. Han mener teknologien feilaktig blir omtalt som miljøversting, men møter motstand.

• Goodwin, Morten; Borgersen, Karl Audun Kagnes & Omslandseter, Rebekka Olsson (2025). Kunstig intelligens får skylden for et enormt strømforbruk den ikke har. doi: https:/www.aftenposten.no/meninger/debatt/i/B05a6G/kunstig-intelligens-faar-skylden-for-et-enormt-stroemforbruk-den-ikke-har. Full text in Research Archive Show summary

  Språkmodeller er offer for grov overdrivelse når det gjelder strøm om ressursforbruk.

• Kristoffersen, Kai Jæger; Viken, Oda; Goodwin, Morten; Wetzer, Elisabeth; Tung, Karianne Oldernes & Vennerød, Christian [Show all 7 contributors for this article] (2025). Nekter å svare om umerkede puslespill er KI-generert: – De bør være ærlige. [Journal]. https://www.nrk.no/. Full text in Research Archive
• Goodwin, Morten; Nærland, Mina Hauge & Bjørkeng, Per Kristian (2024). Ungt tech-geni frykter at KI tar over innen fem år. [Journal]. Aftenposten. Full text in Research Archive
• Goodwin, Morten & Johansen, Ina Gravem (2024). Ikke tro det når du ser det. [Journal]. Folkebladet. Full text in Research Archive
• Goodwin, Morten (2024). Hvordan din HR-avdeling kan dra nytte av AI. [Internet]. HRNorge. Full text in Research Archive
• Sørnes, Astrid Johanne; Klevjer, Carl August; Goodwin, Morten; Strumke, Inga & Lakhani, Karim L. (2024). Ekspertar trur 2024 blir året for «deepfakes». [Internet]. NRK. Full text in Research Archive
• Goodwin, Morten (2024). Intercitymøte med tema kunstig intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Robotenes Revolusjon: Sikkerhet eller Sårbarhet? Full text in Research Archive
• Goodwin, Morten & Tønset, Arne Egil (2024). Kunstig intelligens i Krig. [Internet]. Helgemorgen TV. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens: En ny ære for reiselivet. Full text in Research Archive
• Goodwin, Morten (2024). Den store serien om KI (6:10) -Slår KI pokereliten? [Radio]. NRK Abelstårn. Full text in Research Archive
• Goodwin, Morten & Kjørstad, Elise (2024). En robot som hjælper? Her er forhindringerne ifølge forskere. [Internet]. Videnskab DK. Full text in Research Archive
• Goodwin, Morten (2024). Deepfake nakenbilder og videoer spres av norske kjendiser. [Internet]. Nettavisen. Full text in Research Archive
• Goodwin, Morten (2024). Understanding AI's impact on visual effects and animations. Full text in Research Archive
• Goodwin, Morten (2024). Sannheten om kunstig intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens + skole = sant. Full text in Research Archive
• Goodwin, Morten (2024). Mer muntlige utspørring i fremtiden. [Radio]. NRK sørlandsendingen P1. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens – hva nå? Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens med Morten Goodwin. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens, – velsignelse eller forbannelse? Full text in Research Archive
• Andersen, Per-Arne; Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Vi spår kunstig intelligens-året 2024 og fem år frem i tid. [Internet]. Podcast Addict. Full text in Research Archive
• Goodwin, Morten (2024). Fremtidens Offentlig Forvaltning med Kunstig Intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens og ChatGPT i praksis. Full text in Research Archive
• Goodwin, Morten; Johansen, Christina Engen; Robstad, Finn Erik & Habbestad, Sveinung (2024). Video sjokkerer: – Det er skremmende. [Internet]. TV2 Nyheter. Full text in Research Archive
• Goodwin, Morten (2024). En ny ære for leverandører. Full text in Research Archive
• Goodwin, Morten (2024). Hvordan forholder vi oss til kunstig intelligens? [Internet]. Skolelederen. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens - hvordan påvirker teknologien arbeidslivet og utdanningsinstitusjoner? Full text in Research Archive
• Goodwin, Morten (2024). Hva er sannheten om kunstig intelligens, og hvordan kommer det til å forandre skolen i fremtiden? Full text in Research Archive
• Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Stanford KI-indeks: Trenger of Transformasjoner I KI-landskapet. [Internet]. Spotify. Full text in Research Archive
• Opalic, Sven Myrdahl & Goodwin, Morten (2024). Kunstig intelligens sørger for at det trengs mye mindre energi for å holde maten din kald. [Internet]. Nationen. Full text in Research Archive
• Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Fra ChatGPT til Gemini: Navigering i chatbot-universets mangfold. [Internet]. Spotify. Full text in Research Archive
