Artificial imagination

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Artificial imagination is a narrow subcomponent of artificial general intelligence which generates, simulates, and facilitates [1] real or possible fiction models to create predictions, inventions, [2] or conscious experiences.

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The term artificial imagination is also used to describe a property of machines or programs. Some of the traits that researchers hope to simulate include creativity, vision, digital art, humor, and satire. [3]

Practitioners in the field are researching various aspects of Artificial imagination, such as Artificial (visual) imagination, [4] Artificial (aural) Imagination, [5] modeling/filtering content based on human emotions and Interactive Search. Some articles on the topic speculate on how artificial imagination may evolve to create an artificial world "people may be comfortable enough to escape from the real world". [6]

Some researchers such as G. Schleis and M. Rizki have focused on using artificial neural networks to simulate artificial imagination. [7]

Another important project is being led by Hiroharu Kato and Tatsuya Harada at the University of Tokyo in Japan. They have developed a computer capable of translating a description of an object into an image, which could be the easiest way to define what imagination is. Their idea is based on the concept of an image as a series of pixels divided into short sequences that correspond to a specific part of an image. The scientists call this sequences “visual words” and those can be interpreted by the machine using statistical distribution to read an create an image of an object the machine has not encountered.

The topic of artificial imagination has garnered interest from scholars outside the computer science domain, such as noted communications scholar Ernest Bormann, who came up with the Symbolic Convergence Theory and worked on a project to develop artificial imagination in computer systems. [8] An interdisciplinary research seminar organized by the artist Grégory Chatonsky on artificial imagination and postdigital art has taken place since 2017 at the Ecole Normale Supérieure in Paris. [9]

The typical application of artificial imagination is for an interactive search. Interactive searching has been developed since the mid-1990s, accompanied by the World Wide Web's development and the optimization of search engines. Based on the first query and feedback from a user, the databases to be searched are reorganized to improve the searching results.

Artificial imagination allows us to synthesize images and to develop a new image, whether it is in the database, regardless its existence in the real world. For example, the computer shows results that are based on the answer from the initial query. The user selects several relevant images, and then the technology analyzes these selections and reorganizes the images' ranks to fit the query. In this process, artificial imagination is used to synthesize the selected images and to improve the searching result with additional relevant synthesized images. This technique is based on several algorithms, including the Rocchio algorithm and the evolutionary algorithm. The Rocchio algorithm, [10] locating a query point near relevant examples and far away from irrelevant examples, is simple and works well in a small system where the databases are arranged in certain ranks. The evolutionary synthesis is composed of two steps: a standard algorithm and an enhancement of the standard algorithm. [11] [12] Through feedback from the user, there would be additional images synthesized so as to be suited to what the user is looking for.

General artificial imagination

Artificial imagination has a more general definition and wide applications. [13] The traditional fields of artificial imagination include visual imagination and aural imagination. More generally, all the actions to form ideas, images and concepts can be linked to imagination. Thus, artificial imagination means more than only generating graphs. [14] For example, moral imagination is an important research subfield of artificial imagination, although classification of artificial imagination is difficult.

Morals are an important part to human beings' logic, while artificial morals are important in artificial imagination and artificial intelligence. A common criticism of artificial intelligence is whether human beings should take responsibility for machines‘ mistakes or decisions and how to develop well-behaved machines. [15] [16] As nobody can give a clear description of the best moral rules, it is impossible to create machines with commonly accepted moral rules. [17] However, recent research about artificial morals circumvent the definition of moral. Instead, machine learning methods are applied to train machines to imitate human morals. [18] [19] As the data about moral decisions from thousands of different people are considered, the trained moral model can reflect widely accepted rules. [19]

Memory is another major field of artificial imagination. Researchers such as Aude Oliva have performed extensive work on artificial memory, especially visual memory. [20] Compared to visual imagination, the visual memory focuses more on how machine understand, analyse and store pictures in a human way. In addition, characters like spatial features are also considered. As this field is based on the brains' biological structures, extensive research on neuroscience has also been performed, which makes it a large intersection between biology and computer science.

