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Business Intelligence - Generative BI. Generative BI [ 67] refers to the application of generative AI techniques, like Large Language Models (LLMs), in business intelligence. This combination accelerates the development of advanced models, automates data analysis, and facilitates the generation of actionable insights.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
Prompt engineering is the process of structuring words that can be interpreted and understood by a text-to-image model. Think of it as the language you need to speak in order to tell an AI model what to draw. ^ Ziegler, Albert; Berryman, John (17 July 2023). "A developer's guide to prompt engineering and LLMs".
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
GitHub Copilot was initially powered by the OpenAI Codex, [13] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. [14] The Codex model is additionally trained on gigabytes of source code in a dozen programming languages.
An example of user-generated content in the virtual world of Second Life. User-generated content (UGC), alternatively known as user-created content (UCC), is generally any form of content, such as images, videos, audio, text, testimonials, and software (e.g. video game mods), that has been posted by users on online content aggregation platforms such as social media, discussion forums and wikis.
The QR code system was invented in 1994, at the Denso Wave automotive products company, in Japan. [5] [6] [7] The initial alternating-square design presented by the team of researchers, headed by Masahiro Hara, was influenced by the black counters and the white counters played on a Go board; [8] the pattern of position detection was found and determined by applying the least-used ratio (1:1:3 ...
Natural language understanding. Natural language understanding ( NLU) or natural language interpretation ( NLI) [ 1] is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem. [ 2]
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