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.
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?
Much has been said about the effective running of machine learning projects. However, the topic keeps coming up. It is vital to remember that the purpose of ML projects is not modeling itself, but achieving defined business goals.
While enterprises recognize the measurable business benefits of AI adoption, they don’t necessarily see the path to get there. As the Everest Group survey indicates, 3 out of 5 of enterprises fail to adopt AI and don’t achieve meaningful business outcomes. Let’s look for the missing key to harness the full potential of AI implementation.
In recent years, we have seen rapid development in the field of artificial intelligence, which has led to increased interest in areas that have not often been previously addressed. As AI becomes more and more advanced, beyond model effectiveness, experts are being challenged to understand and retrace how the algorithms came up with their results, and how the models are reasoning and why [Samek and Muller, 2019].
We discuss the main challenges of AI implementations in retail and manufacturing with Ireneusz Prus, Director of Data Monetization at Maspex Group.
AI projects requires building interdisciplinary teams – both from business and technology. Executing the process successfully is quite a challenge and hiring and AI vendor may be an effective solution.
Read deepsense.ai’s article for MIT Sloan Management Review about artificial intelligence as a powerful tool for a circular economy and more sustainable development.
Computer vision is a foundational element of smart factory solutions. At deepsense.ai we have created diverse AI-driven, automated computer vision-based solutions that undertake the most demanding challenges in many different production lines.
Generating images from text has been a subject of rapid development as it might provide significant enhancements for the solutions across various domains. This text describes a complete framework that allows text-to-image conversion by combining several machine learning solutions.