Artificial intelligence has become a significant trend lately. At Collision 2023 in Toronto, there was much talk about the prospects and challenges of generative AI (read more here). According to popular belief, it is set to replace many professionals across various fields, but in this article, we discuss why we disagree with this notion.
The surge in interest towards artificial intelligence is attributed to the success of ChatGPT. In this article, we elaborate on how ChatGPT defines the concept of ‘Generative AI’. As of May 2023, according to Exploding Topics, the number of active users of this system, developed by OpenAI, exceeded 100 million. Meanwhile, the official developer's website attracts over 1.8 billion visitors every month.
The overwhelming interest in artificial intelligence technologies has prompted us to discuss how such technologies are developed. It's a complex process, often out of reach for smaller companies. It requires not only the appropriate expertise but also a significant amount of financial and other resources.
Neural networks form the basis for the majority of modern AI systems. Their operational principle is inspired by the structure and functionality of the human brain. However, unlike the biological neurons humans use, machines employ artificially-created mathematical algorithms.
A neural network needs to be trained to perform specific tasks. This requires a large "textbook" with an extensive dataset. For instance, to teach artificial intelligence to recognize cats in photos, it needs to be shown millions of snapshots of these animals of various breeds, ages, and colours.
In this case, during the training process, the neural network attempts to identify common traits or features presented in the images. When the artificial intelligence makes an error, its model is adjusted and modified to improve outcomes. Neural networks trained to work with text and voice are educated in a similar manner.
However, it's important to understand that in complex artificial intelligence systems, like ChatGPT, one neural network is often insufficient. In such cases, a combination of multiple different neural networks is utilized, each potentially having its specialization. For example, one might be used for recognizing text, while another for images.
Creating and developing artificial intelligence is a very intricate task. Therefore, scientists and engineers are constantly seeking new training methods, structures for neural networks, and other technologies that make AI systems even more powerful, efficient, and useful.
Furthermore, it's essential to realize that the development of artificial intelligence isn't only reliant on the skills of mathematicians and developers. Ethical boundaries, which determine the limits of AI use, are also a crucial aspect, and only specialists in the field can address them.
Artificial intelligence is changing the world for the better and creating new possibilities. For example, with the help of several complex neural networks, the RoomRenderAI application, which we developed for Triforce Construction, can generate room designs post-renovation based on user preferences. Read more about it in our case study.