Artificial Intelligence in the 21ˢᵗ Century
Main Article Content
Abstract
Artificial intelligence (AI) is the most important and interesting technology in the 21st Century due to its vast application. This review focuses on the evolution of AI techniques and their applications in recent decades. Deep learning algorithms/models, represented by Large Language Models (LLMs) have resulted in groundbreaking advancements, indicating that AI is evolving to improve its capacity to interact with and help people in various fields such as finance, medicine, and science research. The potential for research in AI is immense, and there is a need for scientific principles behind AI. Future perspectives on how machines can be developed to work with humans and to be compatible with human values and preferences are also discussed.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
- Legg S, Hutter M. A collection of definitions of intelligence. In B. Goertzel & P. Wang (Eds.), Advances in artificial general intelligence: Concepts, architectures, and algorithms vol 157 of frontiers in artificial intelligence and applications. (pp. 17–24). Amsterdam, NL: IOS Press, 2007.
- Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015, 349(6245): 255-260. https://doi.org/10.1126/science.aaa8415
- Kersting K. Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines. Frontiers in Big Data. 2018, 1. https://doi.org/10.3389/fdata.2018.00006
- Raj R, Kos A. Different Techniques for Human Activity Recognition. 2022 29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES). Published online June 23, 2022. https://doi.org/10.23919/mixdes55591.2022.9838050
- Dhall D, Kaur R, Juneja M. Machine Learning: A Review of the Algorithms and Its Applications. Proceedings of ICRIC 2019. Published online November 22, 2019: 47-63. https://doi.org/10.1007/978-3-030-29407-6_5
- Rives A, Meier J, Sercu T, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences. 2021, 118(15). https://doi.org/10.1073/pnas.2016239118
- Littman ML. Reinforcement learning improves behaviour from evaluative feedback. Nature. 2015, 521(7553): 445-451. https://doi.org/10.1038/nature14540
- Filho BDBF, Cabral ELL, Soares AJ. A new approach to artificial neural networks. IEEE Transactions on Neural Networks. 1998, 9(6): 1167-1179. https://doi.org/10.1109/72.728360
- Li Z, Liu F, Yang W, et al. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 2021.
- Townshend RJL, Eismann S, Watkins AM, et al. Geometric deep learning of RNA structure. Science. 2021, 373(6558): 1047-1051. https://doi.org/10.1126/science.abe5650
- Chen D, Bai Y, Ament S, et al. Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. Nature Machine Intelligence. 2021, 3(9): 812-822. https://doi.org/10.1038/s42256-021-00384-1
- Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019, 571(7763): 95-98. https://doi.org/10.1038/s41586-019-1335-8
- Reed CJ, Metzger S, Srinivas A, et al. SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2021. https://doi.org/10.1109/cvpr46437.2021.00270
- Shu-Hsien Liao. Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Systems with Applications. 2005, 28(1): 93-103. https://doi.org/10.1016/j.eswa.2004.08.003
- Levesque HJ. Knowledge Representation and Reasoning. Annual Review of Computer Science. 1986, 1(1): 255-287. https://doi.org/10.1146/annurev.cs.01.060186.001351
- Barbey AK, Karama S, Haier RJ, et al. The Cambridge Handbook of Intelligence and Cognitive Neuroscience. Cambridge University Press, 2021. https://doi.org/10.1017/9781108635462
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015, 521(7553): 436-444. https://doi.org/10.1038/nature14539
- Tong W, Hussain A, Bo WX, et al. Artificial Intelligence for Vehicle-to-Everything: A Survey. IEEE Access. 2019, 7: 10823-10843. https://doi.org/10.1109/access.2019.2891073
- Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv: 2108.07258, 2021. https://doi.org/10.48550/arXiv.2108.07258
- Yuan L, Chen D, Codella N, et al. Florence: A new foundation model for computer vision. arXiv preprint arXiv: 2111.11432, 2021. https://doi.org/10.48550/arXiv.2111.11432
- Singh A, Hu R, Goswami V, et al. FLAVA: A Foundational Language And Vision Alignment Model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2022. https://doi.org/10.1109/cvpr52688.2022.01519
- Bubeck S, Chandrasekaran V, Eldan R, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv: 2303.12712, 2023. https://doi.org/10.48550/arXiv.2303.12712
- Shen Y, Song K, Tian X, et al. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems. 2024, 36.
- Zhong W, Cui R, Guo Y, et al. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv: 2304.06364, 2023. https://doi.org/10.48550/arXiv.2304.06364
- Zhang Z, Zhang A, Li M, et al. Automatic chain of thought prompting in large language models. arXiv preprint arXiv: 2210.03493, 2022. https://doi.org/10.48550/arXiv.2210.03493
- Liu J, Xia CS, Wang Y, et al. Is Your Code Generated By ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation, NIPS, 2023.