See also

Further reading

Related Research Articles

Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions. "Understanding" in this context signifies the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Advances in the field of deep learning have allowed neural networks to surpass many previous approaches in performance.

<span class="mw-page-title-main">Content-based image retrieval</span> Method of image retrieval

Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches.

<span class="mw-page-title-main">Swarm intelligence</span> Collective behavior of decentralized, self-organized systems

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

<span class="mw-page-title-main">Thomas Huang</span> Chinese-American engineer and computer scientist (1936–2020)

Thomas Shi-Tao Huang was a Chinese-born Taiwanese-American computer scientist and electrical engineer. He was a researcher and professor emeritus at the University of Illinois at Urbana-Champaign (UIUC). Huang was one of the leading figures in computer vision, pattern recognition and human computer interaction.

Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking is a computer science technique in which a machine performs its function by outsourcing certain steps to humans, usually as microwork. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training Deep Learning-based Artificial Intelligence systems. In this case, human-based computation has been referred to as human-aided artificial intelligence.

<span class="mw-page-title-main">Algorithmic art</span> Art genre

Algorithmic art or algorithm art is art, mostly visual art, in which the design is generated by an algorithm. Algorithmic artists are sometimes called algorists.

The following outline is provided as an overview of and topical guide to artificial intelligence:

<span class="mw-page-title-main">Imagination</span> Creative ability

Imagination is the production of sensations, feelings and thoughts informing oneself. These experiences can be re-creations of past experiences, such as vivid memories with imagined changes, or completely invented and possibly fantastic scenes. Imagination helps apply knowledge to solve problems and is fundamental to integrating experience and the learning process.

Human–computer information retrieval (HCIR) is the study and engineering of information retrieval techniques that bring human intelligence into the search process. It combines the fields of human-computer interaction (HCI) and information retrieval (IR) and creates systems that improve search by taking into account the human context, or through a multi-step search process that provides the opportunity for human feedback.

<span class="mw-page-title-main">Computational creativity</span> Multidisciplinary endeavour

Computational creativity is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.

Sketch recognition describes the process by which a computer, or artificial intelligence can interpret hand-drawn sketches created by a human being, or other machine. Sketch recognition is a key frontier in the field of artificial intelligence and human-computer interaction, similar to natural language processing or conversational artificial intelligence

Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects.

Multimedia information retrieval is a research discipline of computer science that aims at extracting semantic information from multimedia data sources. Data sources include directly perceivable media such as audio, image and video, indirectly perceivable sources such as text, semantic descriptions, biosignals as well as not perceivable sources such as bioinformation, stock prices, etc. The methodology of MMIR can be organized in three groups:

  1. Methods for the summarization of media content. The result of feature extraction is a description.
  2. Methods for the filtering of media descriptions
  3. Methods for the categorization of media descriptions into classes.
<span class="mw-page-title-main">DeepDream</span> Software program

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This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

<span class="mw-page-title-main">Edward Y. Chang</span> American computer scientist

Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.

