In recent years, artificial intelligence has been gaining significance in various fields of life, and it might seem that before June 2020, when ChatGPT-3 was introduced, artificial intelligence did not exist. So how did it happen that Quarticon was created earlier than Open AI? Was there no AI before? Nothing could be further from the truth. The beginning of the 21st century marked the start of data analytics-based AI. Although both types of AI are advanced, they differ in their operation and applications, the former existed long before Open AI was established.
Data Analytics-Based AI (also known as Predictive AI)
Data analytics-based AI, also known as Predictive AI, focuses on analyzing historical data to forecast future events. This is the area where Quarticon operates. It uses advanced statistical algorithms and machine learning models to predict trends, patterns, and behaviors based on available information.
Key Features of Predictive AI
Data Analysis – processes and analyzes vast amounts of data from various sources, such as sales data, demographic data, or social media.
Statistics and Modeling – relies on mathematical models that learn from data to generate predictions.
Applications – in marketing, risk management, demand forecasting, or medical diagnostics.
For example, Predictive AI can help companies determine which products will be popular in the coming months, allowing for better resource planning and sales strategies. In medicine, it aids in identifying diseases based on medical data analysis. Algorithms can assess symptoms, lab test results, and a patient’s medical history, increasing diagnostic accuracy. Detecting cancers in early stages through medical image analysis. Predicting the risk of heart disease based on demographic data and patients’ lifestyle.
Generative AI
On the other hand, Generative AI is a type of artificial intelligence capable of creating new content, including texts, images, sounds, or other forms of data. Models of this type include systems like GPT – Generative Pre-trained Transformer. It is a type of artificial intelligence model designed to generate text in a natural and contextual manner.
Generative refers to the model’s ability to create new content based on provided data. Generative models can produce texts, images, or other forms of data, and their use includes writing articles, generating dialogues, or creating creative content.
Pre-trained means that the GPT model is first trained on vast datasets of text before being tailored for specific tasks. This process allows it to understand context, grammar, general knowledge, and language patterns. As a result, it can generate texts that are coherent and meaningful.
Transformer is a model architecture that revolutionized natural language processing. Unlike earlier models based on recurrent neural networks (RNN), the Transformer architecture allows for parallel data processing and better capturing of context in longer text sequences. A key element of this architecture is the attention mechanism, which enables the model to focus on different parts of the text during processing.
An important feature of Generative AI is interactivity – it allows users to interact in real-time, increasing engagement and personalization of experiences. Generative AI, such as ChatGPT, can conduct conversations, answer questions, and even create stories and articles, making it extremely useful in many industries.
Why Was Quarticon (Predictive AI) Created Before Open AI (Generative AI)?
Data analytics-based AI was the foundation of earlier AI solutions because it focuses on specific, measurable data analysis. In the 1970s and 1980s, machine learning theories began to develop, which became the basis for more advanced pAI techniques. Algorithms such as linear regression and decision trees began their careers in data analysis.
The boom in information technology and data collection systems in the 2000s made access to vast datasets commonplace. This enabled more complex analyses and the application of algorithms, significantly increasing the effectiveness of pAI.
As data became more accessible and complex, the need for analytical tools grew. Predictive AI helped organizations better understand customer behavior and make informed decisions. In the last decade, pAI began to be widely used in various sectors, such as marketing, finance, healthcare, or retail. Companies started implementing pAI-based solutions to better predict customer behavior, optimize resources, and manage risk. Quarticon was then established in 2010, introducing the famous AI-based product recommendations in Poland and throughout Central and Eastern Europe.
On the other hand, Generative AI, while having the potential to transform interaction with technology, developed based on earlier achievements in data analytics. A breakthrough moment in gAI development was the introduction of Generative Adversarial Networks (GAN) by Ian Goodfellow in 2014. In subsequent years (2018+), models based on the Transformer architecture, such as GPT (Generative Pre-trained Transformer), were created, revolutionizing text generation.
Quarticon, in turn, uses NLP language models (natural language processing) in its AI search engine. It was developed in 2019 as a tool used for indexing and searching large datasets in real-time. The search engine has become a popular choice for applications requiring fast searches and data analysis.
pAI and gAI – Two Different Fields That Together Form Artificial Intelligence
Data Analytics-Based AI and Generative AI are two different but extremely important branches of artificial intelligence. Both have their place in the modern world of technology and business. However, it should be remembered that generative AI is just a very convenient interface between the user and public internet resources.
Generative AI alone will not diagnose a disease, adjust medication doses based on patient genomics, conduct epidemic trend analysis and predict the development of infectious diseases, detect cardiological problems based on heart monitoring, and in more mundane applications – it will not detect behavior patterns between products and users and will not recommend accurate products. Generative AI needs predictive AI to return smart answers.
