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Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.
Artificial intelligence was founded as an academic discipline in 1956, [6] and the field went through multiple cycles of optimism throughout its history, [7] [8] followed by periods of disappointment and loss of funding, known as AI winters. [9] [10] Funding and interest vastly increased after 2012 when deep learning outperformed previous AI ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Many of the early approaches to knowledge represention in Artificial Intelligence (AI) used graph representations and semantic networks, similar to knowledge graphs today. In such approaches, problem solving was a form of graph traversal [2] or path-finding, as in the A* search algorithm. Typical applications included robot plan-formation and ...
Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was proposed in the 2017 paper " Attention Is All You Need ". [ 1 ]
The flashy artificial intelligence startup OpenAI and the semiconductor giant Nvidia are two of the most talked about companies in the world these days amid the rise of generative AI—and the ...
Apple’s new iPhone 16 lineup features new colors, a new camera button and – perhaps most noteworthy – a new artificial intelligence system.. The tech giant is set to roll out features from ...
Multi-head attention enhances this process by introducing multiple parallel attention heads. Each attention head learns different linear projections of the Q, K, and V matrices. This allows the model to capture different aspects of the relationships between words in the sequence simultaneously, rather than focusing on a single aspect.