Artificial Intelligence

In 2023, AI tools are incredibly versatile, encouraging content creators, designers, programmers, and even business owners to use them to achieve their professional goals. Artificial intelligence tools help businesses and individuals to automate processes, make more informed decisions, and improve efficiency.

The journey of a machine learning engineer doesn't stop when a model is deployed in production. It's like having a garden – you can't just plant the seeds and walk away, you have to keep an eye on your plants to ensure they grow well in all sorts of weather. Just like traditional gardening tools won't help much in a hydroponic setup, monitoring machine learning systems isn't the same as watching over regular software.

The term “foundation models” is very new. It was introduced and popularized by the Stanford Institute for Human-Centered Artificial Intelligence when first large-scale multitask models started to appear in 2019-ish.

The release of ChatGPT has urged numerous scientific labs to create or improve their own AI chatbots, leading to what we can call a chatbot race. Today, there are numerous options available on the market, both free and paid. In this blog post, we explore the capabilities of ChatGPt alternatives, along with pros and cons.

Graphic design is one of the fields that has been most impacted by the development of artificial intelligence. According to statistics provided by Forbes, AI is expected to see an annual growth rate of 37.3% from 2023 to 2030. This rapid growth emphasizes the increasing impact of AI technologies in the coming years.

Machine learning models are mainly developed offline but must be deployed in a production environment to process real-time data and handle the problem they were designed to solve. In this blog post, we will explore the fundamentals of deploying an ML model, discuss the challenges you may encounter, and provide steps to streamline the process for greater efficiency.

In this article, you’ll learn about what prompt engineering is, how it works, and even learn a couple of tricks that will help you work with generative AI systems like a pro.

In this blog post, we’ll explore the foundational concepts of the F1 score, discuss its limitations, and look at use cases across diverse domains.

As AI systems become more prevalent in businesses and homes, the need for robust security measures to protect these systems has become increasingly important. In this article, we look at AI security and why it's important.

TinyML – machine learning in embedded devices – is actively gaining popularity. This post will explain how TinyML works and why it is important.

In this blog post, we dive inside СhatGPT’s brain and explore large language models (LLMs) and other technologies that allow bots to communicate like people.

AI alignment is a new field that studies how to make it so that the created systems would answer the needs and expectations of humans. In this post, we share insights into what AI alignment is and how it works.