New WTO Report Explores the Impact of Artificial Intelligence on Global Trade

A new report published today, 21 November, by the World Trade Organization (WTO) Secretariat delves into the growing potential of artificial intelligence (AI) to reshape world trade. The report presents an in-depth analysis of how AI technologies are influencing international commerce and examines the various trade-related policy considerations this advancement raises for governments and businesses alike.

AI’s Transformative Impact on Global Trade

The report highlights how AI is increasingly being applied in sectors such as logistics, manufacturing, services, and trade facilitation, and its capacity to drive productivity, efficiency, and innovation across global value chains. However, the report also emphasizes that the rapid integration of AI into the global economy presents new challenges, including issues of fairness, privacy, and access to technology. These concerns, according to the WTO, underscore the need for international collaboration to ensure that AI’s benefits are broadly shared while mitigating potential risks.

Key Policy Considerations for AI in Trade

Several critical policy areas are raised in the report, which the WTO believes will be vital to navigating the future of AI in international trade:

1. AI in Trade Facilitation and Customs Procedures: The report highlights how AI can streamline customs processes, reduce trade barriers, and improve efficiency in cross-border transactions. AI-powered systems, for instance, are already being used to speed up customs inspections and manage global supply chains.
2. Digital Trade and E-Commerce: AI is playing an increasing role in enhancing digital trade, which has become a dominant force in international markets. The ability to personalize services, optimize supply chains, and facilitate online transactions has created new opportunities for cross-border e-commerce. The WTO report discusses how these developments may influence global trade rules, particularly in relation to data flows and intellectual property protection.
3. Workforce and Skills Development: The report also explores the implications of AI on labor markets, emphasizing the need for nations to invest in workforce training and reskilling to help workers adapt to an AI-driven economy. As AI automates certain tasks, there will be both disruptions and new job creation, which will require targeted policy responses.
4. International Cooperation on Regulation and Standards: A major theme of the report is the importance of establishing global regulatory frameworks that foster innovation while ensuring the ethical use of AI. The WTO calls for a multilateral approach to setting international standards for AI, aiming to reduce the risk of fragmentation in AI governance.

The WTO’s Role in Facilitating AI-Related Trade

The report underscores the central role the WTO can play in ensuring that AI becomes a driver of global trade rather than a source of fragmentation. As AI presents complex legal, ethical, and technical challenges, the WTO is positioned to help members develop cooperative solutions and align international rules on AI.

In particular, the WTO is seen as an essential player in promoting global regulatory convergence. This would help reduce trade barriers and allow AI technologies to be deployed more effectively across borders. The report stresses the importance of collaboration between governments, the private sector, and international organizations to create a trustworthy AI ecosystem that supports global trade while addressing concerns related to transparency, accountability, and security.

WTO Event Launches the Report

The launch of the report took place at a high-profile event at the WTO, where representatives from government, academia, and the private sector gathered to discuss AI’s implications for trade. This event served as a platform for dialogue on how AI can be integrated into global trade systems in a way that promotes inclusivity, fairness, and sustainability.

Concluding Remarks

The WTO’s new report sheds light on the transformative potential of AI for international trade, while emphasizing the importance of cooperation, regulation, and innovation. As AI continues to evolve, the WTO remains committed to supporting its members in navigating the opportunities and challenges posed by this groundbreaking technology. By promoting trustworthy AI systems and fostering global regulatory convergence, the WTO aims to ensure that AI becomes a force for good in driving global trade and economic development.

WTO Report Highlights AI’s Role in Transforming Global Trade and Its Regulatory Challenges

A new report titled “Trading with Intelligence: How AI Shapes and is Shaped by International Trade” explores the transformative potential of artificial intelligence (AI) in global trade. Published by the World Trade Organization (WTO) Secretariat, the report emphasizes how AI can reduce trade costs, reshape trade in services, boost trade in AI-related goods and services, and alter the comparative advantages of economies worldwide. It also discusses the increasing fragmentation of AI regulations and their impact on trade opportunities, particularly for micro, small, and medium-sized businesses.

AI’s Impact on Trade Costs and Economic Opportunities

The report examines how AI can address significant barriers in international trade, including those related to logistics, supply chain management, and regulatory compliance. By automating processes such as customs clearance and border controls, AI has the potential to streamline trade operations, reduce delays, and navigate complex trade regulations. The report highlights that AI could lower trade costs, providing smaller businesses and developing economies with greater access to global markets. This can help level the playing field and increase international trade participation, especially for businesses that have traditionally struggled with the complexities of cross-border trade.

