The use of artificial intelligence, or AI, has spread like wildfire across the digital landscape, but it may seem too complex for beginners looking to use the tool. There are three basic phrases you should know.

Kennesaw

The release of Open AI's ChatGPT has accelerated the development of artificial intelligence and warnings about its impact on society.

Rise of AI

Artificial Intelligence (AI) has become increasingly prevalent across various digital platforms and applications. The technology, known for its complexity, may appear daunting for those new to utilizing it. However, understanding three essential terms can serve as a foundation for beginners interested in exploring the capabilities of AI.

These terms have not been specified in the provided information, which suggests that a deeper dive into the subject matter is necessary to fully grasp the essential AI terminology. Familiarizing oneself with this basic lingo is a crucial first step towards demystifying the technologies behind AI and unlocking its potential applications.



Understanding basic AI lingo

Well, I was just talking to a professor of English at Kennesaw State University about A.I. and realized this, that she was sprinkling in terms that people in the know use every day now. And I had to stop her and get clarity on what some of this meant. So I thought, well, let's look at the language used in A.I. So when folks are talking about it, we can jump in and we can talk about it, too. We're going to cover three phrases that we need to know to be in the know. Predictive A.I., generative A.I., and finally, prompt engineering. You might know one of these, maybe some of them or none at all. So let's learn the language of artificial intelligence. This is one of the most familiar already because we use it every day. It's predictive A.I. It's on your phone, your email, your computer's writing document. It's what we call autocorrect. Kennesaw State University English professor Jeannie Law explains. It is an algorithm, a mathematical equation, if you will, that basically just predicts what it thinks you want to say. So if I write, instead of A.N.D., I write A.R.D., it's going to flag that and it's going to say, did you mean and? The software predicts speech and grammar patterns and ways of operating that can be easily predicted. What follows that is generative A.I. It goes a step further than prediction. It takes what you type into the system, then combines it with the data knowledge it's collected from all over the web. It's like you and the computer are in a conversation. Then it spits out or generates ideas that it believes you want, whole concepts. So it generates new content instead of just trying to predict what it thinks we want it to do. Here's an example. This is in chat GPT. Linda Law typed into the app a beloved recipe from her grandmother, but she wanted it updated to a more modern day keto set of ingredients. And in seconds, she got what she asked for. You might have to tweak it with a few more prompts, but you'll most likely get what you want. Again, this is generative A.I. It's generating content. Finally, there's prompt engineering, and this is about people like writers inputting the right phrases and words and content into the system to make it work best. A prompt engineer connects the turn of phrase to the worldwide web so that the best version of that updated recipe comes back to you. It's training the system to work optimally. Prompt engineering means different things to different disciplines. So if you're a computer scientist, prompt engineering can be used to help you code. If you are a technical writer, prompt engineering can be used to help you develop a step-by-step guide or an instruction manual. Basically, prompt engineering is a strategy or a tactic that we use when we engage with generative A.I. or gen A.I.

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Rise of AI