Digital Literacy for St. Cloud State University
https://www.facebook.com/BusinessInsider.Cars/videos/388752378203738/
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more on VR in this IMS blog
https://blog.stcloudstate.edu/ims?s=virtual+reality
more on autonomous cars in this IMS blog
https://blog.stcloudstate.edu/ims?s=autonomous+vehicles
https://www.linkedin.com/pulse/understanding-4-types-artificial-intelligence-ai-bernard-marr/
Examples of reactive AI include:
For example, autonomous vehicles use limited memory AI to observe other cars’ speed and direction, helping them “read the road” and adjust as needed. This process for understanding and interpreting incoming data makes them safer on the roads.
The Kismet robot head, developed by Professor Cynthia Breazeal, could recognize emotional signals on human faces and replicate those emotions on its own face. Humanoid robot Sophia, developed by Hanson Robotics in Hong Kong, can recognize faces and respond to interactions with her own facial expressions.
The most advanced type of artificial intelligence is self-aware AI. When machines can be aware of their own emotions, as well as the emotions of others around them, they will have a level of consciousness and intelligence similar to human beings. This type of AI will have desires, needs, and emotions as well.
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
Daniel Burrus
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artifical+intelligence
more on deep learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=deep+learning
Heard on Marketplace this morning (Oct. 22, 2018): ethics of artificial intelligence with John Havens of the Institute of Electrical and Electronics Engineers, which has developed a new ethics certification process for AI: https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ec_bios.pdf
***** The student club, the Philosophical Society, has now been recognized by SCSU as a student organization ***
https://ed.ted.com/lessons/the-ethical-dilemma-of-self-driving-cars-patrick-lin
Could it be the case that a random decision is still better then predetermined one designed to minimize harm?
similar ethical considerations are raised also:
in this sitcom
https://www.theatlantic.com/sponsored/hpe-2018/the-ethics-of-ai/1865/ (full movie)
This TED talk:
https://blog.stcloudstate.edu/ims/2017/09/19/social-media-algorithms/
https://blog.stcloudstate.edu/ims/2018/10/02/social-media-monopoly/
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IoT (Internet of Things), Industry 4.0, Big Data, BlockChain,
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IoT (Internet of Things), Industry 4.0, Big Data, BlockChain, Privacy, Security, Surveilance
https://blog.stcloudstate.edu/ims?s=internet+of+things
peer-reviewed literature;
Keyword search: ethic* + Internet of Things = 31
Baldini, G., Botterman, M., Neisse, R., & Tallacchini, M. (2018). Ethical Design in the Internet of Things. Science & Engineering Ethics, 24(3), 905–925. https://doi-org.libproxy.stcloudstate.edu/10.1007/s11948-016-9754-5
Berman, F., & Cerf, V. G. (2017). Social and Ethical Behavior in the Internet of Things. Communications of the ACM, 60(2), 6–7. https://doi-org.libproxy.stcloudstate.edu/10.1145/3036698
Murdock, G. (2018). Media Materialties: For A Moral Economy of Machines. Journal of Communication, 68(2), 359–368. https://doi-org.libproxy.stcloudstate.edu/10.1093/joc/jqx023
Carrier, J. G. (2018). Moral economy: What’s in a name. Anthropological Theory, 18(1), 18–35. https://doi-org.libproxy.stcloudstate.edu/10.1177/1463499617735259
Kernaghan, K. (2014). Digital dilemmas: Values, ethics and information technology. Canadian Public Administration, 57(2), 295–317. https://doi-org.libproxy.stcloudstate.edu/10.1111/capa.12069
Koucheryavy, Y., Kirichek, R., Glushakov, R., & Pirmagomedov, R. (2017). Quo vadis, humanity? Ethics on the last mile toward cybernetic organism. Russian Journal of Communication, 9(3), 287–293. https://doi-org.libproxy.stcloudstate.edu/10.1080/19409419.2017.1376561
Keyword search: ethic+ + autonomous vehicles = 46
Cerf, V. G. (2017). A Brittle and Fragile Future. Communications of the ACM, 60(7), 7. https://doi-org.libproxy.stcloudstate.edu/10.1145/3102112
Fleetwood, J. (2017). Public Health, Ethics, and Autonomous Vehicles. American Journal of Public Health, 107(4), 632–537. https://doi-org.libproxy.stcloudstate.edu/10.2105/AJPH.2016.303628
HARRIS, J. (2018). Who Owns My Autonomous Vehicle? Ethics and Responsibility in Artificial and Human Intelligence. Cambridge Quarterly of Healthcare Ethics, 27(4), 599–609. https://doi-org.libproxy.stcloudstate.edu/10.1017/S0963180118000038