• Goodwin, Morten (2024). KI og fremtidens kultursektor. Full text in Research Archive
• Goodwin, Morten & Strømme, Simen Hunding (2024). Skattemeldingen er en gullgruve for kriminelle. [TV]. TV2. Full text in Research Archive
• Goodwin, Morten (2024). Fremtiden for filmproduksjon. [Internet]. Podcast: Kunstig intelligens med Morten Goodwin. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens: til inspirasjon. Full text in Research Archive
• Goodwin, Morten (2024). Følelser og bevissthet. [Internet]. Næringsforeningen i Kristiansand Regionen (NIKR). Full text in Research Archive
• Goodwin, Morten (2024). Peiling på Kunstig Intelligens. Full text in Research Archive
• Goodwin, Morten & Hofer, Manuela (2024). Morten Goodwin - Professor i kunstig intelligens. [Internet]. Spotify. Full text in Research Archive
• Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Kunstig intelligens med Morten Goodwin. [Internet]. Spotify. Full text in Research Archive
• Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Generell kunstig intelligens er nærmere enn du tror. [Internet]. Spotify. Full text in Research Archive
• Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Bør vi slette Facebook. [Internet]. Spotify. Full text in Research Archive
• Goodwin, Morten & Johansen, John Martin (2024). Nå kommer skattemeldingen – da er det høysesong for nettsvindel. [Internet]. Dokument.no. Full text in Research Archive
• Opalic, Sven Myrdahl; Goodwin, Morten & Wehus, Walter Norman (2024). Slik holder maten din seg kald med halve energien. [Internet]. Forskning.no. Full text in Research Archive
• Goodwin, Morten & Arnesen, Mats (2024). Student ble tatt for KI-språkvask — advokat advarer andre. [Internet]. Khrono. Full text in Research Archive
• Goodwin, Morten (2024). Gemini vs ChatGPT: KI-konkurransen tilspisser seg. [Internet]. Podcast: Kunstig intelligens med Morten Goodwin. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens: En ny æra i eiendomsforvaltning. Full text in Research Archive
• Goodwin, Morten (2024). How can Open Data from parliaments and usage of AI bring us forward in democracy? Full text in Research Archive
• Følstad, Asbjørn; Araujo, Theo; Papadopoulos, Symeon; Law, Effie L.-C.; Luger, Ewa & Goodwin, Morten [Show all 8 contributors for this article] (2024). Preface. In Følstad, Asbjørn; Araujo, Theo; Papadopoulos, Symeon; Law, Effie L.-C.; Luger, Ewa; Goodwin, Morten; Hobert, Sebastian & Brandtzæg, Petter Bae (Ed.), Chatbot Research and Design: 7th International Workshop, CONVERSATIONS 2023, Oslo, Norway, November 22–23, 2023, Revised Selected Papers. Springer. ISSN 9783031549755. p. v–vii. doi: 10.1007/978-3-031-54975-5. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens: En ny tid for røde kors. Full text in Research Archive
• Goodwin, Morten (2024). Utfordringer og muligheter med kunstig intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens, en ny tid for banken. Full text in Research Archive
• Goodwin, Morten & Vaaland, Mattis (2024). AI-ekspertens beste tips for 2024: – Lær deg nevrale nettverk! [Internet]. Kode24. Full text in Research Archive
• Wehus, Walter Norman & Goodwin, Morten (2024). Forskere spår 10 måter kunstig intelligens vil prege de neste årene: – Jeg tror ikke eksamen vil finnes om fem år. [Internet]. Forskning.no. Full text in Research Archive
• Goodwin, Morten & Olsen, Anders Stensland (2024). Seks av ti lærere har tatt elever i juks. [Internet]. Klar tale. Full text in Research Archive
• Goodwin, Morten (2024). Hemmeligheten med å skille teknologisk fremskritt fra tullete fiksjon. Full text in Research Archive
• Goodwin, Morten (2024). Kampen mot desinformasjon. [Journal]. Fædrelandsvennen. Full text in Research Archive
• Goodwin, Morten (2024). Fremtiden med kunstig intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Slik kan kommuner bli kunstig intelligens-katalysator. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens + skole = Sant. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens + skole = Sant. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens forandrer reiselivet. Full text in Research Archive
• Goodwin, Morten & Pettersen, Jan (2024). Forskere spår om kunstig intelligens: «Jeg tror ikke eksamen vil finnes om fem år». [Internet]. Dokument.no. Full text in Research Archive
• Goodwin, Morten (2024). Kan romlig databehandling eller dypbløff bli årets ord? [Internet]. Digital Norway. Full text in Research Archive Show summary

