The field of artificial intelligence is rapidly evolving, with researchers exploring a diverse range of cutting-edge topics and applications. Here are some of the key research areas at ACAI:
Machine learning is a dynamic field empowering computers to learn from data and improve their performance. It encompasses various algorithms like supervised, unsupervised, and reinforcement learning, along with powerful techniques like deep learning and neural networks.
Deep learning, a subset of machine learning, utilizes multi-layered neural networks to tackle complex tasks. Constantly evolving, it excels in processing large volumes of unstructured data like images, audio, and text.
Computer vision enables machines to interpret and understand visual information, similar to human vision. It combines advanced algorithms and deep learning techniques for applications such as autonomous vehicles and augmented reality.
It is a subfield of artificial intelligence (AI) and computational linguistics that deals with the interaction between computers and human languages. Its primary goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
It is a subfield of artificial intelligence (AI) and computational linguistics that deals with the interaction between computers and human languages. Its primary goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
XAI focuses on building transparent and interpretable AI systems that can explain their reasoning behind decisions and actions. This fosters trust and understanding in AI.
Time series analysis deals with analysing data points collected over time, like stock prices or sensor readings. The goal is to understand patterns, make predictions, and identify anomalies.
Reinforcement learning (RL) focuses on training agents to make sequences of decisions by rewarding desired behaviours. It is widely used in scenarios where learning optimal actions through trial and error is essential.
Edge AI refers to deploying AI algorithms directly on devices at the edge of the network, such as smartphones, IoT devices, and embedded systems. This approach reduces latency and bandwidth usage by processing data locally.