|
|
认知鸿沟？人工智能该何去何从
|
Abstract:
人工智能是新一轮科技革命和产业变革的重要驱动力量。然而，人工智能在解决问题中的失误带来的不利后果常常是人类无力招架的。如何优化人工智能的问题解决能力对于其发展至关重要。因此在前人积累和理论分析基础上，拟从脑科学、心理学研究成果探析生物智能与人工智能的认知差别。认知方式的差异意味着个体对于解决问题的方式不同，因而人类认知方式是人工智能突破认知鸿沟实现高效正确解决问题的有效途径。本文总结了脑科学、心理学研究成果促使人工智能算法更新的历史，归纳了顿悟对人工智能算法进步的指导意义，并提出将顿悟体验纳入人工智能算法模型更迭参考。本文对于探析人工智能认知进步及其算法更新具有较好现实意义。
Artificial intelligence is an important driving force for a new round of scientific and technological revolution and industrial transformation. However, AI’s mistakes in solving problems often have adverse consequences that humans are unable to cope with. How to optimize the problem-solving ability of AI is crucial to its development. Therefore, on the basis of previous accumulation and theoretical analysis, this paper intends to explore the cognitive differences between biological intelligence and artificial intelligence from the research results of brain science and psychology. The difference of cognitive style means that individuals have different ways to solve problems. Therefore, human cognitive style is an effective way for artificial intelligence to break through the cognitive gap and realize efficient and correct problem solving. This paper summarizes the history of brain science and psychology research results to promote the updating of artificial intelligence algorithms, summarizes the guiding significance of insight to the progress of artificial intelligence algorithms, and proposes to incorporate insight experience into the reference of artificial intelligence algorithm model. This paper is of great practical significance for exploring the cognitive progress of artificial intelligence and its algorithm updating.
| [1] | 陈先昌(2014). 基于卷积神经网络的深度学习算法与应用研究. 硕士学位论文, 杭州: 浙江工商大学. |
| [2] | 傅小兰(1989). 思惟的信息加工理论和计算机模拟方法. 心理学动态, (2), 16-20, 25. |
| [3] | 林家宝, 崔璇, 戴晓颖, 陈雅珏, 莫雷(2018). 静息状态的大脑信号可预测顿悟. 见 第二十一届全国心理学学术会议摘要集(pp. 456-457). 华南师范大学心理学院. |
| [4] | 刘勇, 胡学敏, 陈卓(2024). 基于心理学理论的多策略生活选择算法. 智能计算机与应用, 14(5), 10-18. |
| [5] | 沈汪兵, 刘昌, 袁媛, 张小将, 罗劲(2013). 顿悟类问题解决中思维僵局的动态时间特性. 中国科学: 生命科学, 43(3), 254-262. |
| [6] | 谭钢, 侯玉玲, 曹智鹏(2013). 遥远距离联想任务脑机制的ERP研究. 心理与行为研究, 11(6), 746-751. |
| [7] | 邢强, 孙海龙, 占丹玲, 胡婧, 刘凯(2017). 执行功能对言语顿悟问题解决的影响: 基于行为与ERPs的研究. 心理学报, 49(7), 909-919. |
| [8] | 张旭(2024). 智能时代的脑科学与类脑智能研究. 中国科学院院刊, 39(5), 840-850. |
| [9] | 张雨婷, 刘勇(2022). 融入教育心理学的SBO算法. 计算机应用研究, 39(9), 2631-2639. |
| [10] | 张桂梅, 刘丕玉(2013). 基于格式塔心理学和Euler spiral的轮廓修复算法. 计算机应用研究, 30(8), 2534-2537, 2560. |
| [11] | 朱毅鑫, 高涛, 范丽凤, 黄思远, 等(2020). “暗”, 不止于“深”——迈向认知智能与类人常识的范式转换. Engineering, 6(3), 230-306. |
| [12] | Abdolrasol, M. G. M., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R. et al. (2021). Artificial Neural Networks Based Optimization Techniques: A Review. Electronics, 10, Article 2689. https://doi.org/10.3390/electronics10212689 |
| [13] | Aitken, M., Toreini, E., Carmichael, P., Coopamootoo, K., Elliott, K., & van Moorsel, A. (2020). Establishing a Social Licence for Financial Technology: Reflections on the Role of the Private Sector in Pursuing Ethical Data Practices. Big Data & Society, 7. https://doi.org/10.1177/2053951720908892 |
| [14] | Baddeley, A. (1981). The Concept of Working Memory: A View of Its Current State and Probable Future Development. Cognition, 10, 17-23. https://doi.org/10.1016/0010-0277(81)90020-2 |
| [15] | Cooper, S. J. (2005). Donald O. Hebb’s Synapse and Learning Rule: A History and Commentary. Neuroscience & Biobehavioral Reviews, 28, 851-874. https://doi.org/10.1016/j.neubiorev.2004.09.009 |
| [16] | Danek, A. H., & Salvi, C. (2020). Moment of Truth: Why Aha! Experiences Are Correct. The Journal of Creative Behavior, 54, 484-486. https://doi.org/10.1002/jocb.380 |
| [17] | Gugerty, L. (2006). Newell and Simon’s Logic Theorist: Historical Background and Impact on Cognitive Modeling. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50, 880-884. https://doi.org/10.1177/154193120605000904 |
| [18] | Hubel, D. H., & Wiesel, T. N. (1962). Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex. The Journal of Physiology, 160, 106-154. https://doi.org/10.1113/jphysiol.1962.sp006837 |
| [19] | Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R. et al. (2004). Neural Activity When People Solve Verbal Problems with Insight. PLoS Biology, 2, e97. https://doi.org/10.1371/journal.pbio.0020097 |
| [20] | Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A. et al. (2017). Overcoming Catastrophic Forgetting in Neural Networks. Proceedings of the National Academy of Sciences, 114, 3521-3526. https://doi.org/10.1073/pnas.1611835114 |
| [21] | Luo, J., & Knoblich, G. (2007). Studying Insight Problem Solving with Neuroscientific Methods. Methods, 42, 77-86. https://doi.org/10.1016/j.ymeth.2006.12.005 |
| [22] | Miller, G. A. (2003). The Cognitive Revolution: A Historical Perspective. Trends in Cognitive Sciences, 7, 141-144. https://doi.org/10.1016/s1364-6613(03)00029-9 |
| [23] | Muzaffar, S., & Afshari, A. (2019). Short-Term Load Forecasts Using LSTM Networks. Energy Procedia, 158, 2922-2927. https://doi.org/10.1016/j.egypro.2019.01.952 |
| [24] | Ohlsson, S. (2011). Deep Learning: How the Mind Overrides Experience. Cambridge University Press. https://doi.org/10.1017/cbo9780511780295 |
| [25] | Sabharwal, A., & Selman, B. (2011). S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition. Artificial Intelligence, 175, 935-937. https://doi.org/10.1016/j.artint.2011.01.005 |
| [26] | Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. P. (2016). One-Shot Learning with Memory-Augmented Neural Networks. arXiv:1605.06065. https://doi.org/10.48550/arXiv.1605.06065 |
| [27] | Scholl, B. J., & Nakayama, K. (2004). Illusory Causal Crescents: Misperceived Spatial Relations Due to Perceived Causality. Perception, 33, 455-469. https://doi.org/10.1068/p5172 |
| [28] | Scolari, M., Seidl-Rathkopf, K. N., & Kastner, S. (2015). Functions of the Human Frontoparietal Attention Network: Evidence from Neuroimaging. Current Opinion in Behavioral Sciences, 1, 32-39. https://doi.org/10.1016/j.cobeha.2014.08.003 |
