What is generative AI? Everything you need to know about the technology behind ChatGPT and Gemini

Artificial intelligence is everywhere, whether you realize it or not. It’s behind the chatbots you talk to online, the playlists you stream, and the personalized ads that somehow know exactly what you crave. Now it’s taking on a more public persona: think Meta AI, which appears in apps like Facebook, Messenger and WhatsApp; or Google’s Gemini, which works in the background on the company’s platforms; or Apple Intelligence, which is only now slowly being rolled out.

AI has a long history, dating back to a conference at Dartmouth in 1956 where artificial intelligence was first discussed as a thing. Milestones along the way include ELIZA, essentially the first chatbot, developed in 1964 by MIT computer scientist Joseph Weizenbaum, and 2004, when Google’s autocomplete first appeared.

Then 2022 came and ChatGPT became famous. Generative AI developments and product launches have rapidly accelerated since then, including Google Bard (now Gemini), Microsoft Copilot, IBM Watsonx.ai and Meta’s open-source Llama models.

Let’s take a look at what generative AI is, how it differs from ‘regular’ artificial intelligence, and whether gene AI can live up to the hype.

Generative AI in a nutshell

At its core, generative AI refers to artificial intelligence systems that are designed to produce new content based on patterns and data they have learned. Instead of just analyzing numbers or predicting trends, these systems generate creative output such as text, images, music, videos and software code.

Some of the most popular generative AI tools on the market include ChatGPT, Dall-E, Midjourney, Adobe Firefly, Claude, and Stable Diffusion.

The most important of its capabilities is that ChatGPT can create human-like conversations or essays based on a few simple prompts. Dall-E and Midjourney create detailed illustrations based on a short description, while Adobe Firefly focuses on image editing and design.

The AI ​​that is not generative AI

However, not all AI is generative. While generational AI focuses on creating new content, traditional AI excels at analyzing data and making predictions. This includes technologies such as image recognition and predictive text. It is also used for new solutions in science, medical diagnostics, weather forecasting, fraud detection and financial analytics for forecasting and reporting. The AI ​​that defeated human great champions to play chess and the board game Go was not generative AI.

These systems may not be as flashy as generation AI, but classic artificial intelligence is a big part of the technology we rely on every day.

How generative AI works

Behind the magic of generative AI lie large language models and advanced machine learning techniques. These systems are trained on massive amounts of data, such as entire libraries of books, millions of images, years of recorded music, and data pulled from the Internet.

AI developers, from tech giants to startups, are well aware that AI is only as good as the data you feed it. If it receives poor quality data, AI can produce biased results. It’s something that even the biggest players in this space, like Google, haven’t been immune to.

The AI ​​learns patterns, relationships and structures within this data during training. When asked, it applies that knowledge to generate something new. For example, if you ask a gen AI tool to write a poem about the ocean, it won’t just pull pre-written verses from a database. Instead, it uses what it has learned about poetry, oceans, and language structure to create a completely original piece.

It’s impressive, but not perfect. Sometimes the results can feel a little off. Maybe the AI ​​misunderstands your request, or gets too creative in ways you didn’t expect. It can confidently provide completely false information, and it is up to you to check the facts. These quirks, often called hallucinations, are part of what makes generative AI both fascinating and frustrating.

The possibilities of generative AI are growing. It can now understand multiple data types by combining technologies such as machine learning, natural language processing and computer vision. The result is called multimodal AI, which can integrate a combination of text, images, video and speech within a single framework, providing more contextually relevant and accurate answers. ChatGPT’s advanced voice mode is an example, as is Google’s Project Astra.

Gen AI brings challenges

There is no shortage of generative AI tools, each with its unique flair. These tools have sparked creativity, but in addition to prejudices and hallucinations, they have also raised many questions, such as: who owns the rights to AI-generated content? Or what material is or is not allowed for AI companies to use for training their language models – see for example the The New York Times lawsuit against OpenAI and Microsoft.

Other concerns – not minor ones – have to do with privacy, job losses, AI responsibility and AI-generated deepfakes. Another problem is the impact on the environment, as training large AI models takes a lot of energy, leading to large ecological footprints.

The rapid rise of generational AI in recent years has accelerated concerns about the risks of AI in general. That’s what governments are stepping up AI regulations to ensure responsible and ethical development, especially that of the European Union AI law.

Generative AI in everyday life

Many people have interacted with chatbots in customer service or used virtual assistants like Siri, Alexa and Google Assistant – which are now on the cusp of becoming generational AI power tools. That, along with apps for ChatGPT, Claude and other new tools, puts AI in your hands.

Meanwhile, according to McKinsey’s 2024 Global AI Survey65% of respondents said their organizations regularly use generative AI, almost double the figure reported just ten months earlier. Industries like healthcare and finance are using generational AI to streamline business operations and automate mundane tasks.

Generative AI is not just for techies or creative people. Once you master the gift of giving directions, it has the potential to do much of the legwork for you in a variety of everyday tasks. Suppose you are planning a trip. Instead of scrolling through pages of search results, ask a chatbot to plan your route. Within seconds you will have a detailed plan tailored to your preferences. (That’s the ideal. Always check its recommendations.) A small business owner who needs a marketing campaign but doesn’t have a design team can use generative AI to create eye-catching images and even ask them to suggest ad copy.

Generative AI is here to stay

Not since the internet and later the iPhone has there been a technological advancement that has caused such a boom. Despite the challenges, generative AI is undeniably transformative. It makes creativity more accessible, helps companies streamline workflows, and even inspires entirely new ways of thinking and problem solving.

But perhaps what’s most exciting is its potential, and we’re just scratching the surface of what these tools can do.