It seems that we are moving towards a future in which much of what we do ⏤ both inside and outside a digital environment ⏤ will be done through conversations with artificial intelligence.
Artificial intelligence (AI) is one of the most disruptive and promising technologies of today. Something that seemed reserved for the future, but has recently made a leap whose impact on our lives we still do not fully understand.
It's not that AI hasn't been in our daily lives before. Quite the contrary: artificial intelligence has been present for many years in many applications and devices that we use every day, such as GPS, social networks or streaming services. FaceApp, the app that was all the rage in 2019, also applied artificial intelligence to show you what your face might look like when you're old. And the list goes on.
However, ChatGPT changed everything. Its launch as a public beta in November 2022 immediately generated great interest and admiration for its ability to produce coherent, fluid and fun responses to any type of query or comment. In fact, it became the fastest-growing app in history , reaching one hundred million users in just two months.
How generative artificial intelligence works
Within the field of AI, there is a branch that focuses on generating original content from existing data: generative artificial intelligence (GAI). GAI uses advanced algorithms and neural networks to learn from a huge data set and then generate new and unique content, in the form of images, video, music or text, with virtually no human intervention. Today, the best-known example of this technology is precisely ChatGPT.
But how does it actually work?
Generative artificial intelligence is based on deep learning methods. It works by chile phone number collecting information about certain elements that will then be used by the machine to generate other ideas.
To create this content, IAG uses what are known as generative adversarial neural networks , a system that uses two artificial neural networks to generate new data similar to existing data. One of the networks, called the “generator,” creates synthetic data that resembles the training data, while the other network, called the “discriminator,” evaluates the generated data and decides whether it is similar enough to the real data.
During training, the generator adjusts its output to fool the discriminator, while the discriminator is trained to identify the generator's fake data. This process is repeated many times until the generator is able to create data that is indistinguishable from real data.
ChatGPT and other systems like Midjourney rely on this system to create accurate and original content. However, as we have seen over the past few months, IAG-based models can also generate false content , something commonly known as “hallucination.” In that sense, minimizing these errors is one of the most important and urgent goals of the companies leading the research and development of IAG systems.