Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules
2. Related Work
3. Basic Information Society Laws
- Moore’s law : The growth of the capabilities of electronic devices, e.g., chips, is exponential. As originally stated, the number of transistors in a dense integrated circuit (IT) doubles approximately every two years. This law has been valid for over half a century and is analysed in more detail later.
- Joy’s law : The peak computer speed doubles each year. This law was first formulated in 1983. The formula is ComputerSpeed = 2 ** (year: 1984). The rule is related to Moore’s law and bears the same time-resistant properties.
- Pollack’s law : Due to microarchitectural advances, microprocessor performance increases roughly proportional to the square root of the increase in complexity, whereas power consumption increases roughly linearly proportional to the increase in complexity. Pollack’s law implies that microarchitectural advances improve the performance by √2 ≈ 41%, thus bearing some similarity to Moore’s law and allowing progress without exceeding the energy demands.
- Bell’s law : Roughly every decade, a new, lower priced computer class (or generation) forms based on a new programming platform, network and interface, resulting in new usage and the establishment of a new industry. It is related to the Moore’s law, which refers to years; however, Bell’s law refers to computer classes, i.e., generations. It takes approximately 10 years to exploit the possibilities of a particular computer class, and during that time a new computer class is researched and finally introduced.
- Kryder’s law : Disk capacity (more specifically, magnetic disk areal storage density) grows exponentially, even faster than Moore’s law. However, similar to Moore’s law but much sooner, the limit of fast growth was achieved in approximately 2019, and the magnetic disk capacity was then more or less stable .
- Makimoto’s law : There is a 10-year cycle between research and standardization, meaning that we can see future commercial capabilities by examining today’s research facilities. There is also Makimoto’s wave , which explains not only the semiconductor waves but also the AI and machine learning (ML) waves. Indeed, AI has progressed in waves, but not exactly in 10-year waves. Unlike Moore’s law, Makimoto’s law describes the general property between research and the market in electronics and is not as prone to time as some other rules.
- Keck’s law : Communication capabilities (actual traffic) grow exponentially. Keck’s law has successfully predicted the trends for the data rates in optical fibres for four decades. Keck’s law is another example of an exponential law predicting incredibly fast growth that was valid for a certain time period but is currently slowing and may be facing a plateau in the foreseeable future.
- Gilder’s law or the law of telecoms : The total telecommunications system capacity (b/s) triples every three years, and the bandwidth grows at least three times faster than computing power. Gilder’s law is similar to Keck’s law.
- Koomey’s law : The number of computations per joule of energy dissipated has been doubling approximately every 1.57 years. Similar to other exponential laws, Koomey’s law is losing its consistency. In 2000, the doubling slowed to every 2.6 years. Koomey’s law is also related to the end of Dennard scaling in 2005, i.e., the ability to build smaller transistors with constant power density.
- Dennard’s law or Dennard scaling : As the size of transistors decrease, their power density stays constant. It is strongly related to one period of Moore’s law but is more or less saturated.
- Rock’s law or Moore’s second law : The cost of a semiconductor chip fabrication plant doubles every four years. This law is related to technological progress, although without the past issue with time validity as Moore’s (first) law.
- Neven’s law : Quantum computers are gaining computational power at a doubly exponential rate. Quantum supremacy was declared by Google in October 2019. In October 2020, quantum supremacy was reclaimed by Chinese researchers , but both publications raised several questions. The law claims that quantum computers are progressing fast, thus enabling further growth of computational computing power. The timescale of this rule has yet to be observed for a sufficient number of years.
- Amdahl’s law : Amdahl’s law predicts the theoretical speedup limit when using multiple processors, meaning there is always a fraction of a problem that cannot be parallelized. It can be defined with the following formula defining speedup S using the percentage p of the tasks that can be parallelized and the availability of threads s that enable parallel execution: S = 1/(1 − p) + p/s). At the limit, when there is an unlimited supply of parallel execution mechanisms, this equation turns into 1/(1 − p). Amdahl’s law is not sensitive to time-related issues.
- Gustafson’s or Gustafson–Barsis’s law : This law addresses the shortcomings of Amdahl’s law by considering flexible tasks and is more accurate for faster devices.