- Lee C, Jin J, Kim T, et al. OWQ: Lessons learned from activation outliers for weight quantization in large language models. arXiv preprint arXiv: 2306.02272, 2023. https://doi.org/10.48550/arXiv.2306.02272
- Wang X, Tang X, Zhao X, et al. Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Published online 2023. https://doi.org/10.18653/v1/2023.emnlp-main.621
- Ratner N, Levine Y, Belinkov Y, et al. Parallel Context Windows for Large Language Models. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Published online 2023. https://doi.org/10.18653/v1/2023.acl-long.352
- Zhang D, Li S, Zhang X, et al. SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities. Findings of the Association for Computational Linguistics: EMNLP 2023. Published online 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.1055
- Wu C, Yin S, Qi W, et al. Visual chatgpt: Talking, drawing and editing with visual foundation models. arXiv preprint arXiv: 2303.04671, 2023. https://doi.org/10.48550/arXiv.2303.04671
- Taecharungroj V. “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing. 2023, 7(1): 35. https://doi.org/10.3390/bdcc7010035
- Zhang X, Wang L, Helwig J, et al. Artificial intelligence for science in quantum, atomistic, and continuum systems. arXiv preprint arXiv: 2307.08423, 2023. https://doi.org/10.48550/arXiv.2307.08423
- Berens P, Cranmer K, Lawrence ND, et al. AI for Science: An Emerging Agenda. arXiv preprint arXiv: 2303.04217, 2023. https://doi.org/10.48550/arXiv.2303.04217
- Wang H, Fu T, Du Y, et al. Scientific discovery in the age of artificial intelligence. Nature. 2023, 620(7972): 47-60. https://doi.org/10.1038/s41586-023-06221-2
- Kochkov D, Smith JA, Alieva A, et al. Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences. 2021, 118(21). https://doi.org/10.1073/pnas.2101784118
- Gao W, Raghavan P, Coley CW. Autonomous platforms for data-driven organic synthesis. Nature Communications. 2022, 13(1). https://doi.org/10.1038/s41467-022-28736-4
- Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021, 596(7873): 583-589. https://doi.org/10.1038/s41586-021-03819-2
- Liu G, Catacutan DB, Rathod K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chemical Biology. 2023, 19(11): 1342-1350. https://doi.org/10.1038/s41589-023-01349-8
- Zhao W, Zhao S, Li L, et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nature Biotechnology. 2021, 40(4): 606-617. https://doi.org/10.1038/s41587-021-01092-2
- Kench S, Cooper SJ. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nature Machine Intelligence. 2021, 3(4): 299-305. https://doi.org/10.1038/s42256-021-00322-1
- Wang Z, Chen A, Tao K, et al. MatGPT: A Vane of Materials Informatics from Past, Present, to Future. Advanced Materials. 2023, 36(6). https://doi.org/10.1002/adma.202306733
- Zhang M, Qamar M, Kang T, et al. A survey on graph diffusion models: Generative ai in science for molecule, protein and material. arXiv preprint arXiv: 2304.01565, 2023. https://doi.org/10.48550/arXiv.2304.01565
- Fecher B, Hebing M, Laufer M, et al. Friend or foe? Exploring the implications of large language models on the science system. AI & SOCIETY. Published online October 26, 2023. https://doi.org/10.1007/s00146-023-01791-1
- Erduran S. AI is transforming how science is done. Science education must reflect this change. Science. 2023, 382(6677). https://doi.org/10.1126/science.adm9788
- Azer SA, Guerrero APS. The challenges imposed by artificial intelligence: are we ready in medical education? BMC Medical Education. 2023, 23(1). https://doi.org/10.1186/s12909-023-04660-z
- Schäfer MS. The Notorious GPT: science communication in the age of artificial intelligence. Journal of Science Communication. 2023, 22(02). https://doi.org/10.22323/2.22020402
- Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature. 2017, 550(7676): 354-359. https://doi.org/10.1038/nature24270
- Silver D, Hubert T, Schrittwieser J, et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science. 2018, 362(6419): 1140-1144. https://doi.org/10.1126/science.aar6404
- Grzybowski A, Singhanetr P, Nanegrungsunk O, et al. Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment. Ophthalmology and Therapy. 2023, 12(3): 1419-1437. https://doi.org/10.1007/s40123-023-00691-3
- White RD, Demirer M, Gupta V, et al. Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions. Journal of Medical Imaging. 2022, 9(05). https://doi.org/10.1117/1.jmi.9.5.054504
- Castonguay A, Lovis C. Introducing the “AI Language Models in Health Care” Section: Actionable Strategies for Targeted and Wide-Scale Deployment (Preprint). Published online October 18, 2023. https://doi.org/10.2196/preprints.53785
- Subasri V, Krishnan A, Dhalla A, et al. Diagnosing and remediating harmful data shifts for the responsible deployment of clinical AI models. Published online March 29, 2023. https://doi.org/10.1101/2023.03.26.23286718
- Gao X. Role of 5G network technology and artificial intelligence for research and reform of English situational teaching in higher vocational colleges. Journal of Intelligent & Fuzzy Systems. 2021, 40(2): 3643-3654. https://doi.org/10.3233/JIFS-189399
- Xia J, Yan Y, Ji L. RETRACTED ARTICLE: Research on control strategy and policy optimal scheduling based on an improved genetic algorithm. Neural Computing and Applications. 2021, 34(12): 9485-9497. https://doi.org/10.1007/s00521-021-06415-7
- Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion. 2020, 58: 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
- Gunning D, Stefik M, Choi J, et al. XAI—Explainable artificial intelligence. Science Robotics. 2019, 4(37). https://doi.org/10.1126/scirobotics.aay7120
- Das A, Rad P. Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv: 2006.11371, 2020. https://doi.org/https://doi.org/10.48550/arXiv.2006.11371
- Liao QV, Gruen D, Miller S. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Published online April 21, 2020. https://doi.org/10.1145/3313831.3376590
- Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion. 2022, 77: 29-52. https://doi.org/10.1016/j.inffus.2021.07.016
- Chen Z, Silvestri F, Wang J, et al. The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Published online July 18, 2023. https://doi.org/10.1145/3539618.3592070
- Chuang YN, Wang G, Yang F, et al. Efficient xai techniques: A taxonomic survey. arXiv preprint arXiv: 2302.03225, 2023. https://doi.org/10.48550/arXiv.2302.03225
- Miller T. Explainable AI is Dead, Long Live Explainable AI! 2023 ACM Conference on Fairness, Accountability, and Transparency. Published online June 12, 2023. https://doi.org/10.1145/3593013.3594001