Morten Goodwin

Professor

Institutt for informasjons- og kommunikasjonsteknologi

E-post: morten.goodwin@uia.no
Telefon: +47 37233105
Mobiltelefon: +4795248679

Kontor A3061 (Jon Lilletuns vei 9, 4879 Grimstad, Norway)

Forskningsgrupper

Ansvarsområde

Morten Goodwin er professor i kunstig intelligens ved Universitetet i Agder og har en stilling som professor 2 ved OsloMet. Han er medgründer og ledende forsker hos AI Experts, og fungerer som nestleder ved Senter for kunstig intelligensforskning. Han er anerkjent som en av Norges fremste eksperter innen kunstig intelligens. Goodwin kombinerer roller som professor, lærer, samfunnsdebattant, formidler, forfatter og CTO. Med mer enn 15 års akademisk erfaring, en doktorgrad og over 200 fagfellevurderte artikler, er han en dedikert forsker som fokuserer på dype nevrale nettverk. I 2023 mottok han NORA-prisen for sitt fremragende bidrag til AI-samfunnet, utdanning og formidling, og ble av Khrono listet blant de 300 mest publiserte forskerne i Norge.

Goodwin er involvert i en rekke omfattende forskningsprosjekter som dekker både grunnleggende og anvendt AI-forskning. Dette inkluderer arbeid innen sektorer som landbruk, akvakultur, kulturproduksjon, utdanning, helse, industrioptimalisering, naturlig språkbehandling og anbefalingsmotorer. Han er en entusiastisk formidler, og i egenskap av forfatter, debattant og offentlig foredragsholder, leverer han omtrent 100 populærvitenskapelige presentasjoner årlig. Hans primære fokus er på potensialet AI har å tilby og avmystifisering av hans forskningsområde.

