Dinesh Babu has played critical roles in UNESCO initiatives such as the UNESCO Media Alliance and the UNESCO MIL Cities Project.

He is the AI4 Media Global Research Association Coordinator for AI-based media research. His research interests range from mass communication and journalism to animation studies, environmental and health communication, tribal studies, new media, and development communication. 

INDIAai interviewed Dinesh to get his perspective on AI.

Tell us about the ongoing AI/ML and data science research at Amrita Vishwa Vidyapeetham.

Researchers in our Signal and Image Processing Group at Amrita Vishwa Vidyapeetham are working on innovative algorithms for enhancing image recognition and signal processing. The Bioinformatics and System Biology group is engaged in projects related to genomics, proteomics, and personalized medicine through AI-driven approaches. Our Speech and Language group is dedicated to advancing conversational AI and natural language understanding. The Applied Energy, Mechanics, and Materials group is researching AI applications in energy efficiency, material design, and predictive maintenance in mechanical systems. Lastly, the Geospatial Intelligence and Disaster Management group is focused on harnessing AI for disaster prediction, real-time monitoring, and effective response strategies. These ongoing research endeavours underscore our commitment to the forefront of AI/ML and data science advancement. As a Media faculty at Amrita Vishwa Vidyapeetham, we focus on AI for media and practical applications for big data journalism. We encourage students to use AI to identify fake news through Media Literacy Campaigns incorporating AI. 

What, in your opinion, are the significant challenges to data science and AI education in India?

Despite India's exponential advancements in AI and machine learning, the country needs help to achieve a successful data-driven future. Our primary challenge in India is a need for standardized curriculum and accreditation. Even universities and colleges offering AI and machine learning courses need more funding and experience. Skilled educators and specialists with industrial expertise can aid quality education. It contributes to the closing of the gap between industry and academia. These challenges could be solved by developing a unified curriculum in AI/ML with appropriate room for adaptation to field updates. Providing materials that aid in practical learning sessions for students to succeed in the competitive and dynamic AI/ML business is critical. 

What can the government do to encourage more students to pursue AI education?

In the global tech landscape, modern-day courses like AI and ML are vital for a country's growth and economy. Children in elementary school could be taught the fundamentals of AI and machine learning. It allows them to become acquainted with the expanding domains of artificial intelligence in the global scene and build fundamental abilities. Financial aid in the form of scholarships might be granted to students with exceptional backgrounds to pursue such courses, making them available to everybody without any inequities. A Centre for Excellence might be formed at the college and university levels to provide courses ranging from the fundamentals to the advanced level, hands-on training, and knowledge of industrial requirements. Centres of excellence can serve as research hubs while also collaborating with industries. Platforms like MOOC and online courses launch AI/ML courses from the beginner level and short-term courses at an advanced level for those who need to upskill in the field. 

What are the most important emerging trends in AI today, in your opinion?

As a new field, AI is being applied to various industries. AI's extraordinary intervention in health care may be demonstrated in medical image analysis, drug discovery, patient care, and enhancing health care systems. Robotic surgery, healthcare fraud detection, and remote diagnostics have all been embraced by many institutions. Natural Language Processing employs AI for chatbot development, sentiment analysis, and language translation. AI chatbots are increasingly being utilized to improve customer support. The use of AI in education allows for more personalized learning strategies. It serves as a platform for fraud detection and financial investment in trading and finance. It is also used in agriculture for precision farming and crop monitoring. Climate monitoring and forecasting benefit more than just agricultural enterprises. 

You have more than two decades of academic experience; what is the one aspect that institutions must change among students?

Educational institutions are responsible for grooming new-age AI professionals. They have to incorporate ethical considerations into AI curricula. Ethical considerations impart a sense of moral awareness, fairness, transparency, accountability, and privacy. They should critically evaluate and understand the potential consequences of their work and foster a sense of responsibility in society. Understanding the strategies for making informed, ethically sound decisions when faced with complex and conflicting considerations is required. Familiarising with the ethical code of conduct in the workplace and research community is expected. Professionalism in the AI work atmosphere is limited to technical proficiency and ethical responsibility. 

How do you see AI and data science developing in the industry today? What are the most significant contemporary trends you see emerging worldwide in AI?

AI and data science are both topics that are rapidly emerging and developing. One of the most intriguing advancements in AI involves its application in healthcare. AI is widely employed in medical diagnostics, and machine learning models assist doctors in developing accurate diagnoses and disease outcomes. 

AI has made substantial contributions to the financial industry's development by easing the installation of application switches and assisting in fraud detection, customer service, and investment advice. To give a structured and personalized learning experience in education, AI will be useful in creating intelligent tutoring solutions that target individual student needs. Integrating AI and Cliamte changer will be useful in improvising weather forecasting, optimizing energy consumption, and monitoring environmental changes. Additionally, ethical considerations, transparency, and regulation also play a significant role in shaping the future of AI. 

What advice do you have for people interested in AI research?

AI demands an in-depth understanding of the fundamental concepts of Computer science, mathematics and statistics. Programming languages like Python and R are essential for building excellence in AI. Mathematical concepts of linear algebra, calculus, and statistics help us understand AI algorithms. To build a basic foundation in the field, depend on online educational resources. Work on open-source projects of AI to gain first-hand practical experience. AI is a dynamic and rapidly evolving field. Hence, it is essential to keep updated about the emerging trends in the field. 

Please provide me with a list of important AI research articles and books.

Books

• Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom - Explores the potential implications of superintelligent AI and the associated risks.
• "Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky - Provides a comprehensive introduction to AI techniques.
• "Pattern Recognition and Machine Learning" by Christopher M. Bishop - A book focused on machine learning and pattern recognition techniques.
• Human Compatible: Artificial Intelligence and the Problem of Control" by Stuart Russell - Discusses the challenges of aligning AI with human values.
• "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" by George F. Luger - A classic textbook on AI problem-solving methods.

Articles

Ilić, M., Mikić, V., Kopanja, L. et al. Intelligent techniques in e-learning: a literature review. Artif Intell Rev 56, 14907–14953 (2023). https://doi.org/10.1007/s10462-023-10508-1

Ma, H., Zhang, Y., Sun, S. et al. A comprehensive survey on NSGA-II for multi-objective optimization and applications. Artif Intell Rev 56, 15217–15270 (2023). https://doi.org/10.1007/s10462-023-10526-z 

Liu, Z. An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusion. Artif Intell Rev 56, 15495–15522 (2023). https://doi.org/10.1007/s10462-023-10533-0 

 Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007 

Zaman, N.I.D., Hau, Y.W., Leong, M.C. et al. A review on the significance of body temperature interpretation for early infectious disease diagnosis. Artif Intell Rev 56, 15449–15494 (2023). https://doi.org/10.1007/s10462-023-10528-x 

Kusal, S., Patil, S., Choudrie, J. et al. A systematic review of applications of natural language processing and future challenges with special emphasis on text-based emotion detection. Artif Intell Rev 56, 15129–15215 (2023). https://doi.org/10.1007/s10462-023-10509-0 

Laureate, C.D.P., Buntine, W. & Linger, H. A systematic review of the use of topic models for short text social media analysis. Artif Intell Rev 56, 14223–14255 (2023). https://doi.org/10.1007/s10462-023-10471-x 

Bhushan, M., Pandit, A. & Garg, A. Machine learning and deep learning techniques for analyzing heart disease: a systematic literature review, open challenges and future directions. Artif Intell Rev 56, 14035–14086 (2023). https://doi.org/10.1007/s10462-023-10493-5 

Yu, K., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z 

Image source: Unsplash