Understanding Artificial Intelligence in Simple Terms
Image by Editor | ChatGPT
# Introduction
Artificial intelligence (AI) has become a widely discussed topic recently, with various interpretations depending on the person you ask. Some may use complex technical jargon, while others might offer simpler explanations. This article aims to adopt the latter approach, clarifying in straightforward terms what AI is, its capabilities and limitations, along with some essential components or foundational elements currently associated with AI.
# What is AI? Capabilities and Limits
AI is a division of computer science dedicated to the creation of computer systems that can replicate human or animal intelligence skills to tackle specific problems or tasks. The intelligent characteristics these systems can emulate include reasoning, predicting future or unknown events, visual perception, communicating with humans or other AIs, and interacting with their environments, among others.
One prominent feature in many modern AI systems, which encompasses nearly all the skills mentioned, is the capacity to learn from data. Similar to how a young child learns by observing and experiencing various instances (words, images, actions, etc.), AI systems learn from large volumes of data to execute complex tasks such as predictions, inferences, human language communication, and much more.
It may surprise some to learn that AI is not a novel concept of the 21st century; it actually originated in the mid-20th century. However, thanks to advancements in data learning — along with improvements in computational power and data accessibility — AI has made significant strides in the last decade and a half, with generative and language models like ChatGPT being prime examples of recent high-impact achievements.
Nonetheless, AI has distinct limitations: it does not genuinely “understand” the world as humans do (its processing is largely mathematical!), can err if it learns from low-quality data, and generally struggles with tasks that necessitate common sense or human emotions. The vast amounts of data and computational resources required also incur considerable expenses.
# Areas of AI
Let’s detail the primary areas of AI and some key points to enhance understanding of them.
Domains of artificial intelligence and their mutual relationships | Image by Author
- In the formative years of AI, knowledge representation and reasoning were prominent research topics, focusing on creating software that could reason and derive conclusions from real-world data represented in an understandable format for machines. Much of the logic governing autonomous agents (sometimes found in robotic systems that interact with the physical world) relies on these methodologies, and the recent emergence of agentic AI solutions has reignited interest in this area.
- Machine learning is currently the most widely used category of AI: nearly all modern AI systems employ data-driven approaches to accomplish tasks such as image classification, price prediction, customer product recommendations, and more.
- Deep learning can be viewed as an “advanced iteration” of machine learning, consisting of larger, more intricate systems that learn from extensive datasets to tackle more complex challenges. Notably, applications in computer vision and natural language processing—such as visually identifying objects and engaging in human-like conversations—have predominantly been resolved utilizing deep learning systems, illustrating their close interrelation.
- Where do the latest trends of generative AI and language models (LLMs) fit in? These are vast deep learning structures taken to an advanced level.
That offers a brief, straightforward overview of what AI is. We hope you found it engaging!
Iván Palomares Carrascosa is an expert, author, speaker, and consultant in AI, machine learning, deep learning & LLMs. He educates and assists others in leveraging AI effectively in real-world situations.