Abouzeid, Ahmed Abdulrahem Othman; Granmo, Ole-Christoffer; Goodwin, Morten & Webersik, Christian (2024). Towards misinformation mitigation on social media: novel user activity representation for modeling societal acceptance. Journal of Computational Social Science (JCSS). ISSN 2432-2717. doi: 10.1007/s42001-024-00256-9.
Drøsdal, Didrik Kallhovd; Grimsmo, Andreas; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Exploring the Potential of Model-Free Reinforcement Learning using Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455080.
Gunvaldsen, Ole; Thorsen, Henning Blomfeld; Andersen, Per-Arne; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Towards IoT Anomaly Detection with Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455063.
Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Goodwin, Morten (2023). Natural Language Modeling with the Tsetlin Machine. I Fujita, H. (Red.), Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. . Springer. ISSN 978-3-031-36821-9. s. 141–150. doi: 10.1007/978-3-031-36822-6_12.
Biermann, Daniel; Goodwin, Morten & Granmo, Ole-Christoffer (2023). Transfer Learning Through Knowledge-Infused Representations with Contextual Experts. I Maglogiannis, Ilias; Iliadis, L.; MacIntyre, J. & Dominguez, Manuel (Red.), Artificial Intelligence Applications and Innovations. Springer. ISSN 978-3-031-34106-9. doi: 10.1007/978-3-031-34107-6_45.
Borgersen, Karl Audun Kagnes; Goodwin, Morten; Sharma, Jivitesh; Aasmoe, Tobias; Leonhardsen, Mari & Rørvik, Gro Herredsvela (2023). CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric . I Bramer, Max & Stahl, Frederic (Red.), Artificial Intelligence XL: 43rd SGAI International Conference on Artificial Intelligence, AI 2023. Springer. ISSN 978-3-031-47993-9. s. 89–102. doi: 10.1007/978-3-031-47994-6_7.
Jyhne, Sander; Andersen, Per-Arne; Goodwin, Morten & Oveland, Ivar (2023). A Contrastive Learning Scheme with Transformer Innate Patches. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, s. 103–114. doi: 10.1007/978-3-031-47994-6_8. Vis sammendrag
This paper presents Contrastive Transformer (CT), a contrastive learning scheme using the innate transformer patches. CT enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all patch-based vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is treated as an image. We apply and test CT for the case of aerial image segmentation, known for low-resolution data, large class imbalance, and similar semantic classes. We perform extensive experiments to show the efficacy of the CT scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different transformer architectures. Ultimately, the results show a consistent increase in mean Intersection-over-Union (IoU) across all classes.
Laursen, Rune Alexander; Alo, Peshang; Goodwin, Morten & Andersen, Per-Arne (2023). Distinct Sequential Models for Inference Boosting. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, s. 198–203. doi: 10.1007/978-3-031-47994-6_15. Vis sammendrag
Type 1 Diabetes (T1D) is a chronic disease where the body is unable to regulate the Blood Glucose Level (BGL), leading to severe health consequences if not regulated. Accurate BGL predictions can enable better disease management and improve treatment decisions. However, predicting future BGLs is a complex problem due to the inherent complexity and variability of the human body. This paper investigates using a new technique to outperform a State-of-the-Art (SotA) Convolutional Recurrent Neural Network (CRNN) model by forecasting BGLs on the same dataset. The problem is structured, and the data is preprocessed as a multivariate multi-step time series. The Distinct Sequential Models for Inference Boosting (DSMIB) technique is used, which manages missing data and counters potential issues from other techniques by employing both a Long Short-Term Memory (LSTM) model and a Transformer-based model together. The experimental results show that this technique reduces the Root Mean Squared Error (RMSE) by approximately 14.28% when predicting the BGL 30 min in the future compared to the SotA model. This improvement highlights the potential of this approach to assist diabetes patients with effective disease management.
Evensen, Vetle Nesland; Henriksen, Gabriel Bergman; Melhus, Sondre; Olsen, Ole Steine; Haugen, Kristina & Dolmen, Dag [Vis alle 11 forfattere av denne artikkelen] (2023). ReFrogID: Pattern Recognition for Pool Frog Identification Using Deep Learning and Feature Matching. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 14381, s. 365–376. doi: 10.1007/978-3-031-47994-6_33. Vis sammendrag
The global decline in amphibian populations is a pressing issue, with numerous species facing the threat of extinction. One such species is the pool frog, Pelophylax lessonae, whose Norwegian population has experienced a significant long-term decline since monitoring began in 1996. This decline has pushed the species to the verge of local extinction. A substantial knowledge gap in the species’ biology hinders the proposal and evaluation of effective management actions. Consequently, there is a pressing need for efficient techniques to gather data on population size and composition. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in various domains, including ecology and evolution. Current research in these fields primarily focuses on species modeling, behavior detection, and identity recognition. The progress in mobile technology, ML, and DL has equipped researchers across numerous disciplines, including ecology, with innovative data collection methods for building knowledge bases on species and ecosystems. This study addresses the need for systematic field data collection for monitoring endangered species like the pool frog by employing deep learning and image processing techniques. In this research, a multidisciplinary team developed a technique, termed ReFrogID, to identify individual frogs using their unique abdominal patterns. Utilizing RGB images, the system operates on two main principles: (1) a DL algorithm for automatic segmentation achieving AP@89.147, AP50@99.123, and AP75@98.942, and (2) pattern matching via local feature detection and matching methods. A new dataset, pelophylax_lessonae, addresses the identity recognition problem in pool frogs. The effectiveness of ReFrogIDis validated by its ability to identify frogs even when human experts fail. Source code is available at here.