According to the report, the global real trade growth could increase by nearly 14 percentage points by 2040, assuming widespread AI adoption and high productivity gains. Conversely, a more cautious scenario—where AI adoption and productivity growth are uneven—forecasts a trade growth increase of just under 7 percentage points. Notably, while high-income economies are expected to experience the largest productivity gains, developing economies are poised to benefit significantly by reducing trade costs.

Shifting Patterns in Services Trade

AI is also poised to revolutionize trade in services, particularly digitally delivered services. The report anticipates that services trade could grow by nearly 18 percentage points under the optimistic scenario of universal AI adoption and productivity growth. The automation and optimization potential of AI in digital services could lead to increased global demand for such services, creating new opportunities for countries and businesses to expand in the digital economy.

Risks of an AI Divide

Despite the significant benefits AI can bring to global trade, the report warns of the risks associated with a growing AI divide. Economies and businesses with unequal access to AI technologies and expertise may struggle to compete, creating disparities in trade opportunities. Smaller firms, particularly in developing countries, could face disadvantages if they lack the resources or knowledge to adopt AI tools effectively.

The report highlights the importance of addressing the risks of fragmentation in AI governance, which could lead to inconsistent regulations and standards across different regions. This could make it harder for businesses to navigate AI-related trade opportunities and create barriers for smaller businesses trying to enter the global market.

Intellectual Property and Data Governance Challenges

Another critical issue raised in the report is the relationship between AI and intellectual property (IP) rights. As AI technologies develop, it becomes increasingly important to clarify how existing IP frameworks apply to AI-generated innovations. Data governance also poses a significant challenge, as AI relies heavily on access to large datasets. Ensuring that data is shared and used responsibly is key to fostering innovation while protecting privacy and security.

The Role of the WTO in AI Regulation and Trade

The report emphasizes the central role of the WTO in promoting multilateral cooperation on AI governance. As the global economy increasingly relies on AI, the WTO is uniquely positioned to foster discussions, negotiations, and rule-making processes to ensure that AI benefits are broadly shared while addressing its associated risks. WTO Director-General Ngozi Okonjo-Iweala writes in the foreword of the report: “This report aims to stimulate a discussion on how the WTO can promote the development and deployment of AI and help mitigate its associated risks and looming concerns about regulatory fragmentation.”

The WTO can serve as a forum for negotiating international rules and standards related to AI, helping to prevent fragmentation and ensure policy coherence across regions. By addressing the trade-related aspects of AI governance, the WTO can support the development of a global framework that ensures fairness and trust in AI technologies.

Conclusion

The report underscores the significant potential of AI to transform global trade by reducing costs, reshaping service sectors, and driving economic growth. However, it also warns of the need for careful management of AI’s risks, particularly with regard to regulatory fragmentation, data governance, and intellectual property. As AI continues to evolve, the WTO’s role in fostering international cooperation and policy coherence will be crucial in ensuring that the opportunities of AI are fully realized for all economies, large and small.