• Goodwin, Morten (2024). Hemmeligheten med å få til kunstig intelligens i framtidas skoler. Full text in Research Archive Show summary

• Goodwin, Morten; Vries, Robin de; Moelans, Robrecht; Pauly, Klaas & Hill, Donal (2024). Leveraging AI for Plastic Pollution Detection and Monitoring. Full text in Research Archive Show summary

• Goodwin, Morten & Bjørkeng, Per Kristian (2024). Nå kommer KI som kan lære alt om deg og ditt. [Journal]. Aftenposten. Full text in Research Archive
• Goodwin, Morten (2024). AI in the realm of fake speech: Capabilities and challenges. Full text in Research Archive
• Goodwin, Morten (2024). Kort fortalt: Hva trenger sykepleierstudenter å vite om kunstig intelligens? [Internet]. Pedagogisk snakkis. Full text in Research Archive Show summary

• Goodwin, Morten (2024). Hvordan påvirker kunstig intelligens deg som leder? [Internet]. ledernytt.no. Full text in Research Archive
• Goodwin, Morten (2024). Meta utsetter omstridt KI-grep. [Radio]. NRK Helgemorgen. Full text in Research Archive
• Goodwin, Morten (2024). Senteret for Offentlig Sektor Kunstig Intelligens (SOSKI). Full text in Research Archive
• Goodwin, Morten (2024). Politisk parti har laga falsk video av statsministeren. [Internet]. NRK. Full text in Research Archive
• Goodwin, Morten (2024). Slik har du ikke sett statsråden før: tok en Marilyn Monroe. [Journal]. Grimstad Adressetidende. Full text in Research Archive
• Goodwin, Morten (2024). En nøkkel til gjennombrudd i KI-forskningen. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens -- en ny tid for forsikring. Full text in Research Archive
• Goodwin, Morten (2024). Løft blikket - hva skal vi gjøre nå. Full text in Research Archive
• Goodwin, Morten & Bjørkeng, Per Kristian (2024). KI som er bedre enn deg skulle være flere tiår unna. Nå tror ekspertene de er rett rundt hjørnet. [Journal]. Aftenposten. Full text in Research Archive
• Goodwin, Morten (2024). KI-stunt i Debatten – klarer du se forskjell? [TV]. NRK. Full text in Research Archive
• Goodwin, Morten (2024). Nøkkelen til kunstig intelligens. Full text in Research Archive
• Goodwin, Morten (2024). Er Næringene i Arendal klar for Kunstig Intelligens? Full text in Research Archive
• Goodwin, Morten (2024). Er kunstig intelligens slutten. [TV]. NRK Debatten. Full text in Research Archive
• Goodwin, Morten & Pettersen, Jan (2024). Må vi revurdere eksamen på grunn av kunstig intelligens? [Journal]. Document.no. Full text in Research Archive
• Goodwin, Morten (2024). 22-åring har satt fyr på Silicon Valley. [Journal]. Aftenposten. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens og mulighetene for NAV. Full text in Research Archive
• Goodwin, Morten (2024). Kunstig intelligens i media. Full text in Research Archive
• Goodwin, Morten & Skogedal, Magnus Nødland (2024). Kan ChatGPT bli din nye personlige trener? [Internet]. forskning.no. Full text in Research Archive
• Grønås-Werring, Morten; Sand, Kari; Bjørlo, Lena Vatne; Khovrak, Inna; Rom, Iselin Bøge & Thaulow, Kristin [Show all 10 contributors for this article] (2025). Kompetanse og digital teknologi: Sluttrapport fra utredningen. Oxford Research. doi: https:/oxfordresearch.no/publications/3530/. Full text in Research Archive Show summary

