1. Ulusal Yapay Zeka Buluşması, Kapadokya

• Tüm videolar
• Üretken Yapay Zekâ ve Dil Modelleri

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Keynote, XVIII. Swiss-Turkish Economic Forum

From Swiss Days, İstanbul 2024

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Emre Can Açıkgöz, M.S. 2024

Current position: PhD Student at University of Illinois Urbana-Champaign, Illinois (Homepage) MS Thesis: Grounding Language in Motor Space: Exploring Robot Action Learning and Control from Proprioception. August 2024. (PDF, Presentation)

Thesis Abstract:

Language development, particularly in its early stages, is deeply correlated with sensory-motor experiences. For instance, babies develop progressively via unsupervised exploration and incremental learning, such as labeling the action of ”walking” by first discovering to move their legs via trial and error. Drawing inspiration from this developmental process, our study explores robot action learning by trying to map linguistic meaning onto non-linguistic experiences in autonomous agents, specif- ically for a 7-DoF robot arm. While current grounded language learning (GLL) in robotics emphasizes visual grounding, our focus is on grounding language in a robot’s internal motor space. We investigate this through two key aspects: Robot Action Classification and Language-Guided Robot Control, both within a ’Blind Robot’ scenario by relying solely on proprioceptive information without any visual input in pixel space. In Robot Action Classification, we enable robots to understand and categorize their actions using internal sensory data by leveraging Self-Supervised Learning (SSL) through pretraining an Action Decoder for better state representation. Our SSL-based approach significantly surpasses other baselines, particularly in scenarios with limited data. Conversely, Language-Guided Robot Control poses a greater challenge by requiring robots to follow natural language instructions, interpret linguistic commands, generate a sequence of actions, and continuously interact with the environment. To achieve that, we utilize another Action Decoder pretrained on sensory state data and then fine-tune it alongside a Large Language Model (LLM) for better linguistic reasoning abilities. This integration enables the robot arm to execute language-guided manipulation tasks in real time. We validated our approach using the popular CALVIN Benchmark, where our methodology based on SSL significantly outperformed traditional architectures, particularly in low-data scenarios on action classification. Moreover, in the instruction following tasks, our Action Decoder-based framework achieved on-par results with large Vision-Language Models (VLMs) in the CALVIN table-top environment. Our results underscore the importance of robust state representations and the potential of the robot’s internal motor space for learning embodied tasks.

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IPN röportajı: Yapay Zeka Devrimi: Gelecekte Nasıl Ayakta Kalırız

Yapay zekanın geçmişi, bugünü ve geleceği Gençlerin yapay zeka alanında hangi meslekleri seçebilecekleri | Gençlere meslek seçiminde tavsiyeler Yapay zeka alanında başarılı olmak için gereken beceriler | Akıllı yazılımların farklı sektörlerdeki etkileri Tuğba Ağaoğlu'nun sunduğu program: https://youtu.be/p5xWWrS8XLA?si=cKBFh4T8AZqwHHLY.


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Batuhan Özyurt, M.S. 2023

Current position: AI Research Engineer, Codeway Studios (LinkedIn) MS Thesis: Localizing Knowledge in Large Language Model Representations. October 2023. (PDF)

Thesis Abstract:

Large language models (LLMs) are very proficient in NLP tasks. In the first part of this work, we evaluate the performance of LLMs on the task of finding the locations of characters inside a long narrative. The objective of the task is to generate the correct answer when the input is a piece of a narrative followed by a question asking the location of a character. For the evaluation of the task, we generate two new datasets by annotating the characters and their locations in the narratives: Andersen and Persuasion. We show that the LLM performance is not satisfactory on these datasets when compared to the simple baseline we designed that does not use machine learning. We also experiment with in-context learning to improve the performance and report results. Moreover, we address the problem that the LLMs are limited by the bounded context length. We hypothesize that if we localize the character-location relation information among the activations inside an LLM, we can store those activations and inject them into other models that are run with a different prompt so that the LLM can answer the questions about the information that was carried from another prompt, even though the character and location relation is not mentioned explicitly in the current prompt. We develop five different techniques to localize the character-location relation information occurring in the LLMs: Moving and adding LLM activations to other prompts, adding noise to LLM activations, checking cosine similarity between LLM activations, editing LLM activations, and visualizing attention scores during answer generation. We report the observations we made using these techniques.

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