AI Terms for the Non-Tech | Rodrigo Blanco

The only AI Glossary of Terms designed for the Non Tech

I’ve read all of them, studied all of them and they all gave me a headache. Here’s a better glossary of AI terms that all Business owners, content creators, solopreneurs, influencers and Non-Tech people can actually use and learn from.

Accelerator: This is like a special device that makes your AI applications run faster. It’s like a powerful engine in a sports car, speeding up how quickly your AI can think and process data. You’d use it to save time when running complex AI models or processes.

Active Learning: Imagine your AI is a curious student who doesn’t just passively accept information but actively asks you questions to better understand and improve. This helps your AI adapt and refine its knowledge, making it more effective over time.

Algorithm: Just like a recipe has steps to follow to make a dish, an algorithm is a set of instructions that an AI follows to solve a specific problem or task. It can be simple or complex, depending on what the task is, but it’s a critical part of how AI gets things done.

Agents: Think of agents like little virtual assistants. They can do tasks on their own, without you constantly telling them what to do. For instance, they could be browsing the internet to gather data for your new online course, or they could automatically manage the tedious parts of running an online business, like calculating taxes or scheduling social media posts.

AGI (Artificial General Intelligence): AGI is like the ‘holy grail’ of AI – a machine that’s as smart as a human in every way. Right now, most AI excels in specific tasks but isn’t very adaptable. AGI would be capable of learning and understanding anything a human could, from running your online business to creating new content for your social media.

Alignment: Alignment in AI is all about making sure an AI’s goals match ours. Like ensuring a new hire in your business understands and works towards your company’s mission, AI alignment is the process of teaching AI to understand and value human goals, ethics, and safety.

Artificial Intelligence (AI): In broad terms, AI is like your virtual assistant that can do everything from following specific rules to learning from experiences, all in an attempt to mimic human intelligence. It’s the backbone of many of the digital services and tools you use today.

ASI (Artificial Super Intelligence): If AGI is a machine as smart as a human, ASI is a machine that’s even smarter. An ASI could potentially out-think the best human brains in practically every field, from business strategy to content creation. Imagine an AI that could run your business more efficiently than any human, or create viral content better than any influencer.

Attention: Attention in AI is like spotlight in a theatre. Just as a director might use a spotlight to focus the audience’s attention on a particular actor, an AI uses attention to focus on important parts of the data when making decisions. For example, when writing an email, an AI with attention will focus on the relevant parts of your draft to help compose the final version.

AutoML (Automated Machine Learning): Picture a super helpful tool that handles all the complex parts of AI for you. Whether you’re a small business owner, a content creator, or a digital marketer, with AutoML, you can use machine learning models without needing to become an expert in the field.

Back Propagation: This is a fancy name for a process AI models use to learn from mistakes. When an AI makes a prediction and gets it wrong, back propagation is like a feedback mechanism, allowing the AI to adjust its ‘thinking process’ so it can do better next time. It’s like tweaking your content creation strategy based on what posts perform well.

Bag of Words: It’s as if the AI has a bag where it puts all the words used in a piece of text without caring about the order or grammar. This technique is useful when AI needs to understand or categorize text, such as for content analysis or customer feedback.

Bayesian Networks: Imagine the AI trying to draw connections between different data points, a bit like creating a family tree but for data. It’s a statistical technique that allows AI to make educated guesses about relationships and influences within a dataset.

Bias: AI models can make assumptions based on the data they’re trained on, just like a new team member might make assumptions based on their past experiences. If the AI’s assumptions are off or unfair, it can lead to skewed or discriminatory results. For example, an AI developed to curate content might ignore certain types of posts if it’s biased against them.

Bias-Variance Tradeoff: It’s like the art of balance in AI. The AI has to find the sweet spot between being too rigid and not learning enough (bias), and being too flexible and over-reacting to small changes (variance) for an optimal model performance.

Chain of Thought: In AI, this is like the AI’s line of reasoning. It’s how the AI gets from the information it’s given to the decision it makes. For an AI making content recommendations, the chain of thought might be the series of steps it took to decide that a particular video or article would interest you.