Courtesy :TED-Ed

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130. ^ Zarocostas, John (7 December 2013). “Global Trade Deal Reached”. WWD. Archived from the original on 11 December 2013. Retrieved 8 December 2013.
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132. ^ “Deputy Directors-General”. World Trade Organization. Archived from the original on 12 August 2017. Retrieved 6 November 2022.
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134. ^ Beattie, Alan; Williams, Aime (14 May 2020). “WTO chief Roberto Azevêdo to step down early”. The Financial Times. Archived from the original on 10 December 2022.
135. ^ “Moon, allies intensify campaign for Yoo Myung-hee to head WTO”. Joongang Daily. 12 October 2020. Archived from the original on 13 October 2020.
136. ^ Baschuk, Bryce (28 October 2020). “U.S. Sows WTO Turmoil by Vetoing Front-Runner for Top Job”. Bloomberg. Archived from the original on 31 October 2020. Retrieved 28 October 2020.
137. ^ “History is made: Ngozi Okonjo-Iweala chosen as Director-General”. 15 February 2021. Archived from the original on 5 June 2021. Retrieved 26 May 2021.
138. ^ World Trade Organization (2023). WTO Annual Report 2023 (PDF). pp. 198–199. ISBN 978-92-870-7428-7. Retrieved 29 March 2024. {{cite book}}: |website= ignored (help)
139. ^ “How to Save Globilization From Its Cheerleaders” (PDF). 8 February 2016. p. 14. Archived (PDF) from the original on 8 February 2016. Retrieved 19 May 2020.
140. ^ Joseph, Sarah; Joseph, Sarah Louise (2011). Blame it on the WTO?: A Human Rights Critique. OUP Oxford. pp. 171–174. ISBN 978-0-19-956589-4. Archived from the original on 14 February 2024. Retrieved 23 August 2020.
141. ^ Chen, Natalie (2021). “Gravity and heterogeneous trade cost elasticities”. Economic Journal. 132 (644): 1349–1377. doi:10.1093/ej/ueab067. OCLC 1259290547.
142. ^ Goldstein, Judith; Gulotty, Robert (2021). “Trading Away Tariffs: The Operations of the GATT System”. World Trade Review. 21 (2): 135–158. doi:10.1017/S1474745621000458. ISSN 1474-7456. S2CID 239630296.
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145. ^ Shin, Wonkyu; Ahn, Dukgeun (2019). “Trade Gains from Legal Rulings in the WTO Dispute Settlement System”. World Trade Review. 18 (1): 1–31. doi:10.1017/S1474745617000544. ISSN 1474-7456.
146. ^ Bown, Chad P. (2004). “On the Economic Success of GATT/WTO Dispute Settlement”. The Review of Economics and Statistics. 86 (3): 811–823. doi:10.1162/0034653041811680. S2CID 28108505. Archived from the original on 24 March 2020. Retrieved 24 March 2020.
147. ^ Bown, Chad P. (2004). “Trade Policy under the GATT/WTO: Empirical Evidence of the Equal Treatment Rule”. The Canadian Journal of Economics. 37 (3): 678–720. doi:10.1111/j.0008-4085.2004.00243.x. ISSN 0008-4085. JSTOR 3696011. S2CID 5705448.
148.  Russell & Norvig (2021), pp. 1–4.
149. ^ AI set to exceed human brain power Archived 2008-02-19 at the Wayback Machine CNN.com (July 26, 2006)
150. ^ Kaplan, Andreas; Haenlein, Michael (2019). “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence”. Business Horizons. 62: 15–25. doi:10.1016/j.bushor.2018.08.004. ISSN 0007-6813. S2CID 158433736.
151. ^ Jump up to:^a b c Artificial general intelligence: Russell & Norvig (2021, pp. 32–33, 1020–1021)
     Proposal for the modern version: Pennachin & Goertzel (2007)
     Warnings of overspecialization in AI from leading researchers: Nilsson (1995), McCarthy (2007), Beal & Winston (2009)
152. ^ Russell & Norvig (2021, §1.2).
153. ^ Jump up to:^a b Dartmouth workshop: Russell & Norvig (2021, p. 18), McCorduck (2004, pp. 111–136), NRC (1999, pp. 200–201)
     The proposal: McCarthy et al. (1955)
154. ^ Jump up to:^a b Successful programs of the 1960s: McCorduck (2004, pp. 243–252), Crevier (1993, pp. 52–107), Moravec (1988, p. 9), Russell & Norvig (2021, pp. 19–21)
