AI Projects #8 for Social Good Showcase
AI Projects #8 for Social Good Showcase will take place on Saturday, May 18 from 12.30 to 17.30, at Beykoz Kundura.
AI Projects is a 3-month research and project group in which undergraduate, master's, and doctoral students and professionals freely develop their projects and conduct research in the field of Deep Learning and AI at inzva.
AI Projects #8 for Social Good, took place between February 24 and May 11, with the participants of our community. We worked with 21 people on 7 projects focused on social good and are excited to share the results with you!
You can see the report of the 6th batch here and the 7th batch here.
Duration: 7 projects; 15 minutes presentation & 5 minutes Q&A
Application Deadline: May 10 , 23.59.
Application Confirmation Deadline: May 14, 23.59
Applications are closed! You will be notified with an attendance form once your spot is confirmed.
Note: BEV Foundation (inzva) reserves the right to change or modify any of the conduct, design, and rules of the program at any time and in their sole discretion.
Showcase Program
12.30 - 13.00 Welcoming & Onboarding
13.00 - 13.10 Welcome Speech (Pınar Yıldız, AI Programs Coordinator)
13.10 - 13.20 Keynote Speaker (Utku Yavuz, General Manager)
13.20 - 13.30 AI Projects goes on (Zeynep Abalı, AI Projects Program Lead)
13.30 - 13.50 Sign Language Translation for Turkish Sign Language
Karahan Şahin, Metehan Seyran, Şafak Akıncı, Şilan Fidan Vural
13.50 - 14.10 Predicting Protein-Protein Interactions
Kayra Kösoğlu, Mehmet Anıl Taysi, Simge Şenyüz
14.10 - 14.30 MediMate:Medical Chatbot
Abdullah Maraş, Ceyda Başoğlu, Selin Çıldam
14.30 - 15.30 Lunch Break
15.30 - 15.50 Machine Unlearning in LLMs
Emir Faruk Erman, Ömer Faruk Şengül, Şerife Gül Korkut
15.50 - 16.10 Traffic Light Management
Melih Darcan, Volkan Bakır, Yusuf Koca
16.10 - 16.30 Coffee Break
16.30 - 16.50 Entity State Tracking with Mamba SSM
Berkin Deniz Kahya, Gürkan Soykan
16.50 - 17.10 Turkish Language Modeling with Tiny Models
Ayşe Sarı, Buğra Hamza Gündoğ, Elvan Karasu, Şafak Bilici
17.10 - 17.30 Closing Remarks
#8 PROJECTS
Sign Language Translation for Turkish Sign Language
Karahan Şahin, Metehan Seyran, Şafak Akıncı, Şilan Fidan Vural
Sign language recognition from videos is crucial for effective communication with deaf people. In this study, we will adopt language modeling strategies to the sign language domain by quantization of hand and body landmarks in spatial and temporal basis by utilizing graph convolutional networks.
Predicting protein-protein interactions from Foldseek sequence using NLP methods
Kayra Kösoğlu, Mehmet Anıl Taysi, Simge Şenyüz
Proper protein-protein interactions are vital to a healthy body. Any abnormality in protein interactions may lead to diseases. In this study, we addressed the challenge of predicting these interactions using structure-enhanced sequences of proteins (which contain 3D information in 1D format) to solve the protein-protein interaction problem. To do so, we utilized a variety of deep learning architectures ranging from 1D-CNNs to GCNs.
MediMate:Medical Chatbot
Abdullah Maraş, Ceyda Başoğlu, Selin Çıldam
This project aims to develop an advanced, interactive AI chatbot designed to accurately comprehend and respond to inquiries from patients and healthcare staff. By integrating and fine-tuning state-of-the-art open-source language models such as LLama and Mistral, we have customized our chatbot to support the Turkish language, ensuring it delivers precise and useful answers. The chatbot is intended to enhance accessibility and provide valuable assistance to a diverse array of users.
Machine Unlearning in LLMs
Emir Faruk Erman, Ömer Faruk Şengül, Şerife Gül Korkut
Machine unlearning presents a sophisticated methodology within artificial intelligence, emphasizing selective forgetting mechanisms rather than the wholesale retraining of models. This approach is particularly significant in light of stringent privacy regulations such as the General Data Protection Regulation (GDPR) and the Right to Be Forgotten (RTBF). Instead of erasing entire datasets, AI systems can now implement targeted processes to modify or discard specific data points while retaining overall knowledge structures. By adopting this strategy, organizations can ensure compliance with privacy regulations without compromising the functionality or efficiency of their AI models. Such an approach not only conserves computational resources but also bolsters trust in AI systems by demonstrating a commitment to safeguarding individuals' privacy rights.
DeTraffic: Deep Reinforcement Learning to De-Traffic Our Lives
Melih Darcan, Volkan Bakır, Yusuf Koca
The average person spends 43 hours in traffic annually. This amount of time should and can be reduced to contribute to both society and individuals in the long run. DeTraffic is here to the rescue, it's a Multi-agent deep reinforcement learning model that controls traffic lights.
Entity State Tracking with Mamba SSM
Berkin Deniz Kahya, Gürkan Soykan
In this project, we apply the Mamba model to several different entity state tracking datasets and benchmark its performance, comparing it with transformer-based architectures such as T5. Entity tracking is a high-level linguistic behavior, and achieving high accuracy in this task requires many additional capabilities. The importance of entity state tracking lies in understanding the context, maintaining conversation flow, personalizing responses, and facilitating complex conversation tasks that include entities, such as flight booking and scheduling appointments. Due to the nature of the problem, it requires long-context reasoning capabilities. Mamba, a recent model using state space model architecture as opposed to the Transformer, shows promise in tasks that require long-context reasoning and offers several other advantages, such as linear scaling.
Turkish Language Modeling with Tiny Models
Ayşe Sarı, Buğra Hamza Gündoğ, Elvan Karasu, Şafak Bilici
Due to growing interest in AI, larger language models are commonly developed, yet smaller models can achieve similar results with greater efficiency. This study evaluates different architectures and their combination with tokenization strategies to optimize language modeling, specifically aiming to develop efficient, compact models for Turkish.
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