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Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervisedlearning to the forefront of adaptive models.
1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.
Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervisedlearning to the forefront of adaptive models.
Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.
Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervisedlearning. This process results in generalized models capable of a wide variety of tasks, such as image classification, naturallanguageprocessing, and question-answering, with remarkable accuracy.
Ikigai Labs Ikigai Labs is a company that provides a platform for building and managing naturallanguageprocessing models. The platform makes it easy to create and manage feature engineering pipelines, which can save time and improve the accuracy of machine learning models.
His research focuses on applying naturallanguageprocessing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings. I love participating in various competitions involving deep learning, especially tasks involving naturallanguageprocessing or LLMs.
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