Meng, Li; Goodwin, Morten; Yazidi, Anis & Engelstad, Paal E. (2023). Unsupervised State Representation Learning in Partially Observable Atari Games. I Tsapatsoulis, Nicolas (Red.), Computer Analysis of Images and Patterns. CAIP 2023. Springer. ISSN 978-3-031-44239-1. s. 212–222. doi: 10.1007/978-3-031-44240-7_21. Fulltekst i vitenarkiv Vis sammendrag
State representation learning aims to capture latent factors of an environment. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused on using masks as an augmentation technique to represent the latent generative factors better. Partially observable environments in reinforcement learning have not yet been carefully studied using unsupervised state representation learning methods. In this article, we create an unsupervised state representation learning scheme for partially observable states. We conducted our experiment on a previous Atari 2600 framework designed to evaluate representation learning models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has shown state-of-the-art performance on this benchmark but remains inferior to its supervised counterpart. Our approach improves ST-DIM when the environment is not fully observable and achieves higher F1 scores and accuracy scores than the supervised learning counterpart. The mean accuracy score averaged over categories of our approach is 66%, compared to 38% of supervised learning. The mean F1 score is 64% to 33%. The code can be found on https://github.com/mengli11235/MST_DIM.
Borgersen, Karl Audun Kagnes; Goodwin, Morten & Sharma, Jivitesh (2023). A comparison between Tsetlin machines and deep neural networks in the context of recommendation systems. Proceedings of the Northern Lights Deep Learning Workshop. doi: 10.7557/18.6807. Vis sammendrag
Recommendation Systems (RSs) are ubiquitous inmodern society and are one of the largest points ofinteraction between humans and AI. Modern RSsare often implemented using deep learning models,which are infamously difficult to interpret. Thisproblem is particularly exasperated in the contextof recommendation scenarios, as it erodes the user’strust in the RS. In contrast, the newly introducedTsetlin Machines (TM) possess some valuable prop-erties due to their inherent interpretability. TMsare still fairly young as a technology. As no RShas been developed for TMs before, it has becomenecessary to perform some preliminary research re-garding the practicality of such a system. In thispaper, we develop the first RS based on TMs toevaluate its practicality in this application domain.This paper compares the viability of TMs withother machine learning models prevalent in the fieldof RS. We train and investigate the performance ofthe TM compared with a vanilla feed-forward deeplearning model. These comparisons are based onmodel performance, interpretability/explainability,and scalability. Further, we provide some bench-mark performance comparisons to similar machinelearning solutions relevant to RSs.
Olsen, Ørjan Langøy; Sørdalen, Tonje Knutsen; Goodwin, Morten; Malde, Ketil; Knausgård, Kristian Muri & Halvorsen, Kim Aleksander Tallaksen (2023). A contrastive learning approach for individual re-identification in a wild fish population. Proceedings of the Northern Lights Deep Learning Workshop. 4. doi: 10.7557/18.6824. Fulltekst i vitenarkiv Vis sammendrag
In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.
Jyhne, Sander; Jacobsen, Jørgen Åsbu; Goodwin, Morten & Andersen, Per-Arne (2023). DeNISE: Deep Networks for Improved Segmentation Edges. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. 675, s. 81–89. doi: 10.1007/978-3-031-34111-3_8. Vis sammendrag
This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks. DeNISE utilizes the inherent differences in two sequential deep neural architectures to improve the accuracy of the predicted segmentation edge. DeNISE applies to all types of neural networks and is not trained end-to-end, allowing rapid experiments to discover which models complement each other. We test and apply DeNISE for building segmentation in aerial images. Aerial images are known for difficult conditions as they have a low resolution with optical noise, such as reflections, shadows, and visual obstructions. Overall the paper demonstrates the potential for DeNISE. Using the technique, we improve the baseline results with a building IoU of 78.9%.
Opalic, Sven Myrdahl; Palumbo, Fabrizio; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2023). COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control. Journal of Energy Storage. ISSN 2352-152X. 72(B). doi: 10.1016/j.est.2023.108202. Vis sammendrag
The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system. The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

