Edition 2021-1: AI and New Media

[1]  I. E. Sutherland, “A head-mounted three dimensional display”. Proceedings of AFIPS 68, pp. 757-764, 1968.

[2] K. Fowle, “The Vanguard of Virtual Reality: An Embarrassing Arcade Game,” The Atlantic, February 4, 2019. [Online]. Available: https://www.theatlantic.com/entertainment/archive/2015/02/when-vr-was-an-arcade-game/385139/

[3] http://chris.bregler.com/videorewrite/

[4] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples”. In International Conference on Learning Representations (ICLR), 2015.

[5] https://vole.wtf/ganksy/

[6] S. Varghese, “Pokémon Go was a warning about the rise of surveillance capitalism,” Wired, February 3, 2019. [Online]. Available: https://www.wired.co.uk/article/the-age-of-surveillance-capitalism-facebook-shoshana-zuboff

[7] M. Granik and V. Mesyura, “Fake news detection using naive bayes classifier”. In 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900–903, 2017.

[8] B. Zarouali, T. Dobber, G. de Pauw, and C. de Vreese, “Using a Personality-Profiling Algorithm to Investigate Political Microtargeting: Assessing the Persuasion Effects of Personality-Tailored Ads on Social Media”. Communication Research. 2020.

[1] Alfano, M., Carter, J., & Cheong M. (2018). Technological Seduction and Self-Radicalization. Journal of the American Philosophical Association, 4(3), 298-322. doi:10.1017/apa.2018.27

[2] James Bennett; Stan Lanning (2007). “The Netflix Prize”. Proceedings of KDD Cup and Workshop 2007.

[3] Carr, D. (2013). Giving Viewers What They Want. Retrieved 31 January 2021 from https://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html

[4] Deleuze, Gilles. “Postscript on the Societies of Control.” October, vol. 59, JSTOR, 1992, pp. 3–7

[5] Esack, A. (2017). Content Is President: The Influence of Netflix on Taste, Politics and The Future of Television. Thesis, Georgia State University.

[6] Floegel, D. (2020). Labor, classification and productions of culture on Netflix. Journal of Documentation, 77(1), 209–228. https://doi.org/10.1108/JD-06-2020-0108

[7] Gillespie, T. (2018). Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media.

[8] Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems, 6(4), 1–19. https://doi.org/10.1145/2843948

[9] Hallinan, B., & Striphas, T. (2016). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18(1), 117–137. https://doi.org/10.1177/1461444814538646

[10] Iqbal, M. (2020). Netflix Revenue and Usage Statistics (2020). Retrieved 31 January 2021 from https://www.businessofapps.com/data/netflix-statistics/#2

[11] Kits, B. (2017). ‘Annihilation’: Behind-the-Scenes of a Producer Clash and That Netflix Deal (Exclusive). Retrieved 31 January 2021 from https://www.hollywoodreporter.com/heat-vision/annihilation-how-a-clash-between-producers-led-a-netflix-deal-1065465

[12] Kits, B and McClintock, P. (2018). Sources: Netflix Paid Paramount More Than $50 Million for ‘Cloverfield Paradox’. Retrieved 31 January 2021 from https://www.hollywoodreporter.com/heat-vision/netflix-paid-paramount-more-50-million-cloverfield-paradox-1082305

[13] Lohmann, L. (2019). Labour, Justice and the Mechanization of Interpretation. Development, 62(1–4), 43–52. https://doi.org/10.1057/s41301-019-00207-2

[14] Morris, C. (2018). Netflix Consumes 15% of the World’s Internet Bandwidth. Retrieved 31 January 2021 from https://fortune.com/2018/10/02/netflix-consumes-15-percent-of-global-internet-bandwidth/

[15] Seaver, N. (2019). Captivating algorithms: Recommender systems as traps. Journal of Material Culture, 24(4), 421–436. https://doi.org/10.1177/1359183518820366

[16] Statista. (2021). Estimated impact of the coronavirus on box office revenue worldwide from 2020 to 2025. In Statista. Retrieved January 31 2021 from https://www.statista.com/statistics/1170721/impact-coronavirus-global-box-office-revenue/#:\~:text=Global box office revenue coronavirus impact 2020-2025&text=The impact of the COVID,billion for the year 2020.

