NeuralMatic Consulting

GLOSSARY
Here are some important terms to know when learning about artificial intelligence (AI), explained:

Artificial Intelligence (AI):
Artificial intelligence, or AI, is when machines, like computers, are designed to perform tasks that usually require human intelligence. Instead of just following instructions step by step, like most regular programs, AI can learn, think, and make decisions based on the information it’s given.

For example, when you type a question into a search engine, AI helps understand what you’re asking and finds the best answers. Or think about apps like Siri or Google Maps—AI helps them understand your voice and give you helpful directions.

AI learns by studying patterns in large amounts of data. For instance, if you show it thousands of pictures of cats, it can “learn” what a cat looks like and then recognize cats in new pictures. This ability to learn and improve makes AI useful in many areas, like healthcare, education, entertainment, and even self-driving cars.

In short, AI is like giving computers the ability to think and solve problems in smarter ways, making them more helpful in our daily lives. Does that make sense?

Machine Learning (ML):
Machine learning is a way for computers to learn and improve at tasks by using data and examples, rather than needing detailed instructions for everything. Imagine training a computer like you’d teach someone to recognize different kinds of animals: you show them lots of pictures of animals, explaining which ones are dogs, cats, or birds. Over time, the computer starts to see patterns—like dogs often have floppy ears or cats usually have whiskers.

In machine learning, these “patterns” help computers predict or decide things on their own. For instance, when you upload a photo to social media and it suggests tagging your friends, that’s machine learning in action. It’s finding patterns in faces based on what it’s learned before!

There are three main ways computers learn in machine learning:

Supervised Learning:
Like having a teacher—it learns by studying examples with labels, like showing a photo labeled “dog.”
Unsupervised Learning:
No labels! It figures out patterns by itself, like sorting animals into groups by similarities.
Reinforcement Learning:
Kind of like trial and error—it tries something, gets feedback (a reward or correction), and learns what works best over time.

People use machine learning for all kinds of things—helping doctors spot diseases, teaching cars to drive themselves, or even improving the recommendations you see on Netflix. What about it interests you?

Algorithm:
An algorithm is a set of step-by-step instructions or rules that a computer (or even a person) follows to solve a problem or complete a task. It’s like a recipe: just like a recipe tells you how to bake a cake by listing the steps in order, an algorithm tells a computer how to accomplish a specific goal.  For example, imagine you want to sort a deck of cards from smallest to largest. An algorithm for that task could be:

1. Look at two cards at a time.
2. If one card is bigger, swap them so they’re in the right order.
3. Move to the next pair of cards and repeat.
4. Keep going through the deck until all the cards are in the correct order.

In computers, algorithms are everywhere. They help decide what video to recommend on YouTube, how to calculate the fastest route on Google Maps, or even how to recognize faces in photos. While some algorithms are simple, others can be very complex, depending on the task.  Does that make sense? Algorithms are the building blocks of how technology solves problems!

Neural Network:
Neural networks are a type of computer system designed to mimic how the human brain works when it processes information. They’re used in artificial intelligence (AI) to help machines recognize patterns, make predictions, and solve problems.  Think of neural networks as a web of interconnected “nodes,” which act like tiny decision-makers. These nodes are organized in layers:

Input Layer:
This is where the data enters the network, like a picture or a sentence.
Hidden Layers:
These layers process the information. Each node analyzes part of the input, passes it along, and adjusts based on feedback. They “learn” by finding patterns and connections in the data.
Output Layer:
This is where the final result comes out like the network identifying whether a picture shows a cat or a dog.

For example, if a neural network is trained to recognize handwritten numbers, it looks at each example (like the number “5”), analyzes the shapes and strokes, and learns to identify the number correctly.  The process is powered by lots of math behind the scenes—like weights, biases, and activation functions—which help the network decide which connections are most important. Over time, with more data and training, neural networks become better at understanding and predicting complex things, such as recognizing faces, translating languages, or even driving cars.

Natural Language Processing (NLP):
Natural Language Processing (NLP) is a field within artificial intelligence that focuses on helping computers understand, interpret, and respond to human language. It bridges the gap between how people communicate (using words, sentences, and context) and how machines process information (using numbers and data).

Here’s an example: when you talk to a virtual assistant like Siri or Alexa, NLP is the technology that allows it to understand your speech, figure out what you’re asking, and provide the correct response. It can handle tasks like translating languages, recognizing speech, summarizing text, or even detecting emotions in written messages.  NLP works by breaking language down into smaller pieces—like words or phrases—and using algorithms to analyze their meaning. This includes understanding things like:

Grammar:
The structure of sentences.
Context:
The meaning behind the words is based on how they’re used.
Ambiguity:
Figuring out when words have multiple meanings and choosing the correct one.

For instance, if you say, “Can you book a ticket for me?” NLP helps the machine figure out that you’re asking to schedule a reservation—not reading a book!  It’s the reason why chatbots like me can have conversations with you and why translation apps work so smoothly. Cool, right? Let me know if you want examples of how it’s used in everyday life!

