Susanna Ray: 10 AI Terms Everyone Should Know

By Fadekemi Ajakaiye 

The term “AI” has been used in computer science since the 1950s, but most people outside the industry didn’t start talking about it until the end of 2022. That’s because recent advances in machine learning led to big breakthroughs that are beginning to have a profound impact on nearly every aspect of our lives.
“We’re here to help break down some of the buzzwords so you can better understand AI terms and be part of the global conversation,” said Susanna Ray, Microsoft Source writer.

1. Large language models
   Large language models, or LLMs, use machine learning techniques to help them process language so they can mimic the way humans communicate. They’re based on neural networks, or NNs, which are computing systems inspired by the human brain — sort of like a bunch of nodes and connections that simulate neurons and synapses. They are trained on a massive amount of text to learn patterns and relationships in language that help them use human words. Their problem-solving capabilities can be used to translate languages, answer questions in the form of a chatbot, summarize text and even write stories, poems and computer code. They don’t have thoughts or feelings, but sometimes they sound like they do, because they’ve learned patterns that help them respond the way a human might. They’re often fine-tuned by developers using a process called reinforcement learning from human feedback (RLHF) to help them sound more conversational.
2. Generative AI
   Generative AI leverages the power of large language models to make new things, not just regurgitate or provide information about existing things. It learns patterns and structures and then generates something that’s similar but new. It can make things like pictures, music, text, videos and code. It can be used to create art, write stories, design products and even help doctors with administrative tasks. But it can also be used by bad actors to create fake news or pictures that look like photographs but aren’t real, so tech companies are working on ways to clearly identify AI-generated content.
3. Hallucinations
   Generative AI systems can create stories, poems and songs, but sometimes we want results to be based in truth. Since these systems can’t tell the difference between what’s real and fake, they can give inaccurate responses that developers refer to as hallucinations or confabulations — much like if someone saw what looked like the outlines of a face on the moon and began saying there was an actual man in the moon. Developers try to resolve these issues through “grounding,” which is when they provide an AI system with additional information from a trusted source to improve accuracy about a specific topic. Sometimes a system’s predictions are wrong, too, if a model doesn’t have current l doesn’t have current information after it’s trained.
4. Responsible AI
   Responsible AI guides people as they try to design systems that are safe and fair — at every level, including the machine learning model, the software, the user interface and the rules and restrictions put in place to access an application. It’s a crucial element because these systems are often tasked with helping make important decisions about people, such as in education and healthcare, but since they’re created by humans and trained on data from an imperfect world, they can reflect any inherent biases. A big part of responsible AI involves understanding the data that was used to train the systems and finding ways to mitigate any shortcomings to help better reflect society at large, not just certain groups of people.
5. Multimodal models
   A multimodal model can work with different types, or modes, of data simultaneously. It can look at pictures, listen to sounds and read words. It’s the ultimate multitasker! It can combine all of this information to do things like answer questions about images.
6. Prompts
   A prompt is an instruction entered into a system in language, images or code that tells the AI what task to perform. Engineers — and really all of us who interact with AI systems — must carefully design prompts to get the desired outcome from the large language models. It’s like placing your order at a deli counter: You don’t just ask for a sandwich, but you specify which bread you want and the type and amounts of condiments, vegetables, cheese and meat to get a lunch that you’ll find delicious and nutritious.
7. Copilots
   A copilot is like a personal assistant that works alongside you in all sorts of digital applications, helping with things like writing, coding, summarizing and searching. It can also help you make decisions and understand lots of data. The recent development of large language models made copilots possible, allowing them to comprehend natural human language and provide answers, create content or take action as you work within different computer programs. Copilots are built with Responsible AI guardrails to make sure they’re safe and secure and are used in a good way. Just like a copilot in an airplane, it’s not in charge — you are — but it’s a tool that can help you be more productive and efficient.
8. Plugins
   Plugins are like relief pitchers in baseball — they step in to fill specific needs that might pop up as the game develops, such as putting in a left-handed pitcher when a left-handed hitter steps up to the plate for a crucial at-bat. Plugins enable AI applications to do more things without having to modify the underlying model. They are what allow copilots to interact with other software and services, for example. They can help AI systems access new information, do complicated math or talk to other programs. They make AI systems more powerful by connecting them to the rest of the digital world.