- Grosch’s or Cray’s law : Computing performance or added economy corresponds to the square root of the increase in speed; that is, to perform a calculation 10 times as cheaply, you must perform it 100 times as fast. The law is not directly related to advances in microelectronics and might be time-independent, but more future data are needed to confirm it.
4. Related Information Society Laws
4.1. Software Laws
- Linus’s law : Given large enough beta tester and codeveloper bases, almost every problem will be characterized quickly and the fix will be obvious to someone. In other words: “given enough eyeballs, all bugs are shallow”. The law contradicts fears that software is becoming uncontrollable with the growing amount of code and is not related to technological issues but to human ingenuity. As such, the law seems to be quite time-independent.
- Wirth’s, Page’s, Gates’ or May’s law : Software is becoming slower more rapidly than hardware is becoming faster. This law may not be fully confirmed in recent years due to various tools and new techniques, and in particular the time relations in this rule seem to be under consideration.
- Brooks’s law: “Adding manpower to a late software project makes it later” is an observation about software project management, but is valid in several other areas where the process cannot be parallelized. It was coined by Fred Brooks in his 1975 book The Mythical Man-Month . Somewhat ironically, an incremental person, when added to a project, makes it take more, not less time.
4.2. Socioeconomic Laws
- Tapscott’s or Negroponte’s law: The economy of an information society is a frictionless economy, information economy, Internet economy, net economy, and new economy; it is global, liberal, without restrictions and regulations, spurred by electronics and information technologies and based on bits instead of atoms. Tapscott  introduced e-commerce and e-business characteristics, while Negroponte  introduced the e-world consisting of bits instead of atoms, transforming the economy into a new stage. The economy in an IS is surely different compared to the period before, but it will last only a certain period of time until another step in human civilization occurs .
- Gross’s law: The information overload law; or the infobesity, infoxication, information anxiety and information explosion law : The side effect of an IS is information overload. This law relates to the excessive information given to people in everyday life and when making decisions due to ICTs generating massive amounts of information that grow exponentially. This law seems to be increasingly more valid with the progress of increasingly more data and information and with the lack of appropriate mechanisms that would enable people to handle the information overload issue.
- Gams’ law : IS, the cyberworld double fortune. The fortune can be real or fictitious, such as cryptocurrency. First presented in 2002, when there was not as much cryptocurrency in the world such as Bitcoin, the observed law taught among the local economics faculty proposes a transition at a remote island where native people trade natural goods such as pigs and coconuts. At one point, a modern king introduces paper money; in their fictitious currency, 1 Illa is worth 1 pig. Counting the natural resources and the paper money, the island has twice as much wealth as before. If neighbouring islands accept their currency, the king can print considerably more paper money and buy a substantial amount of goods abroad. In time, the king’s successor introduces BIlla, a Bitcoin version of their paper currency Illa. The story repeats and the current king, or better, their business elite, can considerably increase their worth. This example should help understand the events in the net economy: why virtual money increases wealth, why elites become increasingly richer and why the fictitious or “normative” standard may not directly correspond to the real status of netizens. For example, the netizens on the fictitious island have the same amount of pigs and coconuts at the end of the story as in the beginning, and if the elites increase their wealth, the average islander has less than in the beginning. Note, however, that the progress enables better production of pigs and other goods, and overall, the middle class more or less stays at the same level while the overall wealth increases. However, nominal wealth is significantly different than actual wealth in terms of pigs and coconuts. As with many economic laws, this one is also not directly bound to the technological process and therefore is not as time-dependent as, e.g., Moore’s law.
- Clift’s law or e-democracy, digital democracy or Internet democracy progress : The web enables democratic progress. The introduction of ICTs and IS tools to political and governance processes is thought to promote democracy since citizens are presumed to be eligible to participate equally in information creation and sharing. In other words, “The Internet is the most democratic and free media in the world.” The World Wide Web supposedly offers participants “a potential voice, a platform, and access to the means of production” . However, in recent decades, the concentration of capital has resulted in an increased concentration of media ownership by large private entities in several American and European countries . According to current polls , over 90% of Americans from a sample of approximately 20,000 considered the media to have major importance for democracy; however, approximately 50% of them see the media as biased to various degrees, impairing and endangering democratic processes. While for decades the optimistic viewpoint prevailed in e-democracy, in recent years, we might be witnessing a change. The future of this law seems quite unclear.