  Prosjektet har kartlagt hvordan fremvoksende digitale teknologier som kunstig intelligens, skytjenester, robotisering, smartoveråking og fjernstyring samt utvidet og virtuell virkelighet tas i bruk i arbeidslivet og hvordan bruken påvirker innholdet i arbeidsoppgaver og jobber. Formålet har vært å belyse hvordan disse endringene påvirker behovet for kompetanse og hvilke strategier arbeidsgivere og arbeidstakere benytter for å møte nye krav og forventninger. Vi har også undersøkt hva som hemmer og fremmer kompetanseutvikling i møte med ny digital teknologi. Rapporten bygger på spørreundersøkelser rettet mot både arbeidsgivere og ansatte, utført av Norstat, samt case-studier innenfor næringene helse- og omsorg, industri, varehandel og informasjon og kommunikasjon. I tillegg er flere enn 11 000 stillingsannonser fra Navs stillingsdatabase analysert ved hjelp av KI for å undersøke hva slags digital kompetanse arbeidsgivere etterspør i rekrutteringsprosesser. Rapportens hovedfunn er at jobber i hovedsak endrer innhold, snarere enn å forsvinne som følge av automatisering. De fleste, både blant arbeidsgivere og arbeidstakere, opplever også at virksomhetene henger med i utviklingen. Ansatte ser i hovedsak ut til å møte nye kompetansebehov som oppstår. Samtidig ser vi tendenser til at det blir økende «strekk i laget», særlig med hensyn til kompetanse innen kunstig intelligens. Det er en generell tendens til at ansatte med lav kompetanse er mindre tilbøyelige til å delta i opplæring og til å prøve seg fram. Virksomheter som ser ut til å lykkes best med kompetanseutvikling i møte med digital teknologi lager gode rammer og systemer rundt uformell læring: De bygger opp ansatte med interesse, lærevillighet og et godt kompetansemessig utgangspunkt og lar dem innta mer formaliserte roller, der de kan bidra til å spre læringen i organisasjonen. De kombinerer strategier for å sikre et grunnplan av felles forståelse og kunnskap med spesialiserte utviklingsmuligheter for dem som har behov for spisskompetanse. Vi peker til slutt på at økt digitalisering også innebærer økt sårbarhet. Trusselbildet i det digitale rom og den geopolitiske utviklingen skaper et mer akutt behov for grunnleggende digital kompetanse i det brede lag av befolkningen. Behovet for spesialistkompetanse kan også øke, for å bygge opp europeiske og nasjonale alternativer til amerikanske og kinesiske teknologiplattformer. Etter en lang periode med stabil, institusjonalisert internasjonal utveksling, som gjorde det enkelt for næringsliv og akademia å rekruttere, samarbeide og spesialisere seg på tvers av landegrenser, gir innføring av tollmurer og handelsbarrierer færre muligheter til å lene seg på internasjonal arbeidsdeling. Det gir behov for å definere hva som utgjør «kritisk nasjonal kompetanse», som et supplement til kritisk infrastruktur, og å sørge for at Norge har tilgang på slik kompetanse.

• Grimsmo, Andreas & Goodwin, Morten (2025). Norwegian High Performance Computing Needs. Zenodo. doi: 10.5281/ZENODO.18979566. Full text in Research Archive Show summary

  This report presents the findings of Work Package 2 (WP2) of the Norwe- gian Artificial Intelligence Cloud (NAIC) project, which aims to build a robust AI/ML infrastructure in Norway. Funded by the Norwegian Research Council, the initiative focuses on democratizing access to High-Performance Computing (HPC) resources, enhancing AI research and education, and supporting innova- tion across academia and industry.

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