155. ^ Jump up to:^a b Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): McCorduck (2004, pp. 426–441), Crevier (1993, pp. 161–162, 197–203, 211, 240), Russell & Norvig (2021, p. 23), NRC (1999, pp. 210–211), Newquist (1994, pp. 235–248)
156. ^ Jump up to:^a b First AI Winter, Lighthill report, Mansfield Amendment: Crevier (1993, pp. 115–117), Russell & Norvig (2021, pp. 21–22), NRC (1999, pp. 212–213), Howe (1994), Newquist (1994, pp. 189–201)
157. ^ Jump up to:^a b Second AI Winter: Russell & Norvig (2021, p. 24), McCorduck (2004, pp. 430–435), Crevier (1993, pp. 209–210), NRC (1999, pp. 214–216), Newquist (1994, pp. 301–318)
158. ^ Jump up to:^a b Deep learning revolution, AlexNet: Goldman (2022), Russell & Norvig (2021, p. 26), McKinsey (2018)
159. ^ Toews (2023).
160. ^ Problem-solving, puzzle solving, game playing, and deduction: Russell & Norvig (2021, chpt. 3–5), Russell & Norvig (2021, chpt. 6) (constraint satisfaction), Poole, Mackworth & Goebel (1998, chpt. 2, 3, 7, 9), Luger & Stubblefield (2004, chpt. 3, 4, 6, 8), Nilsson (1998, chpt. 7–12)
161. ^ Uncertain reasoning: Russell & Norvig (2021, chpt. 12–18), Poole, Mackworth & Goebel (1998, pp. 345–395), Luger & Stubblefield (2004, pp. 333–381), Nilsson (1998, chpt. 7–12)
162. ^ Jump up to:^a b c Intractability and efficiency and the combinatorial explosion: Russell & Norvig (2021, p. 21)
163. ^ Jump up to:^a b c Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: Kahneman (2011), Dreyfus & Dreyfus (1986), Wason & Shapiro (1966), Kahneman, Slovic & Tversky (1982)
164. ^ Knowledge representation and knowledge engineering: Russell & Norvig (2021, chpt. 10), Poole, Mackworth & Goebel (1998, pp. 23–46, 69–81, 169–233, 235–277, 281–298, 319–345), Luger & Stubblefield (2004, pp. 227–243), Nilsson (1998, chpt. 17.1–17.4, 18)
165. ^ Smoliar & Zhang (1994).
166. ^ Neumann & Möller (2008).
167. ^ Kuperman, Reichley & Bailey (2006).
168. ^ McGarry (2005).
169. ^ Bertini, Del Bimbo & Torniai (2006).
170. ^ Russell & Norvig (2021), pp. 272.
171. ^ Representing categories and relations: Semantic networks, description logics, inheritance (including frames, and scripts): Russell & Norvig (2021, §10.2 & 10.5), Poole, Mackworth & Goebel (1998, pp. 174–177), Luger & Stubblefield (2004, pp. 248–258), Nilsson (1998, chpt. 18.3)
172. ^ Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): Russell & Norvig (2021, §10.3), Poole, Mackworth & Goebel (1998, pp. 281–298), Nilsson (1998, chpt. 18.2)
173. ^ Causal calculus: Poole, Mackworth & Goebel (1998, pp. 335–337)
174. ^ Representing knowledge about knowledge: Belief calculus, modal logics: Russell & Norvig (2021, §10.4), Poole, Mackworth & Goebel (1998, pp. 275–277)
175. ^ Jump up to:^a b Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction: Russell & Norvig (2021, §10.6), Poole, Mackworth & Goebel (1998, pp. 248–256, 323–335), Luger & Stubblefield (2004, pp. 335–363), Nilsson (1998, ~18.3.3) (Poole et al. places abduction under “default reasoning”. Luger et al. places this under “uncertain reasoning”).
176. ^ Jump up to:^a b Breadth of commonsense knowledge: Lenat & Guha (1989, Introduction), Crevier (1993, pp. 113–114), Moravec (1988, p. 13), Russell & Norvig (2021, pp. 241, 385, 982) (qualification problem)
177. ^ Newquist (1994), p. 296.
178. ^ Crevier (1993), pp. 204–208.
179. ^ Russell & Norvig (2021), p. 528.
180. ^ Automated planning: Russell & Norvig (2021, chpt. 11).
181. ^ Automated decision making, Decision theory: Russell & Norvig (2021, chpt. 16–18).
182. ^ Classical planning: Russell & Norvig (2021, Section 11.2).
183. ^ Sensorless or “conformant” planning, contingent planning, replanning (a.k.a online planning): Russell & Norvig (2021, Section 11.5).
184. ^ Uncertain preferences: Russell & Norvig (2021, Section 16.7) Inverse reinforcement learning: Russell & Norvig (2021, Section 22.6)
185. ^ Information value theory: Russell & Norvig (2021, Section 16.6).