4.3. Computers and Progress Laws
- Wright’s law : The cost of airplanes is proportional to the inverse of the number of planes manufactured raised to some power. The law seems to be time-independent.
5. Longevity of Moore’s Law
6. AI, AmI and Electronics
Conflicts of Interest
- Weiss, G. Multiagent Systems (Intelligent Robotics and Autonomous Agents Series); MIT: Cambridge, MA, USA, 2013; ISBN 978-0262018890. [Google Scholar]
- Russel, S.; Norvig, P. Artificial Intelligence: A Modern Approach, 3rd ed.; Pearson Education Limited: London, UK, 2014. [Google Scholar]
- Turing, A. Computing Machinery and Intelligence. Mind 1950, 59, 433–460. [Google Scholar] [CrossRef]
- Arribas-Ayllon, M. Ambient Intelligence: An Innovation Narrative; Lancaster University: Lancaster, UK, 2003; Available online: http://www.academia.edu/1080720/Ambient_Intelligence_an_innovation_narrative (accessed on 4 February 2021).
- Augusto, J.C.; McCullagh, P. Ambient intelligence: Concepts and applications. Comput. Sci. Inf. Syst. 2007, 4, 1–27. [Google Scholar] [CrossRef]
- Weiser, M. The computer for the twenty-first century. Sci. Am. 1991, 165, 94–104. [Google Scholar] [CrossRef]
- Streitz, N.; Nixon, P. Special issue on ‘the disappearing computer’. Commun. ACM 2005, 48, 32–35. [Google Scholar] [CrossRef]
- Daoutis, M.; Coradeshi, S.; Loutfi, A. Grounding commonsense knowledge in intelligent systems. J. Ambient. Intell. Smart Environ. 2009, 1, 311–321. [Google Scholar] [CrossRef]
- Ramos, C.; Augusto, J.C.; Shapiro, D. Ambient intelligence—The next step for artificial intelligence. IEEE Intell. Syst. 2008, 23, 15–18. [Google Scholar] [CrossRef]
- Nakashima, H.; Aghajan, H.; Augusto, J.C. Handbook of Ambient Intelligence and Smart Environments; Springer: New York, NY, USA, 2009. [Google Scholar]
- Yampolskiy, R.V. Artificial Superintelligence: A Futuristic Approach, 1st ed.; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
- Soll, J. The Information Master: Jean-Baptiste Colbert’s Secret State Intelligence System; University of Michigan Press: Michigan, WI, USA, 2014. [Google Scholar]
- Artificial Intelligence in Medicine: Applications, Implications, and Limitations. Available online: http://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicine-applications-implications-and-limitations/ (accessed on 4 February 2021).
- Goyal, H.; Mann, R.; Gandhi, Z.; Perisetti, A.; Ali, A.; Aman Ali, K.; Sharma, N.; Saligram, S.; Tharian, B.; Inamdar, S. Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer. J. Clin. Med. 2020, 9, 3313. [Google Scholar] [CrossRef] [PubMed]
- Oren, O.; Gersh, B.J.; Bhatt, D.L. Artificial intelligence in medical imaging: Switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit. Health 2020, 2, e486–e488. [Google Scholar] [CrossRef]
- Beniger, J.R. The Control Revolution: Technological and Economic Origins of the Information Society; Harvard University Press: Cambridge, MA, USA, 1986. [Google Scholar]
- Byung-Keun, K. Internationalising the Internet the Co-Evolution of Influence and Technology; Edward Elgar Publishing: Cheltenham, UK, 2005. [Google Scholar]
- Webster, F.V. Theories of the Information Society, 3rd ed.; Routledge: New York, NY, USA, 2006. [Google Scholar]
- WSIS: Tunis Agenda for the Information Society. Available online: http://www.itu.int/net/wsis/docs2/tunis/off/6rev1.html (accessed on 10 December 2020).
