Seonglae ChoStatement of purpose
Your statement of purpose should explain why you want to study your chosen programme and how it will help your future career aspirations. This should typically be one side of A4 paper.
- Your user name is: 240898702
- Your password is: m1mi0xmu
mysis.qmul.ac.uk
https://mysis.qmul.ac.uk/urd/sits.urd/run/siw_lgn
My genuine interest in Artificial Intelligence (AI) stems from a blend of intellectual curiosity and extensive practical involvement. Fascinated by the potential of AI, I have a particular interest in understanding how Large Language Models (LLMs) function. This curiosity motivated me to investigate Explainable AI, specifically Mechanistic Interpretability. My goal is to uncover the underlying mechanisms of AI process. This passion for interpretable AI guided my research into decision-making within AI summarization, which was accepted at NAACL 2024. Additionally, my three years of work experience in the autonomous driving technology sector have reinforced my belief in the necessity of a thorough grasp to ensure the safe and precise operation of AI.
Three years of software engineering at a startup and a major tech company in South Korea’s mobility sector are among the most enriching experiences of my life. Starting with two years at the mobility startup Stryx, I began by connecting software and hardware, gaining hands-on experience in sensor equipment. It enabled me to develop knowledge of sensor fusion, the starting point of the autonomous driving data pipeline. Later, I took responsibility for the latter part, the vector map generation, providing an overall insight into the data flow. This vertical understanding was instrumental in improving the pipeline, which established me as a key player. My significant contributions led to the delivery of the map for Korea’s first driverless service company, 42Dot, and facilitated Stryx’s successful acquisition by Kakao Mobility.
At Kakao Mobility, a dominant company in Korea's mobility sector, I led the development of the map platform and designed an API (Application Programming Interface) protocol for an AI feature bridging machine learning algorithm. Above that contribution, Kakao Mobility’s testing of a robo-taxi for public use under real-world conditions in Pangyo offered me firsthand exposure to the technology, which profoundly changed my perspective of AI. I experienced some stiffness in driving due to the discrete vector of the lane map because I knew the data under the hood. I realized that understanding internal AI systems is critical for investigating operational problems, as data plays a crucial role in AI system operation. After encountering that limitation, which I felt could not be resolved through industry exposure alone, I became convinced that a deeper academic understanding of AI was essential for making meaningful contributions to the field.
Driven by my resolve to delve into the mechanisms of AI, my return to academia after work experience led me to focus on Explainable AI, aiming to reveal the inner process of AI systems. As an intern at Yonsei University’s Data Intelligence Lab, I had the opportunity to research making AI summarization interpretable. My work on training the RTSum (Relation-Triple Summarization) model and creating the dataset for the RTSum framework, which split and recombined information at a granular level, led to our paper’s acceptance at NAACL 2024. This not only enhanced my research skills but also confirmed my belief in the importance of a thorough understanding of the underlying computing for effectively directing LLMs.
My affirmed belief in an interpretable approach to AI spurred me to actively engage in scholarly dialogue for Interpretable AI. Drawing from Anthropic's research on the interpretability of Transformer models, I explored the role of attention heads in forming in-context learning and conducted in-depth research on the functions of LLMs through OpenAI's Transformer Debugger. I reinterpreted this research and shared it with the AGI (Artificial General Intelligence) Korea group, aiming to highlight my perspective on grasping AI. In particular, Ioannis Patras’ recent work on 'A Study on the Use of Attention for Explaining Video Summarization' at Queen Mary University of London is in line with my approach of analyzing from basic computing units, which offers a unique academic environment that aligns with my research interest.
Among the streams offered by Queen Mary University of London, I am particularly drawn to the 'Agents and Creativity stream' of the Artificial Intelligence MSc program. I am especially excited about the insights from Ioannis Patras' Machine Learning module, and the ‘Interactive Agents and Procedural Generation’ module fits perfectly with my focus about AI control. This educational process will be instrumental in achieving my career goal of developing interpretable AI and contributing to the industry's AGI (Artificial General Intelligence) alignment.
My professional experience and academic research have taught me that ensuring AI operations function as intended requires starting with an understanding of the fundamental computing units. Analyzing the internal workings of attention mechanisms in LLMs through Mechanistic Interpretability would be a critical and promising research area for AI alignment. I am confident in my ability to advance my research goal, leveraging Queen Mary University of London’s ideal environment, with a well-balanced curriculum.
QMUL Visa
Confirmation of TOEFL iBT for CAS and Student Visa
I would like to confirm if I can submit the TOEFL iBT Home Edition listed on QMUL's English requirement page for a UK Student Visa, as I am applying to a Higher Education Provider according to the United Kingdom government. I am asking because the conditional offer letter required me to take a UK government-approved Secure English Language Test. However, the UK government's Student Visa webpage mentions that Higher Education Providers can assess my level of English. Is it correct to submit the TOEFL iBT Home Edition to QMUL, obtain a CAS, and then apply for a Student Visa?
Thank you for your help.
Kind regards,
Seonglae Cho
mysis.qmul.ac.uk
https://mysis.qmul.ac.uk/urd/sits.urd/run/siw_lgn
Referees should know applicants in an academic or, where appropriate, a professional setting, and be able to comment on their suitability for university study
www.qmul.ac.uk
https://www.qmul.ac.uk/media/arcs/docs/admissions-policy-documents/Admissions-Reference-Policy.pdf
Artificial Intelligence MSc - Queen Mary University of London
Artificial Intelligence (AI) is rapidly changing the way we live, work and learn. If you are looking to pursue a career in this booming field, this programme will give you the skills you need, as it is designed to maximise employability across a wide spectrum of industrial and academic posts related to AI. This MSc has been developed with the support of the Institute of Coding.
https://www.qmul.ac.uk/postgraduate/taught/coursefinder/courses/artificial-intelligence-msc/