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How we developed a GPT‑based solution for extracting knowledge from documents/in Generative AI /by Piotr Gródek
In this blogpost we will discuss our latest GPT-based solution addressing the challenge of extracting knowledge from a set of PDF documents.
Diffusion models in practice. Part 2: How good is your model?/in Generative AI /by Jarosław Kochanowicz, Maciej Domagała, Dawid Stachowiak and Dawid Żywczak
This is the second post in our series “Diffusion models in practice”. In this article, we start our journey into the practical aspects of diffusion modeling, which we found even more exciting. First, we would like to address a fundamental question that arises when one begins to venture into the realm of generative models: Where to start?
How to train a large language model using limited hardware?/in Generative AI /by Alicja Kotyla
Large language models (LLMs) are yielding remarkable results for many NLP tasks, but training them is challenging due to the demand for a lot of GPU memory and extended training time. To address these challenges, various parallelism paradigms have been developed, along with memory-saving techniques to enable the effective training of LLMs. In this article, we will describe these methods.
Data generation with diffusion models – part 1/in Generative AI /by Natalia Czerep
It is widely known that computer vision models require large amounts of data to perform well. Unfortunately, in many business cases we are left with a small amount of data. There are several approaches to overcoming the issue of insufficient data, one of which is supplementing the available dataset with new images, which is discussed in this article.
Diffusion models in practice. Part 1: The tools of the trade/in Generative AI /by Jarosław Kochanowicz, Maciej Domagała, Dawid Stachowiak and Krzysztof Dziedzic
The AI revolution continues, and there is no indication of it nearing the finish line. The last year has brought astonishing developments in two critical areas of generative modeling: large language models and diffusion models.
Report: The diverse landscape of large language models. From the original Transformer to GPT-4 and beyond/in Generative AI /by Artur Zygadlo
This report is an attempt to explain and summarize the diverse landscape of LLMs in early 2023.
ChatGPT – what is the buzz all about?/in Generative AI /by Eryk Mazuś and Maciej Domagała
Over the last few months, ChatGPT has generated a great deal of excitement. Some have gone as far as to suggest it is a giant step in developing AI that will overtake humanity in many important areas, both in business and social life. Others view it more as a distraction on the path towards achieving human-level intelligence. How did ChatGPT generate such hype? In this article, we’ll try to explain.
Five solid reasons to outsource your AI software development/in Artificial Intelligence /by deepsense.ai
Customized AI software development is one of the most powerful approaches to leveraging AI in business. Undoubtedly, the main difficulty of this approach is the ability to successfully develop and implement AI projects. Cooperation with an experienced AI vendor, who is responsible for end-to-end delivery, is one of the most effective solutions, bringing with it a number of benefits.
How to leverage ChatGPT to boost marketing strategy?/in Generative AI /by Ewa Szkudlarek
The revolution in marketing is happening before our very eyes. The latest developments in the area of generative models mark a milestone where artificial intelligence and human expertise have come together like never before, and the use of AI in marketing is no longer just a buzzword. With ChatGPT and other large language models, marketers will be able to harness the power of AI in an easy way.
How can we improve language models using reinforcement learning? ChatGPT case study/in Generative AI /by Kinga Prusinkiewicz
ChatGPT is a cutting-edge natural language processing model released in November 2022 by OpenAI. It is a variant of the GPT-3 model, specifically designed for chatbot and conversational AI applications.