10 AI terms everyone should know

by chrisdofdof | Jan 9, 2024 | Apple, Main-Story-2

By Susanna Ray, Microsoft Source writer

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.

1. Artificial intelligence
   Artificial intelligence is basically a super-smart computer system that can imitate humans in some ways,
   like comprehending what people say, making decisions, translating between languages, analyzing if
   something is negative or positive, and even learning from experience. It’s artificial in that its intellect
   was created by humans using technology. Sometimes people say AI systems have digital brains, but
   they’re not physical machines or robots — they’re programs that run on computers. They work by
   putting a vast collection of data through algorithms, which are sets of instructions, to create models that
   can automate tasks that typically require human intelligence and time. Sometimes people specifically
   engage with an AI system — like asking Bing Chat for help with something — but more often the AI is
   happening in the background all around us, suggesting words as we type, recommending songs in
   playlists and providing more relevant information based on our preferences.
2. Machine learning
   If artificial intelligence is the goal, machine learning is how we get there. It’s a field of computer science,
   under the umbrella of AI, where people teach a computer system how to do something by training it to
   identify patterns and make predictions based on them. Data is run through algorithms over and over,
   with different input and feedback each time to help the system learn and improve during the training
   process — like practicing piano scales 10 million times in order to sight-read music going forward. It’s
   especially helpful with problems that would otherwise be difficult or impossible to solve using
   traditional programming techniques, such as recognizing images and translating languages. It takes a
   huge amount of data, and that’s something we’ve only been able to harness in recent years as more
   information has been digitized and as computer hardware has become faster, smaller, more powerful
   and better able to process all that information. That’s why large language models that use machine
   learning — such as Bing Chat and ChatGPT — have suddenly arrived on the scene.
3. 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.

4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.