What is AI? A Quick Beginner’s Guide to Artificial Intelligence
Artificial Intelligence (AI) is a fascinating and rapidly evolving branch of computer science that focuses on creating machines or software capable of tasks we usually associate with human intelligence. These tasks range from recognizing speech (like when your phone understands your voice commands) to making logical decisions (like suggesting the next show to watch on your favorite streaming service). In fact, you gave been using AI for much longer than you may realize in your everyday life – through personalized recommendations on social media, virtual assistants like Siri or Alexa, and, of course, tools like ChatGPT (who has not tried it?).
AI’s roots stretch back to the 1950s, but in recent years, increased computing power and vast amounts of accessible data have accelerated its growth and impact. From early 2023 the use of AI exploded with ChatGPT open to the masses to try … and also have fun with.
Although AI can sound intimidating at first, its basic idea is straightforward: we teach computers how to learn from data so they can make better decisions or predictions over time. Understanding these foundational concepts is the first step toward appreciating how AI is changing the world around us, and how quickly also!
At the heart of AI lies the concept of “machine learning.” Rather than programming a computer with a rigid set of rules for every possible situation, machine learning allows the computer to learn from examples. Imagine you want to teach a computer how to recognize pictures of cats. Instead of giving it strict definitions – like telling it cats have pointed ears, whiskers, and certain fur patterns – you feed it thousands or even millions of photos labeled “cat” and “not cat.” Over time, the system processes these images, looking for patterns that reliably distinguish cats from other animals. The more data it sees, the more accurately it can predict whether a new photo is a cat or not. This approach is powerful because it adapts: if it encounters new types of cats (long-haired, short-haired, different breeds), it can refine its understanding and improve its accuracy. This idea of learning from data underpins much of modern AI, enabling everything from spam filtering in email to self-driving cars that recognize pedestrians and traffic signs.
The beauty of this is that ML engineers can adjust the learning to be more accurate. The ML engineers design and write the software to automate AI and Large language Models (LLMs). For example, if a ML task was to identify a cat (using the example above) by the shape and color of the ears and it kept getting it wrong, then adjustments in the identification would have to be done. This is know as model fine-tuning or model retraining.
What is a model I hear you ask? A model in AI is like a smart tool or program that learns to make decisions or predictions based on patterns in data. Think of it as a recipe or a set of instructions that an AI uses to solve a specific problem. This is what the Ml engineer does in designing and tuning the models to give accurate results. Yes, there is a manual process in getting models to produce more accurate results.
Within the broader field of AI, there’s Generative AI. While much of traditional machine learning focuses on classification and prediction – like identifying if something is a cat or a dog – Generative AI is about creation. It doesn’t just classify existing data; it generates brand new content. This can include text, images, music, and even videos. Think of it as if you gave an artist millions of examples of art styles, and afterward, that artist could produce unique, original paintings inspired by what they learned. Generative AI systems, often based on advanced algorithms called “neural networks,” take a similar approach. They learn the patterns and structures within a massive dataset – say, a giant collection of images or text – and then use this understanding to create new outputs that resemble the style of the training data but aren’t direct copies. This is what underlies many popular AI art platforms and text-generation tools.
A well-known practical example of Generative AI is ChatGPT which I mentioned earlier. At its core, ChatGPT is powered by a Large Language Model. This model has been trained on an enormous amount of text from the internet, ranging from books to websites to articles. By learning how words, phrases, and sentences typically follow one another, the model can craft responses that make sense in a wide variety of contexts. Ask ChatGPT for a recipe, and it will generate a list of ingredients and steps. Ask it to write a poem about the ocean, and it can create a verse that captures the rolling waves and tranquil depths. The “intelligence” here emerges from patterns learned during training, enabling it to guess what the best possible “next word” (or words) should be. It’s a bit like predicting how someone might complete a sentence, but on a very large and nuanced scale.
Oh in case you are wondering, GPT is an acronym that stands for “Generative Pre-trained Transformer” and refers to a family of LLMs that can understand and generate text in natural language.
Generative AI tools aren’t just limited to text. Platforms like Midjourney, DALL·E, and Stable Diffusion create striking images based on prompts that describe what you’d like to see. You can request a picture of a “cyberpunk cityscape at night” or “a whimsical watercolor of a dragon in a field of flowers,” and these tools will generate unique images on the fly. They are really a lot of fun to play with.
Musicians, too, are using AI to develop new compositions, blending genres or instruments in ways that might never have been imagined. Researchers are even exploring generative models that craft video or 3D virtual environments. These creative systems are opening up opportunities in fields like game development, marketing, and entertainment, where fresh, unique content is often a major selling point. In education, they can help students visualize complex concepts or generate new learning materials. The applications are expanding daily, showcasing how AI can be a partner in the creative process rather than just a tool for crunching numbers or sorting data.
However, AI’s power and flexibility come with challenges. For one, AI systems – especially generative models – rely heavily on their training data. If the data contains biases, mistakes, or outdated information, the AI’s outputs may inherit these flaws. For instance, an AI that was trained primarily on texts from a certain demographic or viewpoint might produce responses that unwittingly favor or reflect those biases. Similarly, a text-generation tool might struggle to stay current on the latest events if its data cutoff is older. Biases deserves a post on its own (stay tuned). Then there’s the question of ethics: how do we ensure AI respects privacy, avoids spreading misinformation, and doesn’t harm society? Ethics is a huge discusssion point, and also deserves a separate post to talk about it.
Content ownership and intellectual property issues also arise when AI creates art, music, or text that is derivative of existing works. Policymakers, tech companies, researchers, and everyday users are all part of the ongoing conversation about how to balance innovation with responsibility.
Understanding these dynamics is critical if we want to harness AI’s potential while minimizing risks.
Looking toward the future, AI and generative AI will continue to evolve, becoming more integrated into our daily lives. Already, we see are seeing AI working behind the scenes in healthcare—analyzing medical images or helping doctors make more accurate diagnoses. In finance, AI algorithms are detecting fraudulent transactions much faster than human analysts. In retail, chatbots guide customers toward the right products or handle basic support queries around the clock. These chatbots have been around for a while but are so much more intelligent and ‘human-like’ now.
On a larger scale, AI may help address grand challenges like climate change, by analyzing vast datasets from satellites and sensors to optimize energy use or identify more sustainable agricultural practices. Generative AI will likely become an even bigger player (than it already is) in creative industries, aiding artists, filmmakers, and designers with new ideas and faster prototyping. We may also see more personalized education tools, where AI tailors coursework to each student’s learning style and pace. The possibilities are almost endless, but they require thoughtful stewardship and continued efforts to refine how these tools learn and interact with people.
In summary, Artificial Intelligence is not magic—it’s a powerful set of techniques that let computers learn from examples and make informed decisions or generate new content. From machine learning’s fundamental concepts of pattern recognition to the boundless creativity offered by generative AI, we are witnessing how these technologies can transform industries and everyday life. ChatGPT is just one example, offering a glimpse into the potential of large language models to communicate and assist in myriad tasks. As more of us become aware of AI’s capabilities, it’s important to keep a balanced perspective. We should celebrate the innovation and convenience AI can bring while also staying mindful of its limitations and ethical implications. By exploring these concepts now, you’re setting the foundation to engage meaningfully with AI—whether that means using generative tools to create artwork, applying machine learning in your business, or simply staying informed about a technology that’s rapidly reshaping our world. It’s an exciting journey, and as AI continues to evolve, there’s never been a better time to learn about it.
This is by no means a comprehensive guide to AI. It’s just a quick read intro to give you an idea. Check the site for more posts, as well as my YouTube channel.