Generative AI is changing the digital landscape. Not long ago, computers struggled with creativity, often lagging behind human brilliance. But, things are changing. Imagine posing a request to a generative AI tool, like Bing Image Creator, for a ‘cute blue AI creature with orange eyes’ – and that’s it! The tool conjures up the image effortlessly, without any prior guidance or specific training. The world of creativity has indeed been transformed!
This AI revolution isn’t slowing down. We’re witnessing new developments every few months. An AI image generator, for instance, outsmarted experts to clinch a prestigious photography contest. AI-generated images are even making waves on social media. But do remember, these tools aren’t infallible. They can make mistakes, so we must learn to discern between real and AI-generated content.
How does generative AI work?
It falls under the umbrella of machine learning. The computer gobbles up vast amounts of data, much like how you’d munch on a bag of crisps. The goal is to mimic human tasks. Let’s say you want an AI that can generate new faces. You’d feed it a dataset full of face images. With ample training, the computer learns what a face, nose, eyes, ears, and lips look like, and then moves on to finer details like expressions, facial hair, and skin tones.
However, without enough training, the AI might falter. It’s like trying to bake a cake without knowing all the ingredients. This problem is evident with AI image generators like Midjourney. Experts could quickly spot fictional images of Pope Francis by examining the fingers in the image. If the training data lacks complete fingers, the AI can stumble.
Many modern generative AI tools, including ChatGPT, rely on the Transformer architecture. This allows the AI to focus on relationships within the data, predicting the next word in a sentence. Reinforcement learning is another technique, where a human manually scores the AI’s output to help it improve over time.
Generative AI comes with a mixed bag of benefits and limitations. It can reduce manual labour, increase efficiency, and even excel at decision-making. However, it can also exhibit biases, enable malicious acts, and pose risks to jobs.
Generative AI is leaving its footprints across various fields. Chatbots like ChatGPT and Bing Chat are perfect for research and customer support. AI image generators like Midjourney and DALL-E can convert a few words into art. Google’s WaveNet and OpenAI Jukebox are synthesising speech and creating music. Programmers can even use AI companions like GitHub Copilot or OpenAI Codex to speed up their work.
A few years back, these generative AI tools were merely a dream. Now, they’re reality, and with new breakthroughs almost every week, the future holds endless possibilities.
