Abstract: The new generation of artificial intelligence (AI) research initiated by Chinese scholars conforms to the needs of a new information environment changes, and strives to advance traditional artificial intelligence (AI 1.0) to a new stage of AI 2.0. As one of the important components of AI, collective intelligence (CI 1.0), i.‍e., swarm intelligence, is developing to the stage of CI 2.0 (crowd intelligence). Through in-depth analysis and informative argumentation, it is found that an incompatibility exists between CI 1.0 and CI 2.0. Therefore, CI 1.5 is introduced to build a bridge between the above two stages, which is based on bio-collaborative behavioral mimicry. CI 1.5 is the transition from CI 1.0 to CI 2.0, which contributes to the compatibility of the two stages. Then, a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given. The meta-synthesis of wisdom, as an improvement of crowd intelligence, is an advanced stage of bionic intelligence, i.‍e., CI 3.0. It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0, and some elaboration is made. As a result, we propose four development stages (CI 1.0, CI 1.5, CI 2.0, and CI 3.0), which form a complete framework for the development of CI. These different stages are progressively improved and have good compatibility. Due to the dominant role of cooperation in the development stages of CI, three types of cooperation in CI are discussed: indirect regulatory cooperation in lower organisms, direct communicative cooperation in higher organisms, and shared intention based collaboration in humans. Labor division is the main form of achieving cooperation and, for this reason, this paper investigates the relationship between the complexity of behavior and types of labor division. Finally, based on the overall understanding of the four development stages of CI, the future development direction and research issues of CI are explored.

群体智能的四个发展阶段

[1]An XM, Ma GH, Song G, 2018. Origins and evolution of meta-synthesis approach. Syst Eng, 36(10):1-13 (in Chinese).

[2]Askarzadeh A, 2016. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct, 169:1-12.

[3]Axelrod R, Hamilton WD, 1981. The evolution of cooperation. Science, 211(4489):1390-1396.

[4]Bernstein E, Shore J, Lazer D, 2018. How intermittent breaks in interaction improve collective intelligence. Proc Nat Acad Sci USA, 115(35):8734-8739.

[5]Bonabeau E, Dorigo M, Theraulaz G, 1999. Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, New York, USA.

[6]Cai W, Yang CY, 2013. Basic theory and methodology on extenics. Chin Sci Bull, 58(13):1190-1199 (in Chinese).

[7]Chen X, Xiao RB, 2023. A Computational Experimental Study of Rumor Propagation and Opinion Evolution. Huazhong University of Science & Technology Press, Wuhan, China (in Chinese).

[8]China Artificial Intelligence 2.0 Development Strategy Research Project Team, 2018. Strategic Research on Artificial Intelligence 2.0 in China (Volume I). Zhejiang University Press, Hangzhou, China (in Chinese).

[9]Dai RW, 2009. The proposal and recent development of metasynthetic method(M) from qualitative to quantitative. Chin J Nat, 31(6):311-314, 326 (in Chinese).

[10]Galesic M, Barkoczi D, Berdahl AM, et al., 2023. Beyond collective intelligence: collective adaptation. J Royal Soc Interf, 20(200):20220736.

[11]Grinnell J, McComb K, 2001. Roaring and social communication in African lions: the limitations imposed by listeners. Anim Behav, 62(1):93-98.

[12]Grinnell J, Packer C, Pusey AE, 1995. Cooperation in male lions: kinship, reciprocity or mutualism?Anim Behav, 49(1):95-105.

[13]Hare B, Call J, Tomasello M, 2001. Do chimpanzees know what conspecifics know?Anim Behav, 61(1):139-151.

[14]Hilbert M, López P, 2011. The world’s technological capacity to store, communicate, and compute information. Science, 332(6025):60-65.

[15]Hills TT, Todd PM, Lazer D, et al., 2015. Exploration versus exploitation in space, mind, and society. Trends Cogn Sci, 19(1):46-54.

[16]Jiang XY, Li S, 2018. BAS: beetle antennae search algorithm for optimization problems. Int J Robot Contr, 1(1):1-5.

[17]Karsai I, 1999. Decentralized control of construction behavior in paper wasps: an overview of the stigmergy approach. Artif Life, 5(2):117-136.

[18]Kennedy J, Eberhart RC, Shi YH, 2001. Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, USA.

[19]Kshetri N, Dwivedi YK, Davenport TH, et al., 2024. Generative artificial intelligence in marketing: applications, opportunities, challenges, and research agenda. Int J Inform Manag, 75:102716.

[20]Li SY, Li Y, Lin YM, 2019. Intelligent Optimization Algorithms and Emergent Computation. Tsinghua University Press, Beijing, China(in Chinese).

[21]Li W, Wu WJ, Wang HM, et al., 2017. Crowd intelligence in AI 2.0 era. Front Inform Technol Electron Eng, 18(1):15-43.

[22]Lin SJ, Dong C, Chen MZ, et al., 2018. Summary of new group intelligent optimization algorithms. Comput Eng Appl, 54(12):1-9 (in Chinese).

[23]Liu SJ, Yang Y, Zhou YQ, 2018. A swarm intelligence algorithm—lion swarm optimization. Patt Recogn Artif Intell, 31(5):431-441 (in Chinese).

