Human-friendly explanation of AI terminology

Artificial intelligence is the latest thing in technology - it seems like every company is talking about how to make big gains using or developing artificial intelligence. But the field of artificial intelligence is also so full of jargon that it can be difficult to understand what each innovation actually looks like. To help you better understand what happened, we have compiled some of the most common AI expressions. We will do our best to explain their opinion and why they are important. What exactly is artificial intelligence?

Artificial Intelligence: The term "artificial intelligence", often abbreviated to AI, is technically a discipline of computer science dedicated to creating computer systems that can think like humans. But now we mostly hear about artificial intelligence as a technology or even a device, and what exactly that means is hard to pin down. It's also often used as a marketing buzzword, making its definition more fluid than it should be. Google, for example, talks a lot about how it has been investing in artificial intelligence for years. It refers to how many of its products are enhanced by artificial intelligence, and how the company offers tools like Gemini that appear to be intelligent, for example. There are some basic AI models that can provide power for many AI tools, such as Openai's GPT. Then, Mark Zuckerberg, CEO of Yuan, used AI as a noun to refer to a single chat robot. As more companies try to sell artificial intelligence as the next big thing, the way they use the term and other related terms is likely to become more confusing. There are a lot of phrases you might come across in articles or marketing about AI, so to help you understand them better, I've rounded up many of the key AI terms that are currently being widely discussed. But at the end of the day, it's all about making computers smarter.

Machine Learning: Machine learning systems are trained (we'll explain more about training later) to predict new information. That way they can "learn". Machine learning is an area of ​​artificial intelligence and is central to many AI technologies. Artificial General Intelligence (AGI): An artificial intelligence that is as smart or smarter than a human. (OpenAI, in particular, is investing heavily in AGI.) This might be an incredibly powerful technology, but for many people it's also the scariest prospect in the realm of artificial intelligence—just think of all the movies we've seen about superintelligent machines. the world! If that's not enough, there's also work on "superintelligence," or AI, that is much smarter than humans. Generative Artificial Intelligence: Artificial intelligence technology that can generate new text, images, code, etc. Think of all the interesting (if sometimes problematic) answers and images you've seen produced by ChatGPT or Google Gemini. The generated AI tools are motivated by the AI ​​model that usually trains a large amount of data.

Phantom: No, we're not talking about strange visions. It's this: Because generative AI tools are only as good as the data they're trained on, they can "hallucinate," or confidently come up with what they think are the best answers to questions. These hallucinations (or, if you want to be completely honest, bullshit) mean that the systems can make factual errors or give gibberish answers. There is even some debate as to whether the AI ​​illusion can be "fixed".

Bias: Hallucinations aren't the only problem with artificial intelligence—a problem that's perhaps to be expected since AI is ultimately programmed by humans. Therefore, AI tools can be biased based on training data. For example, in 2018, MIT Media Lab computer scientist Joy Buolamwini and Decentralized Artificial Intelligence Research Institute (DAIR) founder and CEO Timnit Gebru co-authored a paper describing how facial recognition software attempts to determine error rates for dark-skinned women.