- Scholz, R. Sustainable Digital Environments: What Major Challenges Is Humankind Facing? Sustainability 2016, 8, 726. [Google Scholar] [CrossRef][Green Version]
- Global Internet Usage. Available online: https://en.wikipedia.org/wiki/Global_Internet_usage (accessed on 10 December 2020).
- Finlay, S. Artificial Intelligence for Everyone; Relativistic: UK, 2020; ISBN 978-1-9993253-1-2. [Google Scholar]
- Blokdyk, G. Ambient Intelligence: A Complete Guide; 5STARCooks: Huston, TX, USA, 2020. [Google Scholar]
- Boyle, J. Shamans, Software, and Spleens: Law and the Construction of the Information Society; Harvard University Press: Cambridge, MA, USA, 1997. [Google Scholar]
- Xiuquan, L.; Tao, Z. An exploration on artificial intelligence application: From security, privacy and ethic perspective. In Proceedings of the 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, 28–30 April 2017; pp. 416–420. [Google Scholar] [CrossRef]
- Lee, H.; Wong, S.F.; Oh, J.; Chang, Y. Information privacy concerns and demographic characteristics: Data from a Korean media panel survey. Gov. Inf. Q. 2019, 36, 294–303. [Google Scholar] [CrossRef]
- Mansell, R.; Steinmueller, W. Mobilizing the Information Society: Strategies for Growth and Opportunity; Oxford University Press: Oxford, UK, 2020. [Google Scholar]
- Cath, C.; Wachter, S.; Mittelstadt, B.; Taddeo, M.; Floridi, L. Artificial Intelligence and the ‘Good Society’: The US, EU, and UK approach. Sci. Eng. Ethics 2018, 24, 505–528. [Google Scholar] [CrossRef] [PubMed]
- Computer laws revisited (Special issue). Computer 2013, 46, 12.
- Gams, M. Intelligence in Information Society. In Enabling Society with Information Technology; Jin, Q., Li, J., Zhang, N., Cheng, J., Yu, C., Noguchi, S., Eds.; Springer: Tokyo, Japan, 2002. [Google Scholar] [CrossRef]
- Gams, M.; Gu, I.Y.; Härmä, A.; Muñoz, A.; Tam, V. Artificial intelligence and ambient intelligence. J. Ambient. Intell. Smart Environ. 2019, 11, 71–86. [Google Scholar] [CrossRef][Green Version]
- Denning, P.J.; Lewis, T.G. Exponential Laws of Computing Growth. Commun. ACM 2017, 60, 54–65. [Google Scholar] [CrossRef]
- Moore, G.E. Cramming more components onto integrated circuits. Electronics 1965, 38, 114–117. [Google Scholar] [CrossRef]
- Markoff, J. The not-so-distant future of personal computing. InfoWorld 1993, 15, 48–50. [Google Scholar]
- Borkar, S.; Chien, A.A. The Future of Microprocessors. Commun. ACM 2011, 54, 67. [Google Scholar] [CrossRef]
- Bell, G. Bell’s Law for the Birth and Death of Computer Classes. Commun. ACM 2008, 51, 86–94. [Google Scholar] [CrossRef]
- Chip, W. Kryder’s Law. Sci. Am. 2005, 293, 32–33. [Google Scholar] [CrossRef]
- Antoniazzi, L. Digital preservation and the sustainability of film heritage. Inf. Commun. Soc. 2020, 1–16. [Google Scholar] [CrossRef]
- Salvadeo, P.A.; Veca, Á.C.; López, R.C. Historic behavior of the electronic technology: The Wave of Makimoto and Moore’s Law in the Transistor’s Age. In Proceedings of the 2012 VIII Southern Conference on Programmable Logic, Bento Goncalves, Brazil, 20–23 March 2012; pp. 1–5. [Google Scholar] [CrossRef]
- Hruska, J. How Makimoto’s Wave Explains the Tsunami of New AI Processors. Available online: https://www.extremetech.com/computing/287137-how-makimotos-wave-explains-the-tsunami-of-specialized-ai-processors-headed-for-market (accessed on 10 December 2020).
