Abstract
Modern Artificial Intelligence (AI) is a rapidly evolving field that encompasses a range of techniques and approaches, including machine learning, deep learning, natural language processing, computer vision, robotics, and more. The development of AI technologies has enabled unprecedented levels of accuracy in tasks such as image and speech recognition, natural language understanding, and game playing. This has been made possible by the rise of deep learning, which involves training artificial neural networks on vast amounts of data to recognize patterns and make predictions with high accuracy. Other recent advances in modern AI include the development of generative models and reinforcement learning. Despite the significant progress made in modern AI, there are still many challenges that need to be addressed, including issues related to data privacy, fairness, and bias, and the need for more explainable AI systems that can provide clear and transparent reasoning for their decisions. This study provides an overview of modern AI and its applications, as well as the challenges and opportunities that lie ahead in this rapidly evolving field.
References
Theodotou, A., & Stassopoulou, A. (2015). A System for Automatic Classification of Twitter Messages into Categories. CONTEXT
Hill, J., Ford, W.R., & Farreras, I.G. (2015). Real conversations with artificial intelligence: A comparison between human-human online interviews and human-chatbot discussions. Comput. Hum. Behav., 49, 245-250
Shingte, Kshitija and Chaudhari, Anuja and Patil, Aditee and Chaudhari, Anushree and Desai, Sharmishta, Chatbot Development for Educational Institute (June 6, 2021). Available at SSRN: https://ssrn.com/abstract=3861241 or http://dx.doi.org/10.2139/ssrn.3861241
Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A.,Miklos, B., Corrado, G.S., Lukács, L., Ganea, M., Young, P., & Ramavajjala, V. (2016). Smart Reply: Automated Response Suggestion for Email. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Kaur, G.P., & Gurm, R.K. (2016). Email Spam Classification using Hybridized Technique with Feature Selection. International Journal of Advanced Research in Computer and Communication Engineering, 5, 260-266
Raminhos, R., Coutinho, E., Miranda, N., Barbas, M., Branco, P., Gonçalves, T., & Palma, G. (2016). SMART Mail - A SMART Platform for Mail Management. ICEIS.
https://www.sciencedirect.com/science/article/pii/S0933365723000192
https://emerj.com/ai-sector-overviews/artificial-intelligence-at-netflix/
Albu, A., & Stanciu, L. (2015). Benefits of using artificial intelligence in medical predictions. 2015 E-Health and Bioengineering Conference (EHB), 1-4…
Anand, N., Edwards, L., Baker, L. X., Chren, M. M. & Wheless, L. Validity of using billing codes from electronic health records to estimate skin cancer counts. JAMA Dermatol…
Pou-Prom C., Raimondo S., Rudzicz F. A Conversational Robot for Older Adults with Alzheimer’s Disease. ACM Trans. Human-Robot Interact. 2020;9:1–25. doi: 10.1145/3380785
Freeman, K. et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 368, m127 (2020).
Dutta, S. Data Modeling: A Fundamental Pillar of Your Future Ai Technology. CAS Blog. https://www.cas.org/resource/blog/data-modeling-fundamental-pillar-your-future-ai-technology
Sharma, Y. Data Quality: The Not-So Secret Sauce for Ai and Machine Learning. CAS Blog. https://www.cas.org/resource/blog/data-quality-not-so-secret-sauce-ai-and-machine-learning
http://icrobotics.co.uk/wiki/index.php/Light_Sensing_Robot
Hamamoto, Ryuji. "Application of artificial intelligence for medical research." Biomolecules 11, no. 1 (2021): 90.