One of the major drawbacks of GPT-based models is their lack of knowledge about custom documents living in knowledge bases in large organizations. In this blog post I will demonstate a way to set up adding custom documents into the body of knowledge of GPT-based models.
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Large language models are emerging as a tool for solving many information and knowledge-related matters. Back in 2020 GPT-3 made huge improvements in the way that humans are able to interact with an AI, where the model showcased its ability to engage in compelling conversations with a human counterpart. In this article I will showcase how these large language models can be used for answering questions from data stored in tabular form, just as the majority of the world's business data.
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In December 2022 SAP held a machine learning competition called “I quit!” attended by 50 participants to showcase the machine learning capabilities of SAP HANA Cloud. The idea is to predict from a database of employees who is the most probable to be leaving his or her job in the short term, based on historical information of employees which have quit recently.
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In my last blog post I have shown how to download the Stable Diffusion model weights and set up an image generation workflow yourself using the Stable Diffusion model as provided by the Huggingface Transformers library. Although these text-to-image generation results can be pretty awesome, you can get similar results by interfacing with the DALL-E 2 playground which is publicly accessible without being on a wait list nowadays.
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The AI field is advancing in an incredible pace and 2022 has been shaken up by a few major announcements of algorithms able to generate fantastic images just by providing a text prompt. Some incredible work has been done by the OpenAI team which introduced DALL-E 2 to the public in March 2022, but also by Google Research who introduced Imagen a few months later.
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