Think AI is just ChatGPT? Think again

Last year, I kicked off my freelance career, diving into the wild world of AI. I figured getting clients would be tough, so I needed a way to stand out. Then it hit me: the internet's drowning in AI "guru" articles that are mostly hot air, think bold claims like "AI will run your life and take over your job!". So, I thought, why not write a series of blogs with actual facts about AI? It's a chance to share real insights, help people understand the tech, and maybe get some new clients to notice me. Win-win! But seriously, AI talk is everywhere; scroll through LinkedIn or news sites, and you're bombarded with stories about the latest AI breakthrough or overhyped promise. As someone working in the field, I find the misinformation frustrating. It confuses newcomers, leaving them unsure of what AI can do. This series is here to set things straight with clear, practical info on AI's uses, especially for businesses. Let's start with the big name everyone's heard: ChatGPT.

What is ChatGPT?

It's not a single AI but an application from OpenAI that acts like a friendly chat window, powered by clever systems, like GPT-4o or earlier GPT versions, that generate human-like text. Imagine a massive digital library where you type your question into ChatGPT's app, and a brilliant librarian finds the perfect answer for you. This librarian has explored a huge archive of books, blogs, and websites, so whether you ask for history trivia or recipe ideas, they deliver a clear, conversational response based on all they've learned.

Here's how it works: picture that librarian in their high-tech library. The library itself, stuffed with resources, is artificial intelligence, or AI, the foundation of the whole setup. The librarian's years of study, mastering the shelves, is the training that makes the system smart, known as machine learning. Their knack for understanding your request, like knowing you want "something about space", is the system's ability to process your words, called natural language processing, or NLP. And the vast archive, brimming with global knowledge, is the enormous collection of text the system draws from, what we call a Large Language Model, or LLM. When you type a question into ChatGPT, the app hands it to the "librarian" (the system behind it) to craft a tailored reply. These systems carry a technical name, Generative Pre-trained Transformer (GPT), which points to their complex design, but we'll explore that later. As a freelancer, I love breaking this down because it shows ChatGPT isn't some sci-fi mystery; it's a practical tool anyone can grasp.

Many people call AI systems like the ones powering ChatGPT a "black box", implying their workings are a total mystery. But while we're still digging into why these models learn the way they do, we know a fair bit about how they operate and train on data. It's less spooky than some gurus make it sound.

So, how do ChatGPT's models churn out those clever responses? It's all about guessing the next word in a sentence, kind of like a supercharged version of your phone's autocomplete, but with a PhD in everything. Picture this: the model's been trained on a mind-boggling pile of text, think billions of web pages, books, and blogs. It uses that to figure out, "Okay, based on what's been said so far, what word makes sense next?" It's not just parroting phrases; it's smart enough to spot patterns and meanings. For every word it picks, it's got a mental list of options, each with a likelihood of fitting in. To keep things interesting, it might add a dash of randomness, which is why asking the same question twice can get you slightly different answers. It builds its response one piece at a time, sometimes a full word, sometimes just a chunk, called a "token" in tech-speak.

This word-guessing trick comes from soaking up a massive amount of text from all over—internet posts, novels, you name it. That's the model's brain food. Want an easy way to see it? Imagine teaching a kid to sort a giant pile of colourful marbles. At first, they're clueless, mixing red with blue. But show them enough examples, and they start spotting patterns, reds go here, blues there. ChatGPT's models learn language in the same way. They're fed endless sentences and conversations, picking up grammar, word pairings, and even snappy writing styles. During training, they're given a chunk of text and told to guess what comes next. Every time they nail it (or screw it up), their inner workings, think of them as brain connections, get tweaked to sharpen their skills. It's like that kid getting better at sorting marbles with practice, except these models are sorting words to sound human.

There are others…

You've probably heard ChatGPT's name thrown around like it's the king of AI, the one tool to rule them all. But as I told you in the beginning, ChatGPT is just an app, not a single model, and it's a tiny slice of the AI world. OpenAI has a whole toolbox of models powering it, like GPT-4.1 for chatting and writing or o3-mini for questions related to math that need more reasoning. Each is built for a specific job; think of them as specialised chefs, not one-size-fits-all cooks. Other companies are doing the same, often with models that can outshine ChatGPT's, depending on what you need. My blog's here to slice through the hype and show why I'm pumped to help businesses tap into these tools.

ChatGPT & other LLMs didn't come out of a vacuum and are the results of decades of contributions from various people. No AI lab is significantly ahead of the others. ~ Yann LeCun, chief scientist at Meta

So, what's the deal with the rest of the field? Anthropic offers Claude, with versions like Claude 3.7 Sonnet for professional coding tasks. Google DeepMind got Gemini for real-time web searches, Gemma for lightweight coding, and BERT for understanding queries. AI at Meta LLaMA series is a developer favourite due to it being open-source, with versions optimised for research or custom apps. Mistral AI builds models like Mixtral, balancing speed and smarts for quick replies, while xAI's Grok is made for snappy, social media-style chats. DeepSeek AI lineup, including R1, is a go-to for number-crunching and data science. This is just a snapshot of all the different models on the market, many of them being better than models made by OpenAI.

