Weak artificial intelligence

Form of artificial intelligence

Weak artificial intelligence (weak AI) is artificial intelligence that implements a limited part of the mind, or, as narrow AI, artificial narrow intelligence (ANI),[1][2][3] is focused on one narrow task.

Weak AI is contrasted with strong AI, which can be interpreted in various ways:

* Artificial general intelligence (AGI): a machine with the ability to apply intelligence to any problem, rather than just one specific problem.
* Artificial super intelligence (ASI): a machine with a vastly superior intelligence to the average human being.
* Artificial consciousness: a machine that has consciousness, sentience and mind (John Searle uses "strong AI" in this sense).

Narrow AI can be classified as being "limited to a single, narrowly defined task. Most modern AI systems would be classified in this category."[4] Artificial general intelligence is conversely the opposite.

## Applications and risks

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Some examples of narrow AI are AlphaGo,[5] self-driving cars, robot systems used in the medical field, and diagnostic doctors. Narrow AI systems are sometimes dangerous if unreliable. And the behavior that it follows can become inconsistent.[6] It could be difficult for the AI to grasp complex patterns and get to a solution that works reliably in various environments. This "brittleness" can cause it to fail in unpredictable ways.[7]

Narrow AI failures can sometimes have significant consequences. It could for example cause disruptions in the electric grid, damage nuclear power plants, cause global economic problems, and misdirect autonomous vehicles.[1] Medicines could be incorrectly sorted and distributed. Also, medical diagnoses can ultimately have serious and sometimes deadly consequences if the AI is faulty or biased.[8]

Simple AI programs have already worked their way into society, oftentimes unnoticed by the public. Autocorrection for typing, speech recognition for speech-to-text programs, and vast expansions in the data science fields are examples.[9] Narrow AI has also been the subject of some controversy, including resulting in unfair prison sentences, discrimination against women in the workplace for hiring, resulting in death via autonomous driving, among other cases.[10]

Despite being "narrow" AI, recommender systems are efficient at predicting user reactions based their posts, patterns, or trends.[11] For instance, TikTok's "For You" algorithm can determine a user's interests or preferences in less than an hour.[12] Some other social media AI systems are used to detect bots that may be involved in propaganda or other potentially malicious activities.[13]

## Weak AI versus strong AI

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John Searle contests the possibility of strong AI (by which he means conscious AI). He further believes that the Turing test (created by Alan Turing and originally called the "imitation game", used to assess whether a machine can converse indistinguishably from a human) is not accurate or appropriate for testing whether an AI is "strong".[14]

Scholars such as Antonio Lieto have argued that the current research on both AI and cognitive modelling are perfectly aligned with the weak-AI hypothesis (that should not be confused with the "general" vs "narrow" AI distinction) and that the popular assumption that cognitively inspired AI systems espouse the strong AI hypothesis is ill-posed and problematic since "artificial models of brain and mind can be used to understand mental phenomena without pretending that that they are the real phenomena that they are modelling"[15] (as, on the other hand, implied by the strong AI assumption).

## See also

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* Artificial intelligence – Intelligence of machines
* Artificial general intelligence – Type of AI with wide-ranging abilities
* Deep learning – Branch of machine learning
* Expert system – Computer system emulating the decision-making ability of a human expert
* Hardware for artificial intelligence – Hardware specially designed and optimized for artificial intelligence
* History of artificial intelligence
* Machine learning – Study of algorithms that improve automatically through experience
* Philosophy of artificial intelligence
* Synthetic intelligence – Alternate term for or form of artificial intelligence
* Virtual assistant – Software agent
* Workplace impact of artificial intelligence – Impact of artificial intelligence on workers

## References

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1. ^ a b Dvorsky, George (1 April 2013). "How Much Longer Before Our First AI Catastrophe?". Gizmodo. Retrieved 27 November 2021.
2. ^ Muehlhauser, Luke (18 October 2013). "Ben Goertzel on AGI as a Field". Machine Intelligence Research Institute. Retrieved 27 November 2021.
3. ^ Chalfen, Mike (15 October 2015). "The Challenges Of Building AI Apps". TechCrunch. Retrieved 27 November 2021.
4. ^ Bartneck, Christoph; Lütge, Christoph; Wagner, Alan; Welsh, Sean (2021). An Introduction to Ethics in Robotics and AI. SpringerBriefs in Ethics. Cham: Springer International Publishing. doi:10.1007/978-3-030-51110-4. ISBN 978-3-030-51109-8. S2CID 224869294.
5. ^ Edelman, Gary Grossman (3 September 2020). "We're entering the AI twilight zone between narrow and general AI". VentureBeat. Retrieved 16 March 2024.
6. ^ Kuleshov, Andrey; Prokhorov, Sergei (September 2019). "Domain Dependence of Definitions Required to Standardize and Compare Performance Characteristics of Weak AI Systems". 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI). Belgrade, Serbia: IEEE. pp. 62–623\. doi:10.1109/IC-AIAI48757.2019.00020. ISBN 978-1-7281-4326-2. S2CID 211298012.
7. ^ Staff, Bulletin (23 April 2018). "The promise and peril of military applications of artificial intelligence". Bulletin of the Atomic Scientists. Retrieved 2 October 2024.
8. ^ Szocik, Konrad; Jurkowska-Gomułka, Agata (16 December 2021). "Ethical, Legal and Political Challenges of Artificial Intelligence: Law as a Response to AI-Related Threats and Hopes". World Futures: 1–17\. doi:10.1080/02604027.2021.2012876. ISSN 0260-4027. S2CID 245287612.
9. ^ Earley, Seth (2017). "The Problem With AI". IT Professional. 19 (4): 63–67\. doi:10.1109/MITP.2017.3051331. ISSN 1520-9202. S2CID 9382416.
10. ^ Anirudh, Koul; Siddha, Ganju; Meher, Kasam (2019). Practical Deep Learning for Cloud, Mobile, and Edge. O'Reilly Media. ISBN 9781492034865.
11. ^ Kaiser, Carolin; Ahuvia, Aaron; Rauschnabel, Philipp A.; Wimble, Matt (1 September 2020). "Social media monitoring: What can marketers learn from Facebook brand photos?". Journal of Business Research. 117: 707–717\. doi:10.1016/j.jbusres.2019.09.017. ISSN 0148-2963. S2CID 203444643.
12. ^ Hyunjin, Kang (September 2022). "AI agency vs. human agency: understanding human-AI interactions on TikTok and their implications for user engagement". Journal of Computer-Mediated Communication. 27 (5). doi:10.1093/jcmc/zmac014. hdl:10356/165641. Retrieved 8 November 2022.
13. ^ Shukla, Rachit; Sinha, Adwitiya; Chaudhary, Ankit (28 February 2022). "TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks". Electronics. 11 (5): 743. doi:10.3390/electronics11050743. ISSN 2079-9292.
14. ^ Liu, Bin (28 March 2021). ""Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?". arXiv:2103.15294 [cs.AI].
15. ^ Lieto, Antonio (2021). Cognitive Design for Artificial Minds. London, UK: Routledge, Taylor & Francis. p. 85\. ISBN 9781138207929.