With the rise of ChatGPT, “AI” has become a term that’s widely used across businesses in different sectors. According to a Forbes Advisor survey, 97% of businesses believe at least one aspect of ChatGPT will help their business. Even the most traditional industries have started to acknowledge the advantages of using AI.
Besides being an intriguing and popular concept, what exactly is AI? Gaining a better understanding of AI can improve communication with technical teams and help grow your business. In this article we’ll introduce some AI-related terms that can help you communicate with technical teams. Learning these concepts can make AI discussions between business and technology more transparent and productive for both sides. A lot of AI concepts are intertwined, but there are some subtle differences. Let’s take a closer look...
Modelling is a way of creating simplified, yet accurate, representations of complex processes. Its goal is to enable understanding and allow predicting outcomes of these processes. We use models every day. When you decide whether or not to cross the street, you model the probability of getting to the other side of the road safely. You assess factors such as the heaviness of traffic, the time you need to cross the street and the speed cars are moving. Even though it happens quickly and unnoticeably, your brain makes a decision based on your previous experiences with crossing streets. In the context of machine learning, modelling refers to creating a computer program that represents a simplified version of a real-life process. A machine learning model makes a decision based on the available data. We teach a computer by showing many examples of decisions and factors influencing them.
Artificial Intelligence (AI) refers to modelling human-like abilities. Those abilities include learning, decision-making, thinking, generalizing or inferring. It’s a set of technologies that mimic humans’ cognitive processes. The goal of AI is to support humans in performing complex tasks and promoting progress in various fields, including science and healthcare. It can also help speed up and automate repetitive tasks that are tedious and prone to error.
Machine Learning (ML) is a subset of AI that teaches a computer to learn from the data. ML models learn underlying patterns from examples available in the data, rather than from a set of rules. They don’t have to be explicitly programmed to make a decision, but they learn and improve from experience. There are many ML modelling techniques. The choice of an appropriate ML model depends on factors such as the type of decision to be made, the characteristics of the data and the size of the data.
Deep Learning (DL) is a type of Machine Learning that uses artificial neural networks to learn from and make decisions using the data. Artificial neural networks mimic the structure of human brains. They consist of interconnected nodes (neurons) which are organised into layers. Each layer processes a different aspect of the information available. Deep Learning can tackle complex tasks such as recognising images or understanding text and speech. It’s especially useful if we have huge amounts of data to train it on.
Generative AI (Gen AI) is a branch of Deep Learning that can create new content. It can generate different types of data, such as images, videos, audio or text. The content can be generated by Gen AI very quickly and it’s often difficult to distinguish from real data. Gen AI has many positive applications, such as improving and designing new products, drafting email responses or providing accurate translations. But it can also be used to generate deepfakes which mimic the behaviour of real people and can be difficult to identify.
Large Language Models (LLMs) are a class of Deep Learning models that are used for tasks connected to natural language. The distinctive feature of LLMs is the fact that they’re trained on huge amounts of text data. They can be used for many language-related tasks, such as understanding, translating or generating human-like language. LLMs are often used to build human-like chatbots and voice-bots that can help answer queries, summarise long texts and complete sentences.
These are just a few AI-related terms commonly being used in business. Those who understand the concepts are already uncovering advantages to using them as part of a wider technology stack. However, it’s important to remember that learning models are heavily dependent on the data they’re trained on, so you’ll need to familiarise yourself with terms related to data too. Stay tuned for more insights and, if you have any questions about our glossary, drop them into comments for the CKDelta AI experts.