References

  1. ↑ Abramson, J.; Ahuja, A; Carnevale, F. (21 November 2022). "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback". p. 26. arXiv: 2211.11602 [cs.LG].
  2. ↑ Allen, K.R.; Lopez-Guevara, T.; Stachenfeld, K.; Sanchez-Gonzalez, A.; Battaglia, P.; Hamrick, J.; Pfaff, T. (1 February 2022). "Physical Design using Differentiable Learned Simulators". arXiv: 2202.00728 [cs.LG].
  3. ↑ "How Generative AI Can Augment Human Creativity". Harvard Business Review. 2023-06-16. ISSN   0017-8012 . Retrieved 2023-06-20.
  4. ↑ Thomee, B.; Huiskes, M.J.; Bakker, E.; Lew, M.S. (July 2007). "Visual information retrieval using synthesized imagery". Proceedings of the 6th ACM international conference on Image and video retrieval. ACM. pp. 127–130. doi:10.1145/1282280.1282303. ISBN   9781595937339. S2CID   11199318 . Retrieved 19 December 2023.
  5. ↑ Audio Content Transmission by Xavier Amatriain & Perfecto Herrera, "Publications" (PDF). Archived from the original (PDF) on 2007-01-06. Retrieved 2007-12-22.
  6. ↑ Hypertext and “the Hyperreal” by Stuart Moulthrop, Yale University http://portal.acm.org/citation.cfm?doid=74224.74246
  7. ↑ Learning from a random player using the reference neuron model in the Proceedings of the 2002 Congress on Evolutionary Computation, 2002. https://ieeexplore.ieee.org/document/1007019/;jsessionid=BBE5A0E379BB9B061933356B7461B639?arnumber=1007019
  8. ↑ Twentieth-Century Roots of Rhetorical Studies, by Jim A. Kuypers and Andrew King, 2001. published by Praeger/Greenwood, page 225.
  9. ↑ Postdigital Artificial Imagination http://postdigital.ens.fr
  10. ↑ Salton, Gerard; Buckley, Chris (June 1, 1990). "Improving retrieval performance by relevance feedback". Journal of the American Society for Information Science. 41 (4): 288–297. doi:10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H. hdl: 1813/6738 .
  11. ↑ "Using an artificial imagination for texture retrieval". 2008 19th International Conference on Pattern Recognition. December 2008. CiteSeerX   10.1.1.330.1562 .
  12. ↑ An Artificial Imagination for Interactive Search (PDF). Springer Berlin Heidelberg. 2007. pp. 19–28.
  13. ↑ Runco, Mark A. (2023-12-01). "AI can only produce artificial creativity". Journal of Creativity. 33 (3): 100063. doi: 10.1016/j.yjoc.2023.100063 . ISSN   2713-3745.
  14. ↑ Valevski, Dani; Leviathan, Yaniv; Arar, Moab; Fruchter, Shlomi (2024). "Diffusion Models Are Real-Time Game Engines". arXiv: 2408.14837 [cs.LG].
  15. ↑ Tigard, Daniel W. (2021-06-10). "Artificial Moral Responsibility: How We Can and Cannot Hold Machines Responsible". Cambridge Quarterly of Healthcare Ethics. 30 (3): 435–447. doi:10.1017/s0963180120000985. ISSN   0963-1801. PMID   34109925.
  16. ↑ Constantinescu, Mihaela; Vică, Constantin; Uszkai, Radu; Voinea, Cristina (2022-04-12). "Blame It on the AI? On the Moral Responsibility of Artificial Moral Advisors". Philosophy & Technology. 35 (2). doi:10.1007/s13347-022-00529-z. ISSN   2210-5433.
  17. ↑ Allen, Colin; Varner, Gary; Zinser, Jason (July 2000). "Prolegomena to any future artificial moral agent". Journal of Experimental & Theoretical Artificial Intelligence. 12 (3): 251–261. doi:10.1080/09528130050111428. ISSN   0952-813X.
  18. ↑ Moser, Christine; den Hond, Frank; Lindebaum, Dirk (March 2022). "Morality in the Age of Artificially Intelligent Algorithms". Academy of Management Learning & Education. 21 (1): 139–155. doi:10.5465/amle.2020.0287. hdl: 1871.1/042dea52-f339-445e-932c-8a06c9a51c0a . ISSN   1537-260X.
  19. 1 2 Floridi, Luciano (2016-12-28). "Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 374 (2083): 20160112. Bibcode:2016RSPTA.37460112F. doi:10.1098/rsta.2016.0112. ISSN   1364-503X. PMID   28336791.
  20. ↑ Oliva, Aude (2008). "Visual long-term memory has a massive storage capacity for object details". Proceedings of the National Academy of Sciences. 105 (38): 14325–14329. Bibcode:2008PNAS..10514325B. doi: 10.1073/pnas.0803390105 . PMC   2533687 . PMID   18787113.
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