Artificial intelligence (AI) doesn’t exist. Well, not in the way most people think. The term “AI” has become a catch-all phrase for various computer science concepts like machine learning, neural networks, and deep learning. When we hear “AI,” we imagine a sentient, decision-making entity. In reality, what we call AI is just a set of tools that can identify patterns and make predictions based on data.
Let’s break it down. Imagine you have 100,000 pictures and want to find all the ones with cats. You’d create an algorithm to sort through these images. First, you’d label a smaller subset of pictures as “cat” or “not a cat.” The algorithm learns from this subset and then applies what it learned to the larger dataset. This is machine learning. It’s not intelligent; it’s just following patterns.
The trouble begins when we mistake these tools for actual intelligence. A human can understand context and nuances, like recognising a stuffed cat as a cat. A machine learning tool can’t. It only knows what it’s been trained to recognise. If it misses something, you can’t just explain the mistake to it. You have to retrain it with new data, which can lead to other errors.
In fields like astrophysics or medical research, machine learning can handle vast amounts of data quickly. For example, astronomers can use it to classify millions of galaxies, and doctors can use it to diagnose diseases from medical images. However, these tools are only as good as the data they’re trained on and always need expert oversight. The machine might identify patterns that aren’t useful or even misleading, like associating older MRI machines with tuberculosis simply because older machines are more common in areas with higher TB rates.
This brings us to a critical point: AI tools are not intelligent, and their outputs are not always reliable. They can lie, make mistakes, and produce biased results. For instance, an AI used to diagnose skin conditions might be less accurate for people of colour if it’s primarily trained on images of white skin. Similarly, a scholarship algorithm might favour applicants from wealthier backgrounds because it’s trained on biased data.
Ethically, using AI to make decisions is fraught with issues. These tools are built on data reflecting our world’s inequalities, and they can perpetuate these biases. Moreover, AI tools are often trained on data that isn’t theirs to use, like books, music, and code created by humans. This raises legal and ethical concerns about ownership and originality.
In practical terms, AI won’t take your job, but it might make your job worse. Companies might replace skilled workers with AI, only to realise the AI can’t do the job well, leading to rehiring at lower wages. This has already happened with translation services and could happen in many other fields.
Moreover, AI-generated products are often subpar. Whether it’s art, music, or writing, AI can produce something that looks or sounds right but lacks depth and quality. This is why AI-generated content often feels soulless and unoriginal.
In summary, AI tools can be useful but are not a replacement for human expertise. They should be used with caution, understanding their limitations and ethical implications. AI doesn’t exist in the way we imagine, and treating it as such can lead to poor decisions and perpetuate existing biases. So, while AI might not take over the world, it could certainly make it a more complicated and less equitable place if we’re not careful.