Machine learning

Artificial intelligence (AI), machine learning, and statistics are constantly advancing, pushing the limits of machine capabilities in learning and predicting. However, validation of emerging AI methods relies heavily on the availability of high-quality, real-world data. This is problematic as many researchers utilize simulated datasets, which often fail to completely represent the intricacies of natural situations.…

Creating AI agents capable of executing tasks autonomously in digital surroundings is a complicated technical challenge. Conventional methods of building these systems are complex and code-heavy, often restricting flexibility and potentially hindering innovation. Recent developments have seen the integration of Large Language Models (LLMs) such as GPT-4 and the Chain-of-Thought prompting system to make these agents…

Deep neural networks, particularly convolutional neural networks (CNNs), have significantly advanced computer vision tasks. However, their deployment on devices with limited computing power can be challenging. Knowledge distillation has become a potential solution to this issue. It involves training smaller "student" models from larger "teacher" models. Despite the effectiveness of this method, the process of…

Large language models, such as BERT, GPT-3, and T5, while powerful in identifying intricate patterns, pose privacy concerns due to the risk of exposing sensitive user information. A possible solution is machine unlearning, a method that allows for specific data elimination from trained models without the need for thorough retraining. Nevertheless, prevailing unlearning techniques designed…