Whether you realize it or not, artificial intelligence is everywhere. It sits behind the chatbots you talk to online, the playlists you stream and the personalized ads showing up in your scrolling. And now it’s taking on a more public persona. Think Meta AI, which is now embedded in apps like Facebook, Messenger and WhatsApp; or Google’s Gemini, working in the background across the company’s platforms; or Apple Intelligence, rolling out across iPhones now.
AI has a long history, going back to a conference at Dartmouth in 1956 that first discussed artificial intelligence as a thing. Milestones along the way include ELIZA, essentially the first chatbot, developed in 1964 by MIT computer scientist Joseph Weizenbaum, and — skipping ahead 40 years — when Google’s autocomplete feature first appeared in 2004.
Then came 2022 and ChatGPT’s rise to fame. Generative AI developments and product launches have accelerated rapidly since then, including Google Bard (now Gemini), Microsoft Copilot, IBM Watsonx.ai and Meta’s open-source Llama models.
Let’s break down what generative AI is, how it differs from «regular» artificial intelligence and whether gen 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’ve learned. Instead of just analyzing numbers or predicting trends, these systems generate creative outputs like text, images music, videos and software code.
Some of the most popular generative AI tools on the market include:
Foremost among its abilities, ChatGPT can craft human-like conversations or essays based on a few simple prompts. Dall-E and Midjourney create detailed artwork from a short description, while Adobe Firefly focuses on image editing and design.
AI that’s not generative
Not all AI is generative. While gen AI focuses on creating new content, traditional AI excels at analyzing data and making predictions. This includes technologies like image recognition and predictive text. It is also used for novel solutions in:
- Science
- Medical diagnostics
- Weather forecasting
- Fraud detection
- Financial analysis for forecasting and reporting
The AI that beat human grand champions at chess and the board game Go was not generative AI.
These systems might not be as flashy as gen AI, but classic artificial intelligence is a huge part of the technology we rely on every day.
How does gen AI work?
Behind the magic of generative AI are 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 scraped 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’s fed poor-quality data, AI can produce biased results. It’s something that even the biggest players in the field, like Google, haven’t been immune to.
The AI learns patterns, relationships and structures within this data during training. Then, when prompted, it applies that knowledge to generate something new. For instance, if you ask a gen AI tool to write a poem about the ocean, it’s not just pulling prewritten verses from a database. Instead, it’s using what it learned about poetry, oceans and language structure to create a completely original piece.
It’s impressive, but it’s not perfect. Sometimes the results can feel a little off. Maybe the AI misunderstands your request, or it gets overly creative in ways you didn’t expect. It might confidently provide completely false information, and it’s up to you to fact-check it. Those quirks, often called hallucinations, are part of what makes generative AI both fascinating and frustrating.
Generative AI’s capabilities are growing. It can now understand multiple data types by combining technologies like machine learning, natural language processing and computer vision. The result is called multimodal AI that can integrate some combination of text, images, video and speech within a single framework, offering more contextually relevant and accurate responses. ChatGPT’s Advanced Voice Mode is an example, as is Google’s Project Astra.
Challenges with generative AI
There’s no shortage of generative AI tools out there, each with its unique flair. These tools have sparked creativity, but they’ve also raised many questions besides bias and hallucinations — like, who owns the rights to AI-generated content? Or what material is fair game or off-limits for AI companies to use for training their language models —see, for instance, the The New York Times lawsuit against OpenAI and Microsoft.
Other concerns — no small matters — involve privacy, accountability in AI, AI-generated deepfakes and job displacement.
«Writing, animation, photography, illustration, graphic design — AI tools can now handle all of that with surprising ease. But that doesn’t mean these roles will disappear. It may simply mean creatives will need to upskill and use these tools to amplify their own work,» Fang Liu, professor at the University of Notre Dame and co-editor-in-chief of ACM Transactions on Probabilistic Machine Learning, told CNET.
«It also offers a way for people who maybe lack the skill, like someone with a clear vision who can’t draw, but can describe it through a prompt. So no, I don’t think it will disrupt the creative industry. Hopefully, it will be co-creation or augmentation, not replacement.»
Another issue is the impact on the environment because training large AI models uses a lot of energy, leading to big carbon footprints.The rapid ascent of gen AI in the last couple of years has accelerated worries about the risks of AI in general. Governments are ramping up AI regulations to ensure responsible and ethical development, most notably the European Union’s AI Act.
Reception of generative AI
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 gen AI power tools. All that, along with apps for ChatGPT, Claude and other new tools, is putting AI in your hands. And public reaction to generative AI has been mixed. Many users enjoy the convenience and creativity it offers, especially for things like writing help, image creation, homework support and productivity.
Meanwhile, in McKinsey’s 2024 Global AI Survey, 65% of respondents said their organizations regularly use generative AI, nearly double the figure reported just 10 months earlier. Industries like health care and finance are using gen AI to streamline business operations and automate mundane tasks.
As mentioned, there are obvious concerns about ethics, transparency, job losses and the potential for misuse of personal data. Those are the top criticisms behind resistance to accepting generative AI.
And people who use generative AI tools will also find that the results still aren’t good enough much of the time. Despite technological advances, most people can recognize if content has been created using gen AI, whether it’s articles, images or music.
AI has hijacked certain phrases I’ve always used, so I must self-correct my writing often because it might sound like AI. Many articles written by AI contain phrases like «In the era of,» or everything is a «testament to» or a «tapestry of.» AI lacks the emotion and experience that comes with, well, being a human and living life. As one artist on Quora explained, «What AI makes is not the same as art evolving from a thought in a human brain» and it «is not created from the passion found in a human heart.»
Generative AI: Everyday life
Generative AI isn’t just for techies or creative people. Once you get the knack of giving it prompts, it has the potential to do a lot of the legwork for you in a variety of daily tasks.
Let’s say you’re planning a trip. Instead of scrolling through pages of search results, you ask a chatbot to plan your itinerary. Within seconds, you have a detailed plan tailored to your preferences. (That’s the ideal. Please always fact-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 visuals and even ask it to suggest ad copy.
Gen AI is here to stay
There hasn’t been a tech advancement that’s caused such a boom since the internet and, later, the iPhone. Despite its challenges, generative AI is undeniably transformative. It’s making creativity more accessible, helping businesses streamline workflows and even inspiring entirely new ways of thinking and solving problems.
But perhaps what’s most exciting is its potential, and we’re just scratching the surface of what these tools can do.
FAQ
What’s an example of generative AI?
ChatGPT is probably the most popular example of generative AI. You give it a prompt, and it can generate text and images; write code; answer questions; summarize text; draft emails; and much more.
What’s the difference between AI and generative AI?
Generative AI creates new content like text, images or music, while traditional AI analyzes data, recognizing patterns or images and making predictions (for instance, in medicine, science and finance).