By: Steven Parker
Interviewer: Good day, everyone! Today, we embark on an extraordinary voyage through Arpita’s career narrative, a beacon of ingenuity and determination in the realm of Artificial Intelligence and Machine Learning. Join me in welcoming Arpita as we delve into her professional chronicle. Please share a bit about your background in this area.
Arpita: Thank you for having me. My journey in AI software quality began during my graduate studies, where I delved into the intersection of machine learning and software engineering. Since then, I’ve been passionate about ensuring that AI systems not only deliver accurate results but also meet stringent quality standards to foster trust and reliability.
Interviewer: That’s fascinating, Arpita. Given the complexity of AI systems, what do you believe sets your approach to software quality in this domain apart from others?
Arpita: In the realm of AI software quality, I advocate for a multidisciplinary approach that combines traditional software engineering practices with specialized techniques for validating AI models. One of the key challenges in AI software quality is ensuring that the models behave as expected across diverse datasets and real-world scenarios.
Interviewer: Your project on “Study of Domain Coverage and Performance of Low-Resource Chatbots” addresses a key challenge of chatbot performance in various domains. Could you elaborate on how your findings might impact the industry?
Arpita: Low-resource generative chatbots refer to AI models designed to generate human-like responses in conversations, but they operate under constraints such as limited training data, computational resources, or domain-specific knowledge. Traditional metrics like perplexity or BLEU scores may not capture the quality of responses accurately. In this research, the approach is leveraging transfer learning, where we pre-train the chatbot on a large dataset from a related domain or task before fine-tuning it on the target domain with limited data. This helps the model acquire general linguistic knowledge and adapt more effectively.
Interviewer: It’s fascinating how you can overcome these challenges creatively. Looking ahead, your projects’ endeavors in enhancing household robotics through DIRL are both pioneering and transformative. Could you elucidate how DIRL is revolutionizing the capabilities of household robots?
Arpita: Certainly. Deep Interactive Reinforcement Learning (DIRL) empowers household robots with the ability to learn from and interact with their environment in a manner akin to human cognition. Unlike traditional approaches, which rely on predefined rules or programming, DIRL enables robots to adapt and evolve their behavior through continuous interaction and learning. This dynamic paradigm not only enhances the efficiency and autonomy of household robots but also imbues them with a level of adaptability and intelligence previously unseen. My vision for the future of household robotics is one where intelligent, adaptive, and empathetic robots seamlessly integrate into our daily lives, augmenting our capabilities and enhancing our quality of life. Whether it’s assisting with household chores, providing companionship to the elderly, or even serving as educational companions for children, DIRL-powered robots hold the potential to revolutionize the way we interact with technology and each other. By harnessing the power of deep learning, human-robot collaboration, and ethical AI principles, we can pave the way for a future where robots are not just tools but trusted companions and collaborators in our journey toward a more connected and inclusive society.
Interviewer: How do you see AI forming long-term businesses in financial services, healthcare and retail industries?
Arpita: AI is currently revolutionizing various industries by improving decision-making, tailoring customer experiences, and bolstering security measures. Looking ahead, Arpita foresees AI taking on a pivotal role in predictive analysis, identifying fraud, and streamlining repetitive tasks, thus enabling individuals to concentrate on more strategic endeavors. She emphasizes that AI is intended to enhance human abilities rather than supplant them.
Interviewer: Your inclusion as a judge for the STEM, Hackathons, innovation and technology awards, coupled together with your broad involvement, gives you a one-of-a-kind viewpoint on innovation. How did the experience of judging these awards contribute to your own professional development?
Arpita: As a judge for a prestigious STEM and technology awards event, I was tasked with evaluating entries in the category of App Design and Innovative Use of Technology. Participating in the evaluation of STEM and technology awards can be an enriching experience, especially when it involves immersive technologies like VR that bring projects to life.
This unique experience not only provided a deeper understanding of the project but also highlighted the transformative potential of technology for social good. It emphasized the importance of human-centered design and community involvement in technology initiatives. As a judge, it reinforced my commitment to recognizing and supporting innovative solutions that address real-world challenges.
Interviewer: What best advice can you share for AI and ML enthusiasts?
Arpita: As you navigate your journey in the dynamic world of AI and ML, remember to build a strong foundation, stay curious, and practice relentlessly. Embrace challenges as opportunities for growth, prioritize ethical considerations in your work, and foster a collaborative spirit within the community. Cultivate resilience, adaptability, and effective communication skills, and never stop learning. With dedication, innovation, and a commitment to making a positive impact, you have the potential to shape the future of AI and ML in profound and meaningful ways.
Interviewer: Arpita’s journey encapsulates the essence of perseverance and innovation, inspiring professionals across diverse domains. As she embarks on new horizons, her indomitable spirit remains a guiding light for aspiring trailblazers in software engineering.
Arpita: Thank you for the insightful conversation. It’s been a pleasure sharing my experience with you all.
Published by: Khy Talara