Artificial Intelligence

In this article, we've collected information on how AI has transformed manufacturing by automating routine tasks with never-sleeping machines and reducing operational costs. We will share some of the most promising use cases and delve into the opportunities and challenges of implementing AI.

Unsupervised learning is a type of machine learning that relies less on human guidance and intervention and more or analyzing raw data and extracting patterns from it. It’s thanks to unsupervised machine learning that today we have so many powerful ML applications such as generative AI systems, search engines, and recommendation systems. In this article, you will learn about how unsupervised learning works and what techniques you can use to build your own ML model.

With the global energy demand escalating, companies in the oil and gas field face growing demands to boost operational efficiency, cut expenses, and adhere to safety and environmental regulations. AI has emerged as a crucial solution to meet these demands. Over the last ten years, the integration of artificial intelligence into the oil and gas sector has significantly transformed the industry.

In machine learning, there are different approaches to building effective learning representations. One of them that appeared first and up to this day continues to be quite a popular way of teaching machines to learn and make predictions, is supervised learning. In this article, you will learn what supervised learning is, how supervised machine learning models are built and how they are used for real-life applications.

Hyperparameter optimization plays a significant role in the development and refinement of machine learning models, ensuring their optimal performance for specific tasks. The Bayesian optimization algorithm stands out among various methods due to its efficiency and effectiveness. Unlike hyperparameter tuning methods like random search and grid search, which evaluate parameter values independently without considering outcomes from previous iterations, Bayesian optimization leverages results from previous evaluations.

Generative artificial intelligence has transformed many industries from content creation to healthcare and fintech. Because the use of generative AI has become so widespread, it introduced certain challenges for the cybersecurity of individuals and whole corporations. McKinsey Global Survey on AI shows that 40 percent of organizations plan to increase their overall AI investment because of advancements in generative AI. At the same time, 53 percent of organizations acknowledge cybersecurity as a generative AI-related risk.