186. ^ Markov decision process: Russell & Norvig (2021, chpt. 17).
187. ^ Game theory and multi-agent decision theory: Russell & Norvig (2021, chpt. 18).
188. ^ Learning: Russell & Norvig (2021, chpt. 19–22), Poole, Mackworth & Goebel (1998, pp. 397–438), Luger & Stubblefield (2004, pp. 385–542), Nilsson (1998, chpt. 3.3, 10.3, 17.5, 20)
189. ^ Turing (1950).
190. ^ Solomonoff (1956).
191. ^ Unsupervised learning: Russell & Norvig (2021, pp. 653) (definition), Russell & Norvig (2021, pp. 738–740) (cluster analysis), Russell & Norvig (2021, pp. 846–860) (word embedding)
192. ^ Jump up to:^a b Supervised learning: Russell & Norvig (2021, §19.2) (Definition), Russell & Norvig (2021, Chpt. 19–20) (Techniques)
193. ^ Reinforcement learning: Russell & Norvig (2021, chpt. 22), Luger & Stubblefield (2004, pp. 442–449)
194. ^ Transfer learning: Russell & Norvig (2021, pp. 281), The Economist (2016)
195. ^ “Artificial Intelligence (AI): What Is AI and How Does It Work? | Built In”. builtin.com. Retrieved 30 October 2023.
196. ^ Computational learning theory: Russell & Norvig (2021, pp. 672–674), Jordan & Mitchell (2015)
197. ^ Natural language processing (NLP): Russell & Norvig (2021, chpt. 23–24), Poole, Mackworth & Goebel (1998, pp. 91–104), Luger & Stubblefield (2004, pp. 591–632)
198. ^ Subproblems of NLP: Russell & Norvig (2021, pp. 849–850)
199. ^ Russell & Norvig (2021), pp. 856–858.
200. ^ Dickson (2022).
201. ^ Modern statistical and deep learning approaches to NLP: Russell & Norvig (2021, chpt. 24), Cambria & White (2014)
202. ^ Vincent (2019).
203. ^ Russell & Norvig (2021), pp. 875–878.
204. ^ Bushwick (2023).
205. ^ Computer vision: Russell & Norvig (2021, chpt. 25), Nilsson (1998, chpt. 6)
206. ^ Russell & Norvig (2021), pp. 849–850.
207. ^ Russell & Norvig (2021), pp. 895–899.
208. ^ Russell & Norvig (2021), pp. 899–901.
209. ^ Challa et al. (2011).
210. ^ Russell & Norvig (2021), pp. 931–938.
211. ^ MIT AIL (2014).
212. ^ Affective computing: Thro (1993), Edelson (1991), Tao & Tan (2005), Scassellati (2002)
213. ^ Waddell (2018).
214. ^ Poria et al. (2017).
215. ^ Search algorithms: Russell & Norvig (2021, chpts. 3–5), Poole, Mackworth & Goebel (1998, pp. 113–163), Luger & Stubblefield (2004, pp. 79–164, 193–219), Nilsson (1998, chpts. 7–12)
216. ^ State space search: Russell & Norvig (2021, chpt. 3)
217. ^ Russell & Norvig (2021), sect. 11.2.
218. ^ Uninformed searches (breadth first search, depth-first search and general state space search): Russell & Norvig (2021, sect. 3.4), Poole, Mackworth & Goebel (1998, pp. 113–132), Luger & Stubblefield (2004, pp. 79–121), Nilsson (1998, chpt. 8)
219. ^ Heuristic or informed searches (e.g., greedy best first and A*): Russell & Norvig (2021, sect. 3.5), Poole, Mackworth & Goebel (1998, pp. 132–147), Poole & Mackworth (2017, sect. 3.6), Luger & Stubblefield (2004, pp. 133–150)
220. ^ Adversarial search: Russell & Norvig (2021, chpt. 5)
221. ^ Local or “optimization” search: Russell & Norvig (2021, chpt. 4)
222. ^ Singh Chauhan, Nagesh (18 December 2020). “Optimization Algorithms in Neural Networks”. KDnuggets. Retrieved 13 January 2024.
223. ^ Evolutionary computation: Russell & Norvig (2021, sect. 4.1.2)
224. ^ Merkle & Middendorf (2013).
225. ^ Logic: Russell & Norvig (2021, chpts. 6–9), Luger & Stubblefield (2004, pp. 35–77), Nilsson (1998, chpt. 13–16)
226. ^ Propositional logic: Russell & Norvig (2021, chpt. 6), Luger & Stubblefield (2004, pp. 45–50), Nilsson (1998, chpt. 13)
227. ^ First-order logic and features such as equality: Russell & Norvig (2021, chpt. 7), Poole, Mackworth & Goebel (1998, pp. 268–275), Luger & Stubblefield (2004, pp. 50–62), Nilsson (1998, chpt. 15)
228. ^ Logical inference: Russell & Norvig (2021, chpt. 10)
229. ^ logical deduction as search: Russell & Norvig (2021, sects. 9.3, 9.4), Poole, Mackworth & Goebel (1998, pp. ~46–52), Luger & Stubblefield (2004, pp. 62–73), Nilsson (1998, chpt. 4.2, 7.2)