[24]Melis AP, Hare B, Tomasello M, 2008. Do chimpanzees reciprocate received favours?Anim Behav, 76(3):951-962.

[25]Nick, 2017. A Brief History of Artificial Intelligence. Posts & Telecom Press, Beijing, China (in Chinese).

[26]Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[27]Passino KM, 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Contr Syst Mag, 22(3):52-67.

[28]Pei J, Deng L, Song S, et al., 2019. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 572(7767):106-111.

[29]Predic B, Stojanovic D, 2015. Enhancing driver situational awareness through crowd intelligence. Expert Syst Appl, 42(11):4892-4909.

[30]Qian XS, Yu JY, Dai RW, 1990. A new area of science—‍open complex giant system and its methodology. Chin J Nat, 13(1):3-10, 64 (in Chinese).

[31]Rajakumar BR, 2012. The lion’s algorithm: a new nature-inspired search algorithm. Proc Technol, 6:126-135.

[32]Reynolds CW, 1987. Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Graph, 21(4):25-34.

[33]Riedl C, Kim YJ, Gupta P, et al., 2021. Quantifying collective intelligence in human groups. Proc Nat Acad Sci USA, 118(21):e2005737118.

[34]Samuelson PA, Nordhaus WD, 2010. Economics (19^th Ed.). McGraw-Hill, New York, USA.

[35]Schaller GB, 1972. The Serengeti Lion: a Study of Predator-Prey Relations. University of Chicago Press, Chicago, USA.

[36]Senge PM, 1990. The Fifth Discipline: the Art and Practice of the Learning Organization. Doubleday/Currency, New York, USA.

[37]Stander PE, Stander J, 1988. Characteristics of lion roars in Etosha National Park. Madoqua, 1988(4):315-318.

[38]Stanton MCB, Roelich K, 2021. Decision making under deep uncertainties: a review of the applicability of methods in practice. Technol Forecast Soc Change, 171:120939.

[39]Wang B, Jin XP, Cheng B, 2012. Lion pride optimizer: an optimization algorithm inspired by lion pride behavior. Sci China Inform Sci, 55(10):2369-2389.

[40]Wang WH, 2007. Qian Xuesen’s Academic Thought. Sichuan Science and Technology Press, Chengdu, China(in Chinese).

[41]Wei J, Tay Y, Bommasani R, et al., 2022. Emergent abilities of large language models.

[42]Wu F, Lu CW, Zhu MJ, et al., 2020. Towards a new generation of artificial intelligence in China. Nat Mach Intell, 2(6):312-316.

[43]Wu HS, Xiao RB, 2020. Flexible wolf pack algorithm for dynamic multidimensional knapsack problems. Research, 2020:1762107.

[44]Wu HS, Xiao RB, 2021. A new approach to swarm intelligence: role-matching labor division of a wolf pack. CAAI Trans Intell Syst, 16(1):125-133 (in Chinese).

[45]Wu LF, Wang DS, Evans JA, 2019. Large teams develop and small teams disrupt science and technology. Nature, 566(7744):378-382.

[46]Xiao RB, 2013. Swarm Intelligence in Complex Systems. Science Press, Beijing, China(in Chinese).

[47]Xiao RB, Chen ZZ, 2023. From swarm intelligence optimization to swarm intelligence evolution. J Nanchang Inst Technol, 42(1):1-10 (in Chinese).

[48]Xiao RB, Hou JD, 2024. Running mechanism of the new national system‍—from the view of meta-synthesis approach and meta-synthesis of wisdom. Chin J Syst Sci, 32(2):‍7‍3-79, 85 (in Chinese).

[49]Xiao RB, Tao ZW, 2007. Research progress of swarm intelligence. J Manag Sci China, 10(3):80-96 (in Chinese).

[50]Xiao RB, Wang YC, 2019. Research progress of self-organized labor division in swarm intelligence. Inform Contr, 48(2):129-139, 148 (in Chinese).

[51]Xiao RB, Feng ZH, Wang JH, 2022. Collective intelligence: conception, research progresses and application analyses. J Nanchang Inst Technol, 41(1):1-21 (in Chinese).

[52]Xiao RB, Li G, Chen ZZ, 2023. Research progress and prospect of evolutionary many-objective optimization. Contr Dec, 38(7):1761-1788 (in Chinese).

[53]Xue JK, Shen B, 2020. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Contr Eng, 8(1):22-34.

[54]Yazdani M, Jolai F, 2016. Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng, 3(1):24-36.

[55]Zhang B, Zhu J, Su H, 2023. Toward the third generation artificial intelligence. Sci China Inform Sci, 66(2):121101.

[56]Zhang W, Mei H, 2020. A constructive model for collective intelligence. Nat Sci Rev, 7(8):1273-1277.

[57]Zheng ZM, Lv JH, Wei W, et al., 2021. Refined intelligence theory: artificial intelligence regarding complex dynamic objects. Sci Sin Inform, 51(4):678-690 (in Chinese).

[58]Zhong YX, 2018. Mechanism-based artificial intelligence theory: a universal theory of artifical intelligence. CAAI Trans Intell Syst, 13(1):2-18 (in Chinese).

[59]Zhou J, Ke P, Qiu X, et al., 2024. ChatGPT: potential, prospects, and limitations. Front Inform Technol Electron Eng, 25(1):6-11.