FocusComputers able to provide novel solutions to complex problems
- Knowledge is … ever-evolving repertoire of possibility
- Knowing is … functioning
- Learner is … a digital technology
- Learning is … expanding the repertoire of possibility
- Teaching is … challenging
SynopsisPopularly, Artificial Intelligence (AI) is understood in terms of machines mimicking human cognition – learning, solving problems, and so on. It is more properly understood as a domain of computer science focused on devices that are able to take some level of agency in achieving novel goals. Beyond that detail, the scope of AI is disputed, in part because “novel goals” is a moving target, as once-unimaginable feats become routine. Commonly mentioned aspects include abilities to collect relevant information about the environment, draw inferences based on that information, and apply those inferences flexibly – all of which are associated with capacities to learn, perceive, interpret, classify, test, compare, select, reason, plan, and reflect.
Prominent constructs and subdiscourses include:
- Cognitive Computing – One strategy used to cut through the clutter of meanings for Artificial Intelligence is to distinguish it from Cognitive Computing. According to advocates of this tactic, AI is defined simply in terms of using technologies to augment human intelligence, to solve complex problems, and to meet complex demands in real time. In contrast, Cognitive Computing is interesting in mimicking human action and reasoning – that is Cognitive Computing refers to those AI efforts that limit themselves to thinking strategies humans would use.
- Computational Creativity (Artificial Creativity; Creative Computation; Creative Computing; Mechanical Creativity) (2010s) – a blending of Artificial Intelligence, Cognitive Psychology, and Neuroscience, aiming to enhance creativity through two complementary foci: (1) to simulate and replicate human creativity using computers and (2) to better understand human creativity
- General AI – that aspect of Artificial Intelligence research that addresses novel and/or expansive tasks that are located in open systems, requiring some manner of invention and/or innovative transfer from another situation (Compare Narrow AI, below.)
- Knowledge Representation and Reasoning (KRR; KR&R; KR2) (Brian Smith, 1980s) – in Artificial Intelligence, a field that combines insights from Psychology and Modes of Reasoning in efforts to devise modes of formatting information in ways that computers can use to solve complex tasks
Narrow AI – that aspect of Artificial Intelligence research that’s focused on specific tasks within a closed system (i.e., all of the possibilities can be specified (Compare General AI, above.)
- Neural Network (Artificial Neural Network) – a reference to combinations hardware and software designed to simulate brain functioning
Perhaps more than any other discourse mentioned on this site, Artificial Intelligence attracts speculation on the future of humanity's relationship with technology. Examples that might be construed as related to discourses on learning include:
- Intelligence Explosion – a hypothesized point in the development of Artificial Intelligence at which AI is able to generate new, faster, and more intelligent AI, which in turn generates new, faster, and more intelligent AI, and so on
- Technological Singularity (The Singularity) (Ray Kurzweil, 2000s)– a hypothesized point in the development of technology at which humanity loses the ability to control (or anticipate) its evolution, leading to unimaginably greater intelligence
CommentaryCriticisms of and anxieties with AI tend to revolve around its potential unintended and unanticipated consequences, with the most extreme worries focused on sentient machines that would merit the same rights as humans, thinking machines that might assume governance of humans, and super-intelligent machines that might wipe out humans.
Authors and/or Prominent InfluencesWarren McCullouch Marvin Minsky Walter Pitts Herbert Simon
Status as a Theory of LearningArtificial Intelligence is a domain of research, but it can also be construed as positing, investigating, and confirming a theory of learning. In the process, AI has afforded insights into and human learning, especially around the untenability of the metaphors and dualisms that are typical of popular-but-untenable Correspondence Discourses.
Status as a Theory of TeachingArtificial Intelligence is not a theory of teaching.
Status as a Scientific TheoryAI is a scientific domain – but, that said, it has had a checkered history since being founded in the 1950s. Spurred by early successes in programming machines to perform tasks that the programmers themselves found difficult (e.g. logic, advanced mathematics), confident predictions were made that machines would soon surpass humans. Against those expectations, there was surprisingly little progress in the first 50 years of AI research, apart from the slow realization that competencies that humans find hard (e.g., logic, chess) are often easy to program, but competencies that are routinely mastered by young children (e.g., recognizing faces, learning a language) are very difficult to program. A major breakthrough came in the late-1990s, when assumptions rooted in Correspondence Discourses, such as interpreting thought in terms of processing inputted information and symbolically encoded internal representations, were abandoned and replaced with evolution-based, exploration-oriented, and emergentist (e.g., iterative dynamics, co-entangled nested systems, swarm tactics) notions associated with Coherence Discourses.
- Cognitive Computing
- Computational Creativity (Artificial Creativity; Creative Computation; Creative Computing; Mechanical Creativity)
- General AI
- Intelligence Explosion
- Knowledge Representation and Reasoning
- Narrow AI
- Neural Network (Artificial Neural Network)
- Technological Singularity (The Singularity)
Please cite this article as:
Davis, B., & Francis, K. (2022). “Artificial Intelligence” in Discourses on Learning in Education. https://learningdiscourses.com.
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