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Diffusion models in practice. Part 1: The tools of the trade/in Generative models /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.
Guide: The diverse landscape of large language models. From the original Transformer to GPT-4 and beyond/in Generative models /by Artur Zygadlo
This guide is an attempt to explain and summarize the diverse landscape of LLMs in early 2023.
ChatGPT – what is the buzz all about?/in Generative models /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.
How to leverage ChatGPT to boost marketing strategy?/in Generative models /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 models /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.
Paramount factors in successful machine learning projects. Part 2/2/in Machine learning /by Robert Bogucki and Jan Kanty Milczek
In the first part of our guide we focused on properly executing the entire process of building and implementing machine learning models with a focus on the main goal – solving the overarching business challenge. In the second part of our material we dig deeper into the topic of modeling.
How to perform self-supervised learning on high-dimensional data/in Computer vision, Artificial Intelligence, Machine learning /by Łukasz Kuśmierz
In this post, the self-supervised learning paradigm is discussed. This method of training machine learning models is emerging nowadays, especially for high-dimensional data. In order to focus the attention of this article, we will only work on examples from the computer vision area. However, the methods presented are general and may be successfully used for problems from other domains as well.
The recent rise of diffusion-based models/in Generative models /by Maciej Domagała
In this post, we will sum up the very recent history of solving the text-to-image generation problem and explain the latest developments regarding diffusion models, which are playing a huge role in the new, state-of-the-art architectures.
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.
Data Science with Graphs – using knowledge graphs on the data before it reaches the ML phase/in Machine learning /by Grzegorz Rybak
Graph usage in AI recently became quite evident with an increased number of research papers and some impressive examples among the industry . This article aims to answer the question: Are there ways to improve a project’s delivery by using graphs even before reaching GraphML?