- Hecht, J. Is Keck’s Law Coming to an End? After Decades of Exponential Growth, Fiber-Optic Capacity May Be Facing a Plateau. Available online: https://spectrum.ieee.org/semiconductors/optoelectronics/is-kecks-law-coming-to-an-end (accessed on 10 December 2020).
- Wilson, J.M. Computing, Communication, and Cognition. Three Laws That Define the Internet Society: Moore’s, Gilder’s, and Metcalfe’s. Available online: http://www.jackmwilson.net/Entrepreneurship/Cases/Moores-Meltcalfes-Gilders-Law.pdf (accessed on 10 December 2020).
- Koomey, J.; Berard, S.; Sanchez, M.; Wong, H. Implications of Historical Trends in the Electrical Efficiency of Computing. IEEE Ann. Hist. Comput. 2010, 33, 46–54. [Google Scholar] [CrossRef]
- Dennard, R.H.; Gaensslen, F.H.; Yu, H.; Rideout, V.L.; Bassous, E.; LeBlanc, A.R. Design of ion-implanted MOSFET’s with very small physical dimensions. IEEE J. Solid-State Circuits 1974, 9, 256–268. [Google Scholar] [CrossRef][Green Version]
- Schaller, B. The Origin, Nature, and Implications of ‘Moore’s Law. Available online: http://jimgray.azurewebsites.net/moore_law.html (accessed on 10 December 2020).
- Hoshida, Y. Moore’s Law Is Replaced by Neven’s Law for Quantum Computing. Available online: https://community.hitachivantara.com/s/article/moores-law-is-replaced-by-nevens-law-for-quantum-computing (accessed on 10 December 2020).
- Letzter, R. China Claims It’s Achieved ‘Quantum Supremacy’ with the World’s Fastest Quantum Computer. Available online: https://www.sciencealert.com/china-has-developed-the-fastest-and-most-powerful-quantum-computer-yet (accessed on 10 December 2020).
- Rodgers, D.P. Improvements in multiprocessor system design. ACM SIGARCH Comput. Archit. News 1985, 13, 225–231. [Google Scholar] [CrossRef]
- Gustafson, J.L. Reevaluating Amdahl’s Law. Commun. ACM 1988, 31, 532–533. [Google Scholar] [CrossRef][Green Version]
- Grosch, H.R.J. High Speed Arithmetic: The Digital Computer as a Research Tool. J. Opt. Soc. Am. 1953, 43, 306–310. [Google Scholar] [CrossRef]
- Raymond, E.S. The Cathedral and the Bazaar; O’Reilly Media: Sebastopol, CA, USA, 1999. [Google Scholar]
- Wirth, N. A Plea for Lean Software. Computer 1995, 28, 64–68. [Google Scholar] [CrossRef]
- Brooks, F.P., Jr. The Mythical Man-Month; Addison-Wesley: Boston, MA, USA, 1995. [Google Scholar]
- Shapiro, C.; Varian, H.R. Information Rules; Harvard Business Press: Brighton, MA, USA, 1999. [Google Scholar]
- Odlyzko, A.; Tilly, B. A Refutation of Metcalfe’s Law and a Better Estimate for the Value of Networks and Network Interconnections; University of Minnesota: Minneapolis, MN, USA, 2005. [Google Scholar]
- Tapscott, D. The Digital Economy: Promise and Peril in the Age of Networked Intelligence; McGraw-Hill: New York, NY, USA, 1997. [Google Scholar]
- Bits and Atoms. Available online: https://www.wired.com/1995/01/negroponte-30/ (accessed on 10 December 2020).
- Norberg, J. Open: The Story of Human Progress; Atlantic Books: London, UK, 2020. [Google Scholar]
- Gross, B.M. The Managing of Organizations: The Administrative Struggle; Free Press of Glencoe: New York, NY, USA, 1964. [Google Scholar]
- Challenge and Promise of E-Democracy. Available online: https://www.griffithreview.com/articles/challenge-and-promise-of-e-democracy/ (accessed on 10 December 2020).
