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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 ...
Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. [2]
On Dec. 20, the company unveiled a new AI model called o3 that showed off-the-charts performance on a series of benchmark tests, including one specifically designed to gauge whether AI models are ...
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 ...
Recursive self improvement (aka seed AI) – speculative ability of strong artificial intelligence to reprogram itself to make itself even more intelligent. The more intelligent it got, the more capable it would be of further improving itself, in successively more rapid iterations, potentially resulting in an intelligence explosion leading to ...
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems.It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources.
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.
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