230. ^ Resolution and unification: Russell & Norvig (2021, sections 7.5.2, 9.2, 9.5)
231. ^ Warren, D.H.; Pereira, L.M.; Pereira, F. (1977). “Prolog-the language and its implementation compared with Lisp”. ACM SIGPLAN Notices. 12 (8): 109–115. doi:10.1145/872734.806939.
232. ^ Fuzzy logic: Russell & Norvig (2021, pp. 214, 255, 459), Scientific American (1999)
233. ^ Jump up to:^a b Stochastic methods for uncertain reasoning: Russell & Norvig (2021, chpt. 12–18, 20), Poole, Mackworth & Goebel (1998, pp. 345–395), Luger & Stubblefield (2004, pp. 165–191, 333–381), Nilsson (1998, chpt. 19)
234. ^ decision theory and decision analysis: Russell & Norvig (2021, chpt. 16–18), Poole, Mackworth & Goebel (1998, pp. 381–394)
235. ^ Information value theory: Russell & Norvig (2021, sect. 16.6)
236. ^ Markov decision processes and dynamic decision networks: Russell & Norvig (2021, chpt. 17)
237. ^ Jump up to:^a b c Stochastic temporal models: Russell & Norvig (2021, chpt. 14) Hidden Markov model: Russell & Norvig (2021, sect. 14.3) Kalman filters: Russell & Norvig (2021, sect. 14.4) Dynamic Bayesian networks: Russell & Norvig (2021, sect. 14.5)
238. ^ Game theory and mechanism design: Russell & Norvig (2021, chpt. 18)
239. ^ Bayesian networks: Russell & Norvig (2021, sects. 12.5–12.6, 13.4–13.5, 14.3–14.5, 16.5, 20.2–20.3), Poole, Mackworth & Goebel (1998, pp. 361–381), Luger & Stubblefield (2004, pp. ~182–190, ≈363–379), Nilsson (1998, chpt. 19.3–19.4)
240. ^ Domingos (2015), chpt. 6.
241. ^ Bayesian inference algorithm: Russell & Norvig (2021, sect. 13.3–13.5), Poole, Mackworth & Goebel (1998, pp. 361–381), Luger & Stubblefield (2004, pp. ~363–379), Nilsson (1998, chpt. 19.4 & 7)
242. ^ Domingos (2015), p. 210.
243. ^ Bayesian learning and the expectation–maximization algorithm: Russell & Norvig (2021, chpt. 20), Poole, Mackworth & Goebel (1998, pp. 424–433), Nilsson (1998, chpt. 20), Domingos (2015, p. 210)
244. ^ Bayesian decision theory and Bayesian decision networks: Russell & Norvig (2021, sect. 16.5)
245. ^ Statistical learning methods and classifiers: Russell & Norvig (2021, chpt. 20),
246. ^ Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. Intellisemantic Editions. ISBN 978-8-8947-8760-3.
247. ^ Decision trees: Russell & Norvig (2021, sect. 19.3), Domingos (2015, p. 88)
248. ^Non-parameteric learning models such as K-nearest neighbor and support vector machines: Russell & Norvig (2021, sect. 19.7), Domingos (2015, p. 187) (k-nearest neighbor)
     • Domingos (2015, p. 88) (kernel methods)
249. ^ Domingos (2015), p. 152.
250. ^ Naive Bayes classifier: Russell & Norvig (2021, sect. 12.6), Domingos (2015, p. 152)
251. ^ Jump up to:^a b Neural networks: Russell & Norvig (2021, chpt. 21), Domingos (2015, Chapter 4)
252. ^ Gradient calculation in computational graphs, backpropagation, automatic differentiation: Russell & Norvig (2021, sect. 21.2), Luger & Stubblefield (2004, pp. 467–474), Nilsson (1998, chpt. 3.3)
253. ^ Universal approximation theorem: Russell & Norvig (2021, p. 752) The theorem: Cybenko (1988), Hornik, Stinchcombe & White (1989)
254. ^ Feedforward neural networks: Russell & Norvig (2021, sect. 21.1)
255. ^ Recurrent neural networks: Russell & Norvig (2021, sect. 21.6)
256. ^ Perceptrons: Russell & Norvig (2021, pp. 21, 22, 683, 22)
257. ^ Jump up to:^a b Deep learning: Russell & Norvig (2021, chpt. 21), Goodfellow, Bengio & Courville (2016), Hinton et al. (2016), Schmidhuber (2015)
258. ^ Convolutional neural networks: Russell & Norvig (2021, sect. 21.3)
259. ^ Deng & Yu (2014), pp. 199–200.
260. ^ Ciresan, Meier & Schmidhuber (2012).
261. ^ Russell & Norvig (2021), p. 751.
262. ^ Jump up to:^a b c Russell & Norvig (2021), p. 17.
263. ^ Jump up to:^{a b c d e f g} Russell & Norvig (2021), p. 785.
264. ^ Jump up to:^a b Schmidhuber (2022), sect. 5.
265. ^ Schmidhuber (2022), sect. 6.
