Can you briefly introduce yourself and share your background in AI research?
I am Görkay Aydemir, currently pursuing my PhD in Computer Engineering. I completed my undergraduate studies in Computer Engineering at Middle East Technical University in 2022, and started my graduate studies following a year’s break. I engaged in a long-term internship with Fatma Güney, who is now my PhD advisor. Our works are published in top ML venues.
What initially sparked your interest in the field of AI? Was there a particular moment or experience that inspired you to pursue this area of study?
During my undergraduate studies, I wanted to pick a subject that would stand out in the future. I explored current trends in technology and global developments, leading me to choose Computer Vision as my focus area, which turned out to be a great choice.
Could you tell us about your current research or thesis topic in AI? What motivated you to choose this specific area?
I specialize in unsupervised computer vision, differing from the commonly used paradigm, supervised learning. Supervised learning depends on human-annotated labels for model training, but unsupervised learning harnesses data itself, bypassing the need for annotations. Considering the costs and complexities of annotating data, I believe there’s inherent value in learning directly from data, as it enables the use of more extensive datasets than what annotation allows.
What excites you the most about the potential applications of AI in the real world? Are there any specific domains or industries where you believe AI can make a significant impact?
While my focus isn’t on Natural Language Processing (NLP), I’m quite impressed by the advancements in models like ChatGPT. It’s likely that these enhanced NLP models will become valuable tools across various job domains in the near future.
What are some of the recent advancements or breakthroughs in AI that you find particularly fascinating or promising? How do you think these advancements can contribute to the overall progress of the field?
Generative AI has seen remarkable success and growing interest in recent years, particularly in areas like Computer Vision (Stable Diffusion etc.) and Natural Language Processing (ChatGPT etc.). These models have shown impressive capability in creating meaningful content. Reflecting on Richard Feynman’s quote, “What I cannot create, I do not understand,” I hold a complementary belief although it is not necessarily true given the hypothesis: “What I can create, I understand.” This perspective leads me to think that generative AI will play a crucial role not only in content creation but also in enhancing our understanding and differentiation of content, an area that remains largely unexplored at present.
How do you stay updated with the latest developments and research papers in the field of AI? Are there any particular resources or platforms you rely on?
The Computer Vision community on X is very active, with new developments and insights frequently shared across various accounts. Therefore, I believe the most effective way to stay informed about the latest papers, improvements, and models is by following these key accounts on X.
A book and movie that you recommend (they don’t have to be related to ai)
Selecting just one favorite is very hard, so here are three of my top choices for both movies and books. For movies, I highly suggest “The Irishman” (2019), “There Will Be Blood” (2007), and “Apocalypse Now” (1979) – each is an exceptional movie. Regarding books, given the wide range of topics, it’s a bit tougher to choose, but after sampling from my favorites, I’d recommend “Power” (Bertrand Russell), “The Hunchback of Notre-Dame” (Victor Hugo), and “Why Nations Fail” (Daron Acemoglu, James A. Robinson).
Bonus Suggestion: Watch “Killers of the Flower Moon” in cinemas, the most recent masterpiece from the legendary Martin Scorsese.