Now, what does "better" mean in this crowded field? It's not about one model crushing the rest, it's about fit. AI pros judge models on accuracy (nailing facts), speed (how quickly the reply lands), context window (how much text it can handle), and specialised skills (like coding, math, or creative writing). For example, Claude's new Sonnet 3.7 is amazing at churning out bug-free code, perfect for acting like an assistant during tough coding sessions. DeepSeek's R1 aces complex math, scoring near-perfect on hard problems. Gemini pulls fresh web info for up-to-date answers, while LLaMA's open-source flexibility lets coders tweak it for unique projects. Grok's edge is zipping through short, punchy replies on X (f.k.a. Twitter), and Mistral's Mixtral delivers fast results without skimping on quality. Every company's got a roster of models like this, each tuned for specific strengths.

Now that we know there are many players, how do they stack up, and what's the real impact? Let's dive into some numbers…

You've probably seen ChatGPT hailed as the ultimate AI sidekick, and it's true—its models have some serious skills. Recent studies show it's a star at generating human-like text, making it a go-to for drafting emails, brainstorming ideas, or even whipping up code snippets. For instance, one study found that GPT 4.0 achieved an 87% accuracy rate on cardiovascular questions from the Medical Knowledge Self-Assessment Program, outperforming the average trainee score. Another study testing its ability to solve clinical vignettes reported an overall accuracy of 71.7%.

But don't let the hype blind you, GPT-4 and its peers, like Anthropic's Claude 3.5 or Google's Gemini 1.5, aren't flawless. Recent studies show that models like these can have hallucination rates between 17% and 28%. These are moments when the model confidently spits out a "fact" or gives a citation from a paper when, in reality, this fact or source does not exist. Other research papers suggest so-called snowball hallucinations, where initial inaccuracies compound into larger errors later on in the conversation.

Ethical red flags are real too: studies suggest biased training data can lead to skewed outputs, like favouring certain demographics in all kinds of scenarios.

So, while these models are powerful, they're not your trusty oracle, yet.

Now, let's talk about the big picture: AI like ChatGPT is a goldmine waiting to explode. Reports from McKinsey and Goldman Sachs peg generative AI as a $2.6 to $4.4 trillion boost to the global economy every year. The whole AI market's on a rocket ride, ballooning from $279 billion in 2024 to a forecasted $1.8 trillion by 2030, growing at a wild 35.9% clip. Looking ahead, AI is set to shake up everything, healthcare with smarter diagnostics, finance with sharper fraud detection, education with custom learning paths, and even transport with self-driving cars. Creative fields are already buzzing with AI pumping out art, music, and scripts.

Given the immense potential and rapid advancements in AI, it is perhaps understandable why a significant amount of hype surrounds the technology. However, it is crucial to approach the often sensational claims made by some "AI gurus" and in mainstream media with a critical eye. Examples of overhyped capabilities include predictions of AI achieving sentience or "superintelligence" in the near future, the complete replacement of human jobs across all sectors, or the notion that AI can flawlessly solve any problem presented to it. Furthermore, instances of ChatGPT models generating misinformation, fabricating non-existent studies, and even creating convincing "deepfakes" like fake receipts serve as stark reminders that these tools are not infallible.

So, why do we buy into the hype?

Blame it on a mix of psychology and culture. Sci-fi movies have us picturing AI as chatty androids with souls, setting the bar way too high. News outlets don't help, blasting out clickbait about AI's next big thing without the fine print, think "AI cures all diseases!" when it's just a lab demo. AI's complexity trips us up too; its slick outputs feel human, but it's just clever math, not a mind. The term "AI" itself is a vague catch-all, muddying the waters. Then there's automation bias, folks trust AI's answers like gospel because it's fast and shiny, skipping the fact-check. FOMO's another kicker: businesses jump on the AI bandwagon, scared of looking like dinosaurs, even if they don't get the tech. And don't forget the money, companies hype AI to juice stock prices or snag investors, pushing flashy demos over gritty reality.

Bottom line: applications like Chat-GPT and their underlying models are beasts at crunching patterns from massive datasets, spitting out text or code that's scarily good. But they're not thinking. They're predicting, not pondering. The AI wave's real, poised to flip industries like healthcare, finance, and education, with trillions in economic upside. I get it, people are caught up in the AI hype, and that's not always a bad thing; it sparks curiosity and drives innovation. Still, through this blog, I'm here to cut through the fluff, giving you straight-up answers about what AI really is and where it's headed, grounded in clear, no-nonsense insights. Got questions or want to team up to explore AI's potential? Hit me up, I'd love to chat!