User:Shengchieh Lee/sandbox

From Wikipedia, the free encyclopedia

The Algorithm:[1][2]

Artificial intelligence:[3][4]

Google Books:[5][6]

Drafts for selected articles[edit]

AI Learning[edit]

Machine learning (ML), a fundamental concept of AI research since the field's inception [9]which allows the system to gain the ability of self-learning and improved from analyzing experience without complicated programming. [10] Besides, the intention for machine learning is to make a better decision based on human experiences and collated data, allowing the computer work by its features without any kind of human interactions.[10]

At a deeper level of machine learning, there are two key methods which are unsupervised and supervised. Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization[11]. In reinforcement learning[12] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.The pictures of cats and gods provide a great example of unsupervised learning. Suppose we provide an image(Picture A) having both dogs and cats sitting together which machine(computer) has not ever seen before, and then the machine cannot recognize and further categorize them with their similarities because they do not know any dog's and cats' features. However, the machine can learn to categorize them by grouping their similarities, patterns, and differences, etc., and then it will easily categorize the above picture into two separate parts. The First category will contain all shapes sharing dog's features in it and the second part will include all similar shapes of cats.[13]

The other picture of a fruit basket(Picture B) described supervised learning, The machine familiar features of each kind of fruit by reviewing data. The long curved yellow or green objects will be classified as a banana; the rounded red objects with the sunken at the top will be classified as an apple; the tiny round shape with compacted form objects will be classified as grapes; the rounded objects with light orange color will be classified as an orange.[14]

The Dartmouth proposal
"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal from the seminal event for AI called Dartmouth Conference which holding in 1956[15].

AI In fiction[edit]

The word "robot" itself was coined by Karel Čapek in his 1921 play R.U.R., the title standing for "Rossum's Universal Robots"

Thought-capable artificial beings appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction.

A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.[16]

Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov's laws are often brought up during lay discussions of machine ethics;[17] while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.[18]

Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always an unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.

Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.[19]

From the further study on AI and importation of it into media by Butler, the topic of Artificial Intelligence has become an indispensable part of pop culture and fiction, and there are many new types of media applying the elements of AI as a human being. One of the well-known games released in 2018 called "Detroit: Become Human" illustrates how AI(which is called Android in the game) using pathos, ethos, and logos in special circumstances, displaying a world full of bionics people with AI.[20] The game informs some philosophical thoughts on human identity and consciousness and provides some potential answers from details in the game. Detroit becomes human also draws people into some ethical question on how should we treat those Andrioids who wanna live like real human beings.

References[edit]

  1. ^ Buckingham, Marcus (June 2012). "Leadership Development in the Age of the Algorithm". Harvard Business Review.{{cite web}}: CS1 maint: url-status (link)
  2. ^ Clark, Josh (Jun 7, 2017). "DESIGN IN THE ERA OF THE ALGORITHM". big medium.{{cite web}}: CS1 maint: url-status (link)
  3. ^ Ahmet, Christina (2018). How Advance Machine Learning Will Shape The Future Of Our World.
  4. ^ Russell, Stuart. "BENEFITS & RISKS OF ARTIFICIAL INTELLIGENCE". Future of life.{{cite web}}: CS1 maint: url-status (link)
  5. ^ Chandler, Nathan. "How Google Books Works". howstuffworks?.{{cite web}}: CS1 maint: url-status (link)
  6. ^ Howard, Jennifer (Aug 10, 2017). "What Happened to Google's Effort to Scan Millions of University Library Books?". EdSurge.{{cite web}}: CS1 maint: url-status (link)
  7. ^ Turing 1950.
  8. ^ Solomonoff 1956.
  9. ^ Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[7] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[8]
  10. ^ a b Team, Expert System (March 2017). "What is Machine Learning? A definition". Expert System. {{cite web}}: |archive-date= requires |archive-url= (help)CS1 maint: url-status (link)
  11. ^ Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218.
  12. ^ "Reinforcement learning", Wikipedia, 2020-10-22, retrieved 2020-10-25
  13. ^ "Supervised and Unsupervised learning - GeeksforGeeks". GeeksforGeeks. 2017-10-01. Retrieved 2020-11-01.
  14. ^ "Supervised and Unsupervised learning". GeeksforGeeks. 2017-10-01. Retrieved 2020-11-01.
  15. ^ "Dartmouth workshop", Wikipedia, 2020-05-31, retrieved 2020-10-25
  16. ^ Buttazzo, G. (July 2001). "Artificial consciousness: Utopia or real possibility?". Computer. 34 (7): 24–30. doi:10.1109/2.933500.
  17. ^ Anderson, Susan Leigh. "Asimov's "three laws of robotics" and machine metaethics." AI & Society 22.4 (2008): 477–493.
  18. ^ McCauley, Lee (2007). "AI armageddon and the three laws of robotics". Ethics and Information Technology. 9 (2): 153–164. CiteSeerX 10.1.1.85.8904. doi:10.1007/s10676-007-9138-2. S2CID 37272949.
  19. ^ Galvan, Jill (1 January 1997). "Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"". Science Fiction Studies. 24 (3): 413–429. JSTOR 4240644.
  20. ^ "Detroit: Become Human", Wikipedia, 2020-10-24, retrieved 2020-10-25

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