Training Data:
Training data is the information that is used to teach an artificial intelligence (AI) system how to perform a specific task. Think of it like study material for an exam—the AI learns from the training data, recognizing patterns and making connections, so it can use that knowledge to make decisions or predictions later.

For example, imagine you’re teaching a computer to recognize dogs in photos. The training data would include lots of pictures of dogs, along with labels saying, “This is a dog.” By studying these labeled examples, the AI learns what features—like fur, ears, and tails—are common in dogs. The more high-quality training data the AI has, the better it becomes at understanding what makes a dog a dog.

Training data doesn’t have to be just images. It can be text, numbers, audio, or anything else depending on the task. For example:
For predicting the weather, training data might include years of temperature, humidity, and wind speed records.
For translating languages, it could be large sets of texts in different languages.

The accuracy and fairness of AI systems depend heavily on the quality and diversity of the training data. If the data is flawed or biased, the AI might not perform well or could even make unfair decisions. It’s why careful preparation of training data is such an important step in building AI systems.

Deep Learning:
Deep learning is a type of machine learning, which is a part of artificial intelligence (AI). What makes deep learning special is that it uses structures called “neural networks”, designed to mimic how the human brain processes information.  Imagine you’re teaching a computer to recognize handwritten numbers, like the digits 0 through 9. In deep learning, the computer uses layers of interconnected nodes (called neurons) to analyze the data. Here’s how it works:

Input Layer:
The computer receives the data, like an image of a number.
Hidden Layers:
These layers are where the magic happens. Each layer processes parts of the data, looking for patterns like shapes or strokes. The deeper the network (more layers), the better it can handle complex tasks.
Output Layer:
The final answer comes out, such as “This number is a 7!”

The key to deep learning is that the computer learns by adjusting connections in the network based on errors it makes. For example, if it mistakenly identifies a “3” as an “8,” it tweaks its internal connections to get closer to the right answer the next time.  Deep learning is powerful because it can handle massive amounts of data and uncover patterns humans might miss. It’s used in technologies like facial recognition, self-driving cars, and translating languages. Over time, deep learning systems become incredibly accurate as they process more and more data.

Bias:
Bias in artificial intelligence (AI) happens when an AI system makes unfair or inaccurate decisions because the data it was trained on is unbalanced or flawed. This can lead to unintended favoritism or discrimination.

Here’s an example: Imagine you’re building an AI system to identify job candidates who are most qualified. If the training data only includes successful people from one group, like men, the AI might “learn” to think men are better candidates, even though that’s not true. This is bias, and it occurs because the data doesn’t fully represent everyone.  Bias can come from:
Biased Training Data:
If the data used to train the AI reflects stereotypes or excludes certain groups.
Human Input:
If people designing the AI unintentionally create rules or patterns that favor one group over another.
Unequal Representation:
If certain groups are underrepresented in the training data, making the AI less effective for those groups.

The goal is to reduce bias by using diverse, high-quality training data and designing systems that are fair and inclusive. Bias isn’t always easy to spot, but addressing it is important to make sure AI is ethical and treats everyone fairly. Does this explanation resonate with you?

Hallucination:
AI hallucination refers to situations when an AI system produces incorrect, nonsensical, or made-up information that is presented as if it were valid. It’s like the AI “imagining” something that isn’t accurate or based on reality. For example, an AI might generate a fact that doesn’t exist or make a claim that sounds plausible but is completely untrue.

Hallucinations can happen because AI models rely on patterns and probabilities in data rather than true understanding. If the AI doesn’t have enough context or encounters ambiguous data, it may “fill in the gaps” incorrectly. Reducing AI hallucinations is a major focus in improving the accuracy and reliability of these systems.

Prompt Engineering:
Prompt engineering is the process of designing and refining the input (or “prompt”) that you give to an artificial intelligence (AI) system so it generates the best possible output. Think of it as crafting the perfect question or instruction to get the most accurate, helpful, or creative answer from an AI.  Here’s an example: Imagine you’re using an AI to write a poem. If you just say, “Write a poem,” the AI might give you something very general. But if you say, “Write a funny poem about a dog chasing a robot in space,” you’re being more specific. This extra detail helps the AI understand exactly what you want, leading to a better result.

In prompt engineering, people experiment with different ways of phrasing or structuring their prompts to guide the AI’s response. This is especially important for tasks like summarizing information, answering technical questions, or generating creative content.  It’s a bit like giving directions—the clearer and more detailed you are, the more likely you are to get where you want to go. Understanding how to “talk to” AI in this way can make it much easier to work with and get great results!

Token:
In the world of artificial intelligence (AI) and computers, a “token” is like a tiny piece of information that a computer processes when working with text or language. You can think of tokens as building blocks that make up sentences and phrases.

For example, if you have a sentence like, “I love pizza,” the AI might break it into tokens, such as individual words: “I,” “love,” and “pizza.” Sometimes, tokens might even be smaller than words, like parts of a word or even single letters, depending on how the AI system is designed. For more complex tasks, like understanding languages, the tokens help the AI analyze and make sense of the text.  In short, a token is a way to represent parts of language so a computer can process and understand them better. Breaking things into tokens is a key step for teaching AI how to work with text, whether it’s answering questions, translating languages, or even chatting with you!