- Kidd, J. Are new media democratic? Cult. Policy Crit. Manag. Res. 2011, 5, 99–109. [Google Scholar]
- Ownership Chart: The Big Six. Available online: http://files.meetup.com/17628282/Media-Big-Six.pdf (accessed on 10 December 2020).
- American Views 2020: Trust, Media and Democracy. Available online: https://knightfoundation.org/reports/american-views-2020-trust-media-and-democracy/ (accessed on 10 December 2020).
- Nagy, B.; Farmer, J.D.; Bui, Q.M.; Trancik, J.E. Statistical Basis for Predicting Tech-nological Progress. PLoS ONE 2013, 8, e52669. [Google Scholar] [CrossRef][Green Version]
- Wright, T.P. Factors affecting the costs of airplanes. J. Aeronaut. Sci. 1936, 3, 122–128. [Google Scholar] [CrossRef]
- Ball, P. Moore’s law is not just for computers. Nature 2013. [Google Scholar] [CrossRef]
- Bayus, B.L. An Analysis of Product Lifetimes in a Technologically Dynamic Industry. Manag. Sci. 1998, 44, 763–775. [Google Scholar] [CrossRef][Green Version]
- Unimaginable Output: Global Production of Transistors. Available online: https://www.darrinqualman.com/global-production-transistors/ (accessed on 10 December 2020).
- Astronomers Have Found the Edge of the Milky Way at Last. Available online: https://www.sciencenews.org/article/astronomers-have-found-edge-milky-way-size (accessed on 10 December 2020).
- How Many Solar Systems Are in Our Galaxy? Available online: https://spaceplace.nasa.gov/other-solar-systems/en (accessed on 10 December 2020).
- Herculano-Houzel, S. The human brain in numbers: A linearly scaled-up primate brain. Front. Hum. Neurosci. 2009, 3, 31. [Google Scholar] [CrossRef] [PubMed][Green Version]
- 55th Anniversary of Moore’s Law. Available online: https://www.infoq.com/news/2020/04/Moores-law-55/ (accessed on 10 December 2020).
- Riding the S-Curve: The Global Uptake of Wind and Solar Power. Available online: https://www.uib.no/en/cet/127447/riding-s-curve-global-uptake-wind-and-solar-power (accessed on 10 December 2020).
- Rotman, D. We’re not prepared for the End of Moore’s Law. Available online: https://www.technologyreview.com/2020/02/24/905789/were-not-prepared-for-the-end-of-moores-law (accessed on 10 December 2020).
- Winston, P. Artificial Intelligence; Pearson: London, UK, 1992. [Google Scholar]
- History of AI Winters. Available online: https://www.actuaries.digital/2018/09/05/history-of-ai-winters/ (accessed on 10 December 2020).
- Moloi, T.; Marwala, T. Artificial Intelligence in Economics and Finance Theories; Springer: Berlin/Heidelberg, Germany, 2020. [Google Scholar]
- IJCAI Conference. 2017. Available online: https://ijcai-17.org (accessed on 4 February 2021).
- Jug, J.; Kolenik, T.; Ofner, A.; Farkas, I. Computational model of enactive visuospa-tial mental imagery using saccadic perceptual actions. Cogn. Syst. Res. 2018, 49, 157–177. [Google Scholar] [CrossRef]
- IEEE Computational Intelligence Society. Available online: https://cis.ieee.org/ (accessed on 10 December 2020).
- Gjoreski, M.; Janko, V.; Slapničar, G.; Mlakar, M.; Reščič, N.; Bizjak, J.; Drobnič, V.; Marinko, M.; Mlakar, N.; Luštrek, M.; et al. Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors. Inf. Fusion 2020, 62, 47–62. [Google Scholar] [CrossRef]
- Yun, Y.; Gu, I.Y.H. Riemannian Manifold-Valued Part-Based Features and Geodesic-Induced Kernel Machine for Human Activity Classification Dedicated to Assisted Living. Comput. Vis. Image Underst. 2017, 161, 65–76. [Google Scholar] [CrossRef]
- The 4 Deep Learning Breakthroughs You Should Know about. Available online: https://towardsdatascience.com/the-5-deep-learning-breakthroughs-you-should-know-about-df27674ccdf2 (accessed on 10 December 2020).