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Følstad, Asbjørn; Araujo, Theo; Papadopoulos, Symeon; Law, Effie L.-C.; Luger, Ewa & Goodwin, Morten [Vis alle 7 forfattere av denne artikkelen] (2023). Chatbot Research and Design: 6th International Workshop, CONVERSATIONS 2022, Amsterdam, The Netherlands, November 22–23, 2022, Revised Selected Papers. Springer. ISBN 978-3-031-25581-6. 13815(*). 211 s.

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Goodwin, Morten (2024). KI og hva det vil bety for kommunal sektor.
Goodwin, Morten (2024). Sannheten om kunstig intelligens i statlig forvaltning.
Goodwin, Morten (2024). Sannheten om kunstig intelligens i statlig forvaltning.
Goodwin, Morten (2024). Sannheten om kunstig intelligens i statlig forvaltning.
Bjørkeng, Per Kristian & Goodwin, Morten (2024). Knekk ChatGPT-koden.
Goodwin, Morten (2024). Kan maskiner forstå dyrespråk. [Radio]. NRK Abelstårn.
Goodwin, Morten (2024). Den store serien om KI (1:10) - Kjærlighet i algoritmenes tid. [Radio]. NRK Abelstårn.
Goodwin, Morten (2024). Den store serien om KI (3:10) - Når maskiner lærer å se. [Radio]. NRK Abelstårn.
Goodwin, Morten (2024). Fagprat: Morten Goodwin om kunstig intelligens og akademia.
Goodwin, Morten (2024). Sannheten om kunstig intelligens.
Goodwin, Morten (2024). Kunstig intelligens: En ny æra for byggebransjen .
Goodwin, Morten (2024). En ny æra for politiet.
Goodwin, Morten & Lian, Anne (2024). Hvordan din HR-avdeling kan dra nytte av AI. [Internett]. HRNorge.
Goodwin, Morten (2024). Kampen mot desinformasjon. [Avis]. Fædrelandsvennen.
Opalic, Sven Myrdahl & Goodwin, Morten (2024). Kunstig intelligens sørger for at det trengs mye mindre energi for å holde maten din kald. [Internett]. Nationen.
Opalic, Sven Myrdahl; Goodwin, Morten & Wehus, Walter Norman (2024). Slik holder maten din seg kald med halve energien. [Internett]. Forskning.no.
Goodwin, Morten & Arnesen, Mats (2024). Student ble tatt for KI-språkvask — advokat advarer andre. [Internett]. Khrono.
Goodwin, Morten & Hunding Strømme, Simen (2024). Skattemeldingen er en gullgruve for kriminelle. [TV]. TV2.
Goodwin, Morten & Johansen, John Martin (2024). Nå kommer skattemeldingen – da er det høysesong for nettsvindel. [Internett]. Dokument.no.
Goodwin, Morten (2024). Følelser og bevissthet. [Internett]. Næringsforeningen i Kristiansand Regionen (NIKR).
Goodwin, Morten (2024). Gemini vs ChatGPT: KI-konkurransen tilspisser seg. [Internett]. Podcast: Kunstig intelligens med Morten Goodwin.
Goodwin, Morten (2024). Fremtiden for filmproduksjon. [Internett]. Podcast: Kunstig intelligens med Morten Goodwin.
Goodwin, Morten (2024). Kunstig intelligens: til inspirasjon.
Mørch, Anders; Goodwin, Morten & Molnes, Geir (2024). De flinke studentene blir bedre med KI. [Fagblad]. Utdanningsnytt.
Goodwin, Morten (2024). Hva trenger sykepleierstudenter å vite om kunstig intelligens?
Goodwin, Morten & Gravem Johansen, Ina (2024). Ikke tro det når du ser det. [Tidsskrift]. Folkebladet.
Goodwin, Morten (2024). Kunstig intelligens: En ny ære for reiselivet.
Goodwin, Morten & Pettersen, Jan (2024). Forskere spår om kunstig intelligens: «Jeg tror ikke eksamen vil finnes om fem år». [Internett]. Dokument.no.
Nodeland, Richard & Goodwin, Morten (2024). Vi er i starten på en helt ny verden. [Avis]. Fædrelandsvennen.
Kvalvaag Torgersen, Oskar & Goodwin, Morten (2024). Rykte om at kunstig intelligens erstatter alle jobber er veldig overdrevet. [Internett]. Sørnett.
Hunding Strømme, Simen & Goodwin, Morten (2024). Hørt om «sovende agenter»? Slik kan de lure deg. [Internett]. TV2.
Rosbach, Marius & Goodwin, Morten (2024). Stor etterspurnad etter folk som kan kunstig intelligens. [Internett]. nett.no.
Halvorsen Bjørgan, Espen & Goodwin, Morten (2024). NTNU med restriktive KI-retningslinjer: — Kan ikke kose på serveren. [Internett]. Khrono.
Goodwin, Morten; de Vries, Robin; Moelans, Robrecht; Pauly, Klaas & Hill, Donal (2024). Leveraging AI for Plastic Pollution Detection and Monitoring. Vis sammendrag
Goodwin, Morten (2024). Kort fortalt: Hva trenger sykepleierstudenter å vite om kunstig intelligens? [Internett]. Pedagogisk snakkis. Vis sammendrag
Goodwin, Morten (2024). Hemmeligheten med å få til kunstig intelligens i framtidas skoler. Vis sammendrag
Goodwin, Morten (2024). Kan romlig databehandling eller dypbløff bli årets ord? . [Internett]. Digital Norway. Vis sammendrag
Goodwin, Morten (2024). AI in the realm of fake speech: Capabilities and challenges.