266. ^ Jump up to:^a b c Schmidhuber (2022), sect. 7.
267. ^ Schmidhuber (2022), sect. 8.
268. ^ Quoted in Christian (2020, p. 22)
269. ^ Smith (2023).
270. ^ “Explained: Generative AI”. 9 November 2023.
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272. ^ Marmouyet (2023).
273. ^ Kobielus (2019).
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275. ^ Wodecki, Ben (5 May 2023). “7 AI Programming Languages You Need to Know”. AI Business. Archived from the original on 25 July 2024. Retrieved 5 October 2024.
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277. ^ Davenport, T; Kalakota, R (June 2019). “The potential for artificial intelligence in healthcare”. Future Healthc J. 6 (2): 94–98. doi:10.7861/futurehosp.6-2-94. PMC 6616181. PMID 31363513.
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280. ^ Jump up to:^a b Bax, Monique; Thorpe, Jordan; Romanov, Valentin (December 2023). “The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence”. Frontiers in Sensors. 4. doi:10.3389/fsens.2023.1294721. ISSN 2673-5067.
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460. ^ Jump up to:^a b Turing’s original publication of the Turing test in “Computing machinery and intelligence“: Turing (1950) Historical influence and philosophical implications: Haugeland (1985, pp. 6–9), Crevier (1993, p. 24), McCorduck (2004, pp. 70–71), Russell & Norvig (2021, pp. 2, 984)
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463. ^ Russell & Norvig (2003), p. 18.
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465. ^ Simon (1965, p. 96) quoted in Crevier (1993, p. 109)
466. ^ Minsky (1967, p. 2) quoted in Crevier (1993, p. 109)
467. ^ Russell & Norvig (2021), p. 21.
468. ^ Lighthill (1973).
469. ^ NRC 1999, pp. 212–213.
470. ^ Russell & Norvig (2021), p. 22.
471. ^ Expert systems: Russell & Norvig (2021, pp. 23, 292), Luger & Stubblefield (2004, pp. 227–331), Nilsson (1998, chpt. 17.4), McCorduck (2004, pp. 327–335, 434–435), Crevier (1993, pp. 145–162, 197–203), Newquist (1994, pp. 155–183)
472. ^ Russell & Norvig (2021), p. 24.
473. ^ Nilsson (1998), p. 7.
474. ^ McCorduck (2004), pp. 454–462.
475. ^ Moravec (1988).
476. ^ Jump up to:^a b Brooks (1990).
477. ^ Developmental robotics: Weng et al. (2001), Lungarella et al. (2003), Asada et al. (2009), Oudeyer (2010)
478. ^ Russell & Norvig (2021), p. 25.
479. ^ Crevier (1993, pp. 214–215), Russell & Norvig (2021, pp. 24, 26)
480. ^ Russell & Norvig (2021), p. 26.
481. ^ Formal and narrow methods adopted in the 1990s: Russell & Norvig (2021, pp. 24–26), McCorduck (2004, pp. 486–487)
482. ^ AI widely used in the late 1990s: Kurzweil (2005, p. 265), NRC (1999, pp. 216–222), Newquist (1994, pp. 189–201)
483. ^ Wong (2023).
484. ^ Moore’s Law and AI: Russell & Norvig (2021, pp. 14, 27)
485. ^ Jump up to:^a b c Clark (2015b).
486. ^ Big data: Russell & Norvig (2021, p. 26)
487. ^ Sagar, Ram (3 June 2020). “OpenAI Releases GPT-3, The Largest Model So Far”. Analytics India Magazine. Archived from the original on 4 August 2020. Retrieved 15 March 2023.
488. ^ DiFeliciantonio (2023).
489. ^ Goswami (2023).
490. ^ Grayling, Anthony; Ball, Brian (1 August 2024). “Philosophy is crucial in the age of AI”. The Conversation. Archived from the original on 5 October 2024. Retrieved 4 October 2024.
491. ^ Jump up to:^a b Jarow, Oshan (15 June 2024). “Will AI ever become conscious? It depends on how you think about biology”. Vox. Archived from the original on 21 September 2024. Retrieved 4 October 2024.
492. ^ McCarthy, John. “The Philosophy of AI and the AI of Philosophy”. jmc.stanford.edu. Archived from the original on 23 October 2018. Retrieved 3 October 2024.
493. ^ Jump up to:^a b Turing (1950), p. 1.
494. ^ Turing (1950), Under “The Argument from Consciousness”.
495. ^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). “AI is closer than ever to passing the Turing test for ‘intelligence’. What happens when it does?”. The Conversation. Archived from the original on 25 September 2024. Retrieved 17 August 2024.
