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Modern AI tools can generate, create, summarize, translate, classify and even converse. Tools in the generativeAI domain allow us to generate responses to prompts after learning from existing artifacts. However, there are smaller models that have the potential to innovate gen AI capabilities on mobile devices.
One popular term encountered in generativeAI practice is retrieval-augmented generation (RAG). at Google, and “ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ” by Patrick Lewis, et al., One of the root causes for failures in graphs generated by LLMs involves the matter of entity resolution.
We define weak AI by its ability to complete a specific task, like winning a chess game or identifying a particular individual in a series of photos. Natural language processing (NLP) and computer vision, which let companies automate tasks and underpin chatbots and virtual assistants such as Siri and Alexa, are examples of ANI.
In summary, EDS is a serious, practical issue affecting all areas of GenAI, most notably LLMs [10, 11], and image generation (GANs [12], Diffusion Models [13]). source: The Missing Link in GenerativeAI | Fiddler AI Blog ]. In this section, we’ll delve into the ‘hard’/technical factors behind EDS in GenerativeAI.
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