Goodwin, Morten (2024). Fremtidens Offentlig Forvaltning med Kunstig Intelligens.
Goodwin, Morten (2024). Kunstig intelligens + skole = sant.
Goodwin, Morten (2024). Mer muntlige utspørring i fremtiden. [Radio]. NRK sørlandsendingen P1.
Andersen, Per-Arne; Goodwin, Morten & Lyngroth, Maren Thormodsæter (2024). Vi spår kunstig intelligens-året 2024 og fem år frem i tid. [Internett]. Podcast Addict.
Goodwin, Morten & Tønset, Arne Egil (2024). Kunstig intelligens i Krig. [Internett]. Helgemorgen TV.
Goodwin, Morten (2024). Kunstig intelligens med Morten Goodwin.
Goodwin, Morten (2024). Robotenes Revolusjon: Sikkerhet eller Sårbarhet?
Goodwin, Morten (2024). Intercitymøte med tema kunstig intelligens.
Goodwin, Morten (2024). Kunstig intelligens, – velsignelse eller forbannelse?
Goodwin, Morten (2024). Kunstig intelligens – hva nå?
Goodwin, Morten (2024). Sannheten om kunstig intelligens.
Sørnes, Astrid Johanne; Klevjer, Carl August; Goodwin, Morten; Strumke, Inga & Lakhani, Karim L. (2024). Ekspertar trur 2024 blir året for «deepfakes». [Internett]. NRK.
Goodwin, Morten (2024). Hemmeligheten med å skille teknologisk fremskritt fra tullete fiksjon.
Goodwin, Morten & Stensland Olsen, Anders (2024). Seks av ti lærere har tatt elever i juks. [Internett]. Klar tale.
Goodwin, Morten (2024). Kunstig intelligens forandrer reiselivet.
Goodwin, Morten (2024). Kunstig intelligens + skole = Sant.
Goodwin, Morten (2024). Kunstig intelligens + skole = Sant.
Goodwin, Morten (2024). Slik kan kommuner bli kunstig intelligens-katalysator.
Goodwin, Morten (2024). Fremtiden med kunstig intelligens.
Goodwin, Morten & Vaaland, Mattis (2024). AI-ekspertens beste tips for 2024: – Lær deg nevrale nettverk! [Internett]. Kode24.
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. [Internett]. Forskning.no.
Einevoll, Gaute Tomas; Strumke, Inga; Goodwin, Morten; Pettersen, Klas Henning & Haggstrom, Olle (2023). Episode #83: Om farer og muligheter med kunstig intelligens. [Internett]. Podcast "Vett og vitenskap".
Lanestedt, Gjermund; Goodwin, Morten & Andersen, Per-Arne (2023). Tid for en (mer) intelligent statsforvaltning? Stat og styring. ISSN 0803-0103. 33(3), s. 7–14. doi: 10.18261/stat.33.3.2.
Goodwin, Morten; Touileb, Samia & Bøhn, Einar Duenger (2023). Blir vi overflødige? En samtale om kunstig intelligens og utdanning.
Goodwin, Morten (2023). Sannheten om kunstig intelligens .
Goodwin, Morten (2023). AI – kunstig intelligens gir muligheter for i trebasert næring.
Goodwin, Morten (2023). Kunstig intelligens for utvikling of design.
Goodwin, Morten (2023). Vi er på vei mot å bli et AI-U-land.
Goodwin, Morten (2023). Hemmeligheten bak virkelig god kunstig intelligens.
Borgersen, Karl Audun Kagnes; Goodwin, Morten & Sharma, Jivitesh (2023). Automating Tagging for Fashion Items: A User Study.
Goodwin, Morten (2023). Sannheten om kunstig intelligens i skolen.
Goodwin, Morten (2023). Kunstig intelligens i offentlig sektor - hvordan kan og bør vi bruke det.
Goodwin, Morten (2023). Velferdsteknologi i demensomsorg – muligheter og begrensninger.
Goodwin, Morten (2023). Sannheten om kunstig intelligens.
Goodwin, Morten (2023). Hemmeligheten med å få til praktisk og nyttig kunstig intelligens.
Goodwin, Morten (2023). Hemmeligheten med å få til god kunstig intelligens i engelsktimen.
Goodwin, Morten (2023). Kunstig intelligens + undervisning = sant?
Goodwin, Morten (2023). Når algoritmene kuraterer barne-tv.
Goodwin, Morten (2023). Vi opplever en IT-sikkerhetskrise, kan kunstig intelligens redde oss?
Goodwin, Morten (2023). Vi opplever en IT-sikkerhetskrise, kan kunstig intelligens redde oss?
Goodwin, Morten (2023). Vi opplever en IT-sikkerhetskrise, kan kunstig intelligens redde oss?
Goodwin, Morten (2023). Sannheten om kunstig intelligens i videregående skole.
Goodwin, Morten (2023). Språkfrokost: Kunstig intelligens, ChatGPT og språk.
Goodwin, Morten (2023). Hvordan bør Norge satse på kunstig intelligens.
Goodwin, Morten (2023). ChatGPT + høyere utdanning = sant.
Goodwin, Morten (2023). ChatGPT. SPENN -Næringsforeningen i Kristiansandsregionen.
Kolhe, Mohan Lal; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Opalic, Sven Myrdahl (2023). Advanced Warehouse Energy Storage System Control Using Deep Supervised- and Reinforcement Learning. Universitetet i Agder. ISSN 978-82-8427-152-1.

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Publisert 16. apr. 2024 11:05