Nakayad Polar, a PhD candidate at the University of Limerick, shares her inspiring journey into the field of Artificial Intelligence (AI), highlighting her academic and professional background, current research, challenges, and advocacy for women in STEM. Originally trained as a process engineer with a bachelor’s degree from the University of Ghana, Nakayad transitioned from working in food production and research to AI through a growing curiosity and recognition of AI’s problem-solving potential. Her PhD focuses on leveraging reinforcement learning to improve decision-making in autonomous vehicles, especially in complex, busy environments where current AI systems struggle. She emphasizes the importance of discipline, resilience, and self-belief in navigating the fast-evolving and sometimes isolating AI landscape. Nakayad also plays an active role in promoting female integration into AI through organizations like Women in AI Ireland, where she co-leads tutorials designed to make AI accessible and engaging. She confronts misconceptions about AI, advocating for a view of AI as a supportive tool rather than a threat, and encourages young women to explore AI careers without fear. Nakayad’s story reflects the intersections of engineering, AI research, gender inclusion, and the transformative potential of technology when approached with curiosity and determination.
Highlights
- 🔍 Nakayad transitioned from process engineering to AI, driven by curiosity and the desire to bridge gaps between engineering and IT.
- 🚗 Her PhD research focuses on using reinforcement learning to enhance autonomous vehicle decision-making in complex, busy environments.
- 💡 AI methodologies are transferable across fields, enabling Nakayad to apply AI solutions in food science and autonomous vehicles alike.
- 👩💻 Nakayad co-leads Women in AI tutorials in Ireland, promoting female participation and demystifying AI through interactive learning.
- 🛠️ Key personal skills for success in AI include discipline, resilience, and the ability to filter constructive feedback from noise.
- 🤖 Nakayad challenges common misconceptions about AI being “evil,” emphasizing its role as a tool to optimize and support human tasks.
- 🌍 Despite fast advancements, AI research faces challenges such as a lack of accessible expert support and industry alignment with academic work.
Key Insights
- 🌐 Interdisciplinary Expertise Bridges Gaps in AI Development: Nakayad’s background in process engineering combined with AI knowledge fills a critical communication gap between domain experts and IT professionals. This hybrid skill set is essential in complex fields like autonomous vehicles, where understanding both the engineering environment and AI algorithms leads to more practical and effective solutions. Her journey underscores the value of interdisciplinary learning in advancing AI applications beyond pure computer science.
- 🚦 Reinforcement Learning as a Tool for Complex Decision-Making in Autonomous Vehicles: Nakayad’s research uses simulation environments to model busy road scenarios, addressing why autonomous vehicles are not yet widely deployed in dense urban settings. Reinforcement learning allows AI agents to learn optimal behaviors through trial and error, adapting to dynamic environments with multiple interacting factors. This highlights the ongoing challenge and opportunity in making autonomous systems safe and reliable under real-world complexity.
- 🎯 Transferability of AI Methodologies Across Domains: AI techniques, such as reinforcement learning, are not confined to one field. Nakayad’s work in food science—using AI to nudge healthier eating habits by analyzing meal photos—illustrates how AI’s core methodologies can address diverse problems. This adaptability makes AI a powerful tool for innovation, especially when combined with domain-specific knowledge.
- 💪 Discipline and Resilience Are Critical Soft Skills in AI Research: The iterative nature of AI research, especially coding and model training, can be frustrating when solutions don’t work for extended periods. Nakayad stresses the importance of daily discipline and resilience, qualities that sustain motivation despite setbacks and slow progress. Additionally, knowing when to block out negative or uninformed criticism helps maintain focus and confidence. These soft skills are often underemphasized but vital for long-term success in cutting-edge fields.
- 👩💻 The Role of Female-Led Communities in Supporting Women in AI: Women in AI Ireland, where Nakayad serves as tutorial co-lead, exemplifies how targeted communities and educational initiatives can foster inclusion and skill development. By offering interactive tutorials and highlighting AI’s broad applications, these groups help overcome the gender imbalance in STEM fields and dispel myths about AI being inaccessible or intimidating. Such organizations provide mentorship, networking, and practical learning opportunities that are crucial for retention and advancement of women in technology.
- ⚖️ Challenges in Industry-Academia Alignment and Recognition: Nakayad points out the disconnect between academic research and industrial priorities, such as JLR’s focus on autonomous driving but not autonomous vehicles in certain contexts. This misalignment complicates career pathways and the practical application of research. Furthermore, difficulties in succinctly communicating AI expertise on a CV can impede recognition by peers and employers. This highlights the need for clearer translation of academic work to industry-relevant language and goals.
- 🤖 Addressing Misconceptions and Fear Surrounding AI: Nakayad addresses widespread societal fears about AI being “evil” or harmful, explaining that such fears often stem from lack of understanding. She advocates for viewing AI as a supportive tool that optimizes processes rather than replaces humans. This perspective is important not only for public acceptance but also for encouraging more women and underrepresented groups to enter AI fields, fostering diversity and balanced development of technology.