Keeling, G. (2018). Legal Necessity, Pareto Efficiency & Justified Killing in Autonomous Vehicle Collisions. Ethical Theory & Moral Practice, 21(2), 413–427. https://doi-org.libproxy.stcloudstate.edu/10.1007/s10677-018-9887-5
Hevelke, A., & Nida-Rümelin, J. (2015). Responsibility for Crashes of Autonomous Vehicles: An Ethical Analysis. Science & Engineering Ethics, 21(3), 619–630. https://doi-org.libproxy.stcloudstate.edu/10.1007/s11948-014-9565-5
Getha-Taylor, H. (2017). The Problem with Automated Ethics. Public Integrity, 19(4), 299–300. https://doi-org.libproxy.stcloudstate.edu/10.1080/10999922.2016.1250575
Keyword search: ethic* + artificial intelligence = 349
Etzioni, A., & Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. Journal of Ethics, 21(4), 403–418. https://doi-org.libproxy.stcloudstate.edu/10.1007/s10892-017-9252-2
Köse, U. (2018). Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety. BRAIN: Broad Research in Artificial Intelligence & Neuroscience, 9(2), 184–197. Retrieved from http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3daph%26AN%3d129943455%26site%3dehost-live%26scope%3dsite
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http://www.cts.umn.edu/events/conference/2018
Keynote presentations will explore the future of driving and the evolution and potential of automated vehicle technologies.
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https://blog.stcloudstate.edu/ims/2016/02/26/philosophy-and-technology/
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims/2018/09/07/limbic-thought-artificial-intelligence/
AI and autonomous cars as ALA discussion topic
https://blog.stcloudstate.edu/ims/2018/01/11/ai-autonomous-cars-libraries/
and privacy concerns
https://blog.stcloudstate.edu/ims/2018/09/14/ai-for-education/
the call of the German scientists on ethics and AI
https://blog.stcloudstate.edu/ims/2018/09/01/ethics-and-ai/
AI in the race for world dominance
https://blog.stcloudstate.edu/ims/2018/04/21/ai-china-education/
Three aspects of digitization form the heart of an Industry 4.0 approach.
• The full digitization of a company’s operations
• The redesign of products and services
• Closer interaction with customers
Making Industry 4.0 work requires major shifts in organizational practices and structures. These shifts include new forms of IT architecture and data management, new approaches to regulatory and tax compliance, new organizational structures, and — most importantly — a new digitally oriented culture, which must embrace data analytics as a core enterprise capability.
Klaus Schwab put it in his recent book The Fourth Industrial Revolution (World Economic Forum, 2016), “Contrary to the previous industrial revolutions, this one is evolving at an exponential rather than linear pace.… It is not only changing the ‘what’ and the ‘how’ of doing things, but also ‘who’ we are.”
This great integrating force is gaining strength at a time of political fragmentation — when many governments are considering making international trade more difficult. It may indeed become harder to move people and products across some national borders. But Industry 4.0 could overcome those barriers by enabling companies to transfer just their intellectual property, including their software, while letting each nation maintain its own manufacturing networks.
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more on the Internet of Things in this IMS blog
https://blog.stcloudstate.edu/ims?s=internet+of+things
also Digital Learning
https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/
for Google’s corporate parent, Alphabet, the opportunities in the world’s largest internet market may be too good to resist. And the full scope of the company’s interest in China now appears to be broader than just internet search.
The latest hint came from Waymo, the driverless-car company that was spun out of Google in 2016. Chinese media noticed this week that the business had quietly registered a Shanghai subsidiary in May, suggesting that it wants a piece of an industry that the Chinese government has made a priority.
Unlike Google, Apple runs its own app store in China, heeding government directives about the kinds of apps that can be available to Chinese users. Microsoft and Amazon offer cloud computing services, working with local partners and following strict controls on how customers’ data is stored.
Baidu, maker of the country’s leading search engine, has made its autonomous-vehicle software platform available to dozens of local and foreign companies. SAIC Motor, China’s largest carmaker, is working with the e-commerce titan Alibaba. BMW and Daimler have received permission in China to test their own self-driving vehicles.
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more on Google and China in this IMS blog
https://blog.stcloudstate.edu/ims?s=google+china