[17] Statista. (2020). Video-on-Demand, Worldwide. In Statista. Retrieved January 31 2021 from https://www.statista.com/outlook/201/100/video-on-demand/worldwide

[18] Statista. (2020). Gross number of subscription video on demand (SVoD) subscribers worldwide from 2015 to 2025. Retrieved February 7 from https://www.statista.com/statistics/949391/svod-subscribers-world/

[19] Statista. (2021) Number of Netflix paid subscribers worldwide from 3rd quarter 2011 to 3rd quarter 2020. In Statista. Retrieved January 31 2021 from https://www.statista.com/statistics/250934/quarterly-number-of-netflix-streaming-subscribers-worldwide/#:\~:text=Netflix had 195.15 million paid,Netflix‘s%20total%20global%20subscriber%20base.

[20] Statista. (2021). Estimated impact of the coronavirus on box office revenue worldwide from 2020 to 2025. In Statista. Retrieved January 31 2021 from https://www.statista.com/statistics/1170721/impact-coronavirus-global-box-office-revenue/#:\~:text=Global box office revenue coronavirus impact 2020-2025&text=The impact of the COVID,billion for the year 2020.

http://www.menah.nl/

[1] This is an adapted version of: https://www.textgain.com/portfolio/likes-onder-de-loep/

[2] https://buffer.com/library/Twitter-timeline-algorithm/

[3] https://sproutsocial.com/insights/Twitter-algorithm/

[4] https://www.nature.com/articles/srep37825

[5] https://www.standaard.be/cnt/dmf20200616_04992948

[6] Thomas M. J. Fruchterman, Edward M. Reingold: Graph Drawing by Force-directed Placement. Softw. Pract. Exp. 21(11): 1129-1164, (1991)

[7] M. Jacomy, S. Heymann, T. Venturini, M. Bastian: ForceAtlas2: A Continuous Graph Layout Algorithm for Handy Network Visualization. Technical Report. Sciences Po – Medialab. Paris, (2012)

[8] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Eti-enne Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008, (2008)

https://www.textgain.com/portfolio/qanon-spreading-conspiracy-theories-on-twitter/

https://www.tijd.be/ondernemen/technologie/antwerps-ai-bedrijf-leidt-europees-onderzoek-naar-online-haatspraak/10278452.html

[1] Schrittwieser, J., Antonoglou, I., Hubert, T. et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature 588, 604–609 (2020). https://doi.org/10.1038/s41586-020-03051-4

[2] van  Seijen,  H.,  Fatemi,  M.,  Romoff,  J.,  Laroche,  R.,  Barnes,  T.,  and  Tsang,  J.  (2017). Hybrid reward architecture for reinforcement learning.  In the Annual Conference on Neural Information Processing Systems (NIPS)

[3] Karakovskiy, S. & Togelius, Julian. (2012). The Mario AI Benchmark and Competitions. Computational Intelligence and AI in Games, IEEE Transactions on. 4. 55-67. 10.1109/TCIAIG.2012.2188528.

[4] Johnson M., Hofmann K., Hutton T., Bignell D. (2016)The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint Conference on Artificial Intelligence, Ed. Kambhampati S., p. 4246. AAAI Press, Palo Alto, California USA. https://github.com/Microsoft/malmo

[5] Goodfellow, Ian & Pouget-Abadie, Jean & Mirza, Mehdi & Xu, Bing & Warde-Farley, David & Ozair, Sherjil & Courville, Aaron & Bengio, Y.. (2014). Generative Adversarial Networks. Advances in Neural Information Processing Systems. 3. 10.1145/3422622.

[6] Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, and Sebastian Risi. 2018. Evolving mario levels in the latent space of a deep convolutional generative adversarial network. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18). Association for Computing Machinery, New York, NY, USA, 221–228. DOI:https://doi.org/10.1145/3205455.3205517

[7] Kim, S. W., Zhou, Y., Philion, J., Torralba, A., & Fidler, S. (2020). Learning to simulate dynamic environments with gamegan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1231-1240).

[8] Witkowski, W. (2020). Videogames are a bigger industry than movies and North American sports combined, thanks to the pandemic. Marketwatch. Retrieved from https://www.marketwatch.com/story/videogames-are-a-bigger-industry-than-sports-and-movies-combined-thanks-to-the-pandemic-11608654990

[9] NVIDIA,  “NVIDIA  DLSS  2.0:  A  Big  Leap  in  AI  Rendering,”  23 March       2020. Retrieved from: https://www.nvidia.com/en-gb/geforce/news/nvidia-dlss-2-0-a-big-leap-in-ai-rendering

[10] AMD, “AMD FidelityFX,” 2020. Retrieved from: https://www.amd.com/en/technologies/radeon-software-fidelityfx.

