Artificial Intelligence, or AI, makes us look better in selfies, obediently tells us the weather when we ask Alexa for it, and rolls out self-drive cars. It is the technology that enables machines to learn from experience and perform human-like tasks.
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As a whole, AI contains many subfields, including natural language processing, computer vision, and deep learning. Most of the time, the specific technology at work is machine learning, which focuses on the development of algorithms that analyzes data and makes predictions, and relies heavily on human supervision.
SMU Assistant Professor of Information Systems, Sun Qianru, likens training a small-scale AI model to teaching a young kid to recognize objects in his surroundings. “At first a kid doesn’t understand many things around him. He might see an apple but doesn’t recognize it as an apple and he might ask, “Is this a banana?” His parents will correct him, “No, this is not a banana. This is an apple.” Such feedback in his brain then signals to fine-tune his knowledge.”
Professor Sun’s research focuses on deep convolutional neural networks, meta-learning, incremental learning, semi-supervised learning, and their applications in recognizing images and videos.
Training an AI model
Because of the complexity of AI, Professor Sun ventures into general concepts and current trends in the field before diving into her research projects.
She explains that supervised machine learning involves models training itself on a labeled data set. That is, the data is labeled with information that the model is being built to determine, and that which may even be classified in ways the model is supposed to classify as data. For example, a computer vision model designed to identify an apple might be trained on a data set of various labeled apple images.
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