- Zhang, X.; Tian, Q.; Wang, L.; Liu, Y.; Li, B.; Liang, Z.; Gao, P.; Zheng, K.; Zhao, B.; Lu, H. Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI. J. Magn. Reason. Imaging 2018, 48, 916–926. [Google Scholar] [CrossRef]
- Ge, C.; Gu, I.Y.H.; Jakola, A.S.; Yang, J. Deep Learning and Multi-Sensor Fusion for Glioma Classification using Multistream 2D Convolutional Networks. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Bi-ology Society (EMBC’18), Honolulu, HI, USA, 18–21 July 2018; pp. 5894–5897. [Google Scholar]
- Chang, K.; Bai, H.X.; Zhou, H.; Su, C.; Bi, W.L.; Agbodza, E.; Kavouridis, V.K.; Senders, J.T.; Boaro, A.; Beers, A.; et al. Residual Convolutional Neural Network for the Determination ofIDHStatus in Low- and High-Grade Gliomas from MR Imaging. Clin. Cancer Res. 2018, 24, 1073–1081. [Google Scholar] [CrossRef][Green Version]
- Eye Scans to Detect Cancer and Alzheimer’s Disease. Available online: https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/eye-scans-to-detect-cancer-and-alzheimers-disease (accessed on 10 December 2020).
- Sarraf, S.; Tofighi, G. DeepAD: Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv 2016. [Google Scholar] [CrossRef][Green Version]
- Bäckström, K.; Nazari, M.; Gu, I.Y.H.; Jakola, A.S. An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging, Washington, DC, USA, 4–7 April 2018. [Google Scholar]
- Pejović, V.; Gjoreski, M.; Anderson, C.; David, K.; Luštrek, M. Toward Cognitive Load Inference for Attention Management in Ubiquitous Systems. IEEE Pervasive Comput. 2020, 19, 35–45. [Google Scholar] [CrossRef]
- Gjoreski, M.; Kolenik, T.; Knez, T.; Luštrek, M.; Gams, M.; Gjoreski, H.; Pejović, V. Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits. Appl. Sci. 2020, 10, 3843. [Google Scholar] [CrossRef]
- Kolenik, T.; Gams, M. Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality? IEEE Technol. Soc. Mag. 2020. [Google Scholar] [CrossRef]
- Kurzweil, R. The Singularity Is Near: When Humans Transcend Biology; Penguin Books: London, UK, 2006. [Google Scholar]
- Bostrom, N. Superintelligence—Paths, Dangers, Strategies; Oxford University Press: Oxford, UK, 2014. [Google Scholar]
- Kolenik, T. Seeking after the Glitter of Intelligence in the Base Metal of Computing: The Scope and Limits of Computational Models in Researching Cognitive Phenomena. Interdiscip. Descr. Complex Syst. 2018, 16, 545–557. [Google Scholar] [CrossRef]
- Beard, J.M. Autonomous Weapons and Human Responsibilities. Georget. J. Int. Law 2014, 45, 617. [Google Scholar]
- Will the Latest AI Kill Coding? Available online: https://towardsdatascience.com/will-gpt-3-kill-coding-630e4518c04d (accessed on 10 December 2020).
|Related Work||Relevant Characteristics|
|||IS related to democracy, Cliff’s law, Gross’ Law|
|||IS related to economics and liberalism, Tapscott’s law|
|[25,26]||Privacy in the era of AI and its exploitation|
|[24,27,28]||General growth of AI|
|||First presentation of a smaller number of IS laws in an academic form (not a list or in terms of IS progress)|
|||IS technology that enables current society|
|||AI and AmI analysis|
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Gams, M.; Kolenik, T. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules. Electronics 2021, 10, 514. https://doi.org/10.3390/electronics10040514
Gams M, Kolenik T. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules. Electronics. 2021; 10(4):514. https://doi.org/10.3390/electronics10040514Chicago/Turabian Style
Gams, Matjaž, and Tine Kolenik. 2021. "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules" Electronics 10, no. 4: 514. https://doi.org/10.3390/electronics10040514