496. ^ Russell & Norvig (2021), p. 3.
497. ^ Maker (2006).
498. ^ McCarthy (1999).
499. ^ Minsky (1986).
500. ^ “What Is Artificial Intelligence (AI)?”. Google Cloud Platform. Archived from the original on 31 July 2023. Retrieved 16 October 2023.
501. ^ “One of the Biggest Problems in Regulating AI Is Agreeing on a Definition”. carnegieendowment.org. Retrieved 31 July 2024.
502. ^ “AI or BS? How to tell if a marketing tool really uses artificial intelligence”. The Drum. Retrieved 31 July 2024.
503. ^ Nilsson (1983), p. 10.
504. ^ Haugeland (1985), pp. 112–117.
505. ^ Physical symbol system hypothesis: Newell & Simon (1976, p. 116) Historical significance: McCorduck (2004, p. 153), Russell & Norvig (2021, p. 19)
506. ^ Moravec’s paradox: Moravec (1988, pp. 15–16), Minsky (1986, p. 29), Pinker (2007, pp. 190–191)
507. ^ Dreyfus’ critique of AI: Dreyfus (1972), Dreyfus & Dreyfus (1986) Historical significance and philosophical implications: Crevier (1993, pp. 120–132), McCorduck (2004, pp. 211–239), Russell & Norvig (2021, pp. 981–982), Fearn (2007, chpt. 3)
508. ^ Crevier (1993), p. 125.
509. ^ Langley (2011).
510. ^ Katz (2012).
511. ^ Neats vs. scruffies, the historic debate: McCorduck (2004, pp. 421–424, 486–489), Crevier (1993, p. 168), Nilsson (1983, pp. 10–11), Russell & Norvig (2021, p. 24) A classic example of the “scruffy” approach to intelligence: Minsky (1986) A modern example of neat AI and its aspirations in the 21st century: Domingos (2015)
512. ^ Pennachin & Goertzel (2007).
513. ^ Jump up to:^a b Roberts (2016).
514. ^ Russell & Norvig (2021), p. 986.
515. ^ Chalmers (1995).
516. ^ Dennett (1991).
517. ^ Horst (2005).
518. ^ Searle (1999).
519. ^ Searle (1980), p. 1.
520. ^ Russell & Norvig (2021), p. 9817.
521. ^ Searle’s Chinese room argument: Searle (1980). Searle’s original presentation of the thought experiment., Searle (1999). Discussion: Russell & Norvig (2021, pp. 985), McCorduck (2004, pp. 443–445), Crevier (1993, pp. 269–271)
522. ^ Leith, Sam (7 July 2022). “Nick Bostrom: How can we be certain a machine isn’t conscious?”. The Spectator. Archived from the original on 26 September 2024. Retrieved 23 February 2024.
523. ^ Jump up to:^a b c Thomson, Jonny (31 October 2022). “Why don’t robots have rights?”. Big Think. Archived from the original on 13 September 2024. Retrieved 23 February 2024.
524. ^ Jump up to:^a b Kateman, Brian (24 July 2023). “AI Should Be Terrified of Humans”. Time. Archived from the original on 25 September 2024. Retrieved 23 February 2024.
525. ^ Wong, Jeff (10 July 2023). “What leaders need to know about robot rights”. Fast Company.
526. ^ Hern, Alex (12 January 2017). “Give robots ‘personhood’ status, EU committee argues”. The Guardian. ISSN 0261-3077. Archived from the original on 5 October 2024. Retrieved 23 February 2024.
527. ^ Dovey, Dana (14 April 2018). “Experts Don’t Think Robots Should Have Rights”. Newsweek. Archived from the original on 5 October 2024. Retrieved 23 February 2024.
528. ^ Cuddy, Alice (13 April 2018). “Robot rights violate human rights, experts warn EU”. euronews. Archived from the original on 19 September 2024. Retrieved 23 February 2024.
529. ^ The Intelligence explosion and technological singularity: Russell & Norvig (2021, pp. 1004–1005), Omohundro (2008), Kurzweil (2005) I. J. Good‘s “intelligence explosion”: Good (1965) Vernor Vinge‘s “singularity”: Vinge (1993)
530. ^ Russell & Norvig (2021), p. 1005.
531. ^ Transhumanism: Moravec (1988), Kurzweil (2005), Russell & Norvig (2021, p. 1005)
532. ^ AI as evolution: Edward Fredkin is quoted in McCorduck (2004, p. 401), Butler (1863), Dyson (1998)
533. ^ AI in myth: McCorduck (2004, pp. 4–5)
534. ^ McCorduck (2004), pp. 340–400.
535. ^ Buttazzo (2001).
536. ^ Anderson (2008).
537. ^ McCauley (2007).
538. ^ Galvan (1997).