[11] Microsoft,   ” DirectML,”   2020.  Retrieved from: https://github.com/microsoft/DirectML/

https://www.ru.nl/donders/@1247277/ai-researchers-receive-e750-000-grant-research/

https://home.pandorabots.com/home.html

https://philosopherai.com/

https://en.wikipedia.org/wiki/Loebner_Prize

https://player.vimeo.com/video/378289559?portrait=0

[1] Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1-11).

[1] Accelerating dynamics of collective attention

([2]Abundance of information narrows our collective attention span)

[3] Facebook ad revenue 2009-2018

[4] Google: ad revenue 2001-2018

[5] [1904.02095] Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes

[6] [1912.04255] Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging

[7] Foot-in-the-door technique and computer-mediated communication | Request PDF

[8] Can Anxiety Lead to Psychosis? – San Diego | API

[9] Top-down enhancement and suppression of the magnitude and speed of neural activity – ignoring something takes effort, we have to spend energy to ignore impulses

[10] Measuring Consumer Information – information overload

[11] 82 thoughts on “Dopamine, Smartphones & You: A battle for your time” – new is exciting

[12] Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images

[13] Facial recognition technology can expose political orientation from naturalistic facial images

Attention is limited:
[14]Le, Thanh P; Najolia, Gina M; Minor, Kyle S; Cohen, Alex S (2016). “The effect of limited cognitive resources on communication disturbances in serious mental illness”. Psychiatry Research. 248 (248): 98–104. doi:10.1016/j.psychres.2016.12.025. PMC 5378554. PMID 28038440. Retrieved 30 October 2020.

[15] Franconeri, Steven L; Alvarez, George A; Cavanagh, Patrick (2013). “Flexible cognitive resources: competitive content maps for attention and memory”. Trends in Cognitive Sciences. 17 (3): 134–141. doi:10.1016/j.tics.2013.01.010. PMC 5047276. PMID 23428935. Retrieved 30 October 2020.

[16]  Desimone, R; Duncan, J (1995). “Neural mechanisms of selective visual attention”. Annual Review of Neuroscience. 18: 193–222. doi:10.1146/annurev.ne.18.030195.001205. PMID 7605061. Retrieved 30 October 2020.

[17]  Christie, S; Schrater, Paul (2015). “Cognitive cost as dynamic allocation of energetic resources”. Frontiers in Neuroscience. 9(9): 289. doi:10.3389/fnins.2015.00289. PMC 4547044. PMID 26379482. S2CID 15545774. Retrieved 30 October2020.

[18] https://www.psychologytoday.com/us/blog/tech-happy-life/201909/how-does-clickbait-work#:\~:text=Clickbait works%2C in part%2C because,truly give us great pleasure.

[19] Psychotic-Like Experiences in Major Depression and Anxiety Disorders: A Population-Based Survey in Young Adults

[20] The Effect of High-Anxiety Situations on Conspiracy Thinking

[21] QAnon

[22] The Making of a YouTube Radical

[23] What Clickbait Teaches Us About Attracting Attention Online

[24] AIS Electronic Library (AISeL) – AMCIS 2016 Proceedings: Measuring Emotional Arousal in Clickbait: Eye-Tracking Approach

Anti-tracking and Data pollution:

TrackMeNot  TrackMeNot (Google Chrome Extension store)  – search engine data pollution extension

AdNauseam – Clicking Ads So You Don’t Have To – ad profiling pollution extension

http://makeinternetnoise.com/index.html

https://proprivacy.com/ruinmysearchhistory

https://privacybadger.org

https://easylist.to/index.html

Privacy Possum – Get this Extension for ? Firefox (en-US)addons.mozilla.org Privacy Possum is Privacy Badger on Steroids

Install ad, recommendation and tracker blocking extensions:
uBlock Origin – Free, open-source ad content blocker.
Privacy Badger
Privacy Possum – Get this Extension for ? Firefox (en-US)addons.mozilla.org Privacy Possum is Privacy Badger on Steroids

Avoid engaging with any recommended content – usually titled as ‘suggested’ or ‘up next’ or ‘you might be interested in’

Avoid using platforms that use your data against you (e.g. DuckDuckGo instead of Google, VSCO instead of Instagram)

If your system is powerful enough, install extensions that pollute your digital footprint:
– by performing random google searches – TrackMeNot  TrackMeNot (Google Chrome Extension store)
– by clicking random ads so you can’t be targeted properly by ads – AdNauseam – Clicking Ads So You Don’t Have To

Avoid any clickbait titles – titles that are emotionally charged and/or try to make you click them.

Try to apply critical thinking as often as possible, especially pay attention to how things are worded when the article succeeds to elicit an emotional response in you.