Chatbot: Chatbots are like virtual customer service reps. They’re computer programs that can talk to humans via text or voice, answer questions, and help with various tasks. If you’ve ever interacted with a customer support bot on a website, you’ve talked to a chatbot. They’re great for answering frequently asked questions, booking appointments, or providing quick support for your customers.

ChatGPT: This is an AI developed by OpenAI that’s really good at generating human-like text. It’s like a ghostwriter that can help draft articles, write engaging marketing copy, or even generate ideas for new content.

CLIP (Contrastive Language–Image Pretraining): CLIP is an AI model that’s great at understanding both text and images. Think of it like a creative director who can come up with descriptions of pictures for your Instagram posts or generate ideas for graphics based on your blog posts.

Compute: Compute is basically how much computer power you’re using to train or run your AI models. It’s like the electricity your business uses – the bigger and more complex your operations, the more compute you’ll need.

Convolutional Neural Network (CNN): CNNs are a type of AI that’s excellent at analyzing images. They can recognize patterns and features in images just like a human would. If you’re an online fashion retailer, a CNN could help you sort and categorize your inventory by identifying the colors, patterns, and styles in your product photos.

Cross-validation: It’s like a rehearsal for your AI model. By training and testing on different parts of your data, cross-validation helps you estimate how well your AI model will perform on unseen data.

Data Augmentation: This is a way to make your AI models smarter by giving them more data to learn from. It’s like using different camera angles and lighting to help a fashion photographer learn how to capture a dress. You create variations of your existing data – like rotating an image or changing its brightness – to help your AI understand the data from all angles.

Data Labeling: It’s like putting tags on your data so your AI knows what it’s looking at. This is essential for supervised learning, where the AI learns from examples to make future predictions or classifications. ( this is a cat, this is not a cat type of thing )

Data Mining: It’s like the AI is a detective sifting through piles of data, searching for patterns and clues. This process is essential for preparing data for machine learning and can yield valuable insights.

Deep Learning: Deep learning is a type of AI that’s like a very deep well of knowledge. It’s a form of machine learning that allows AI to learn complex patterns and make decisions based on them. It’s like having an expert who can understand the nuances of your customers’ behaviors and preferences to tailor their online shopping experience.

Diffusion: In AI, diffusion is a technique to generate new data from existing ones. It’s like taking a picture of a product and using digital effects to create different versions of it. This can be useful if you want to visualize how a product might look under different conditions, like different lighting or angles.

Double Descent: Double Descent is like the rollercoaster ride of AI training. As you make the model more complex, its performance initially improves, but then it starts making more mistakes, but after a certain point, it starts improving again. It’s like trying out more and more complex marketing strategies – at first, they work better, but then they become too complex to manage effectively until you learn how to handle the complexity.

Embedding: In AI, embedding is like translating data into a language that the AI understands. It’s like creating a digital fingerprint for each product in your online store, so your AI can understand and compare them. Products that are similar will have similar ‘fingerprints’.

Emergence/Emergent Behavior (“sharp left turns,” intelligence explosions): Emergence is when simple AI rules result in complex behavior. It’s like teaching an AI to analyze color and style in clothing, but it starts also recognizing fashion trends. “Sharp left turns” or “intelligence explosions” are scenarios where AI suddenly improves or changes in a big way – like if your recommendation AI suddenly became really good at predicting fashion trends.

End-to-End Learning: This is like giving your AI the raw materials and letting it learn how to build the final product. Rather than you manually picking out the features you want it to focus on, end-to-end learning lets the AI figure out what’s important. It’s like giving an AI access to your raw sales data and letting it figure out what factors influence customer purchases.

Expert Systems: These are like specialized AI consultants. They’re applications that can solve complex problems within a specific domain, like diagnosing car issues or predicting real estate prices. If you’re an online coach, you might use an expert system to analyze your clients’ progress and provide personalized advice.

Explainable AI (XAI): This is an area of AI that focuses on making AI decisions understandable to humans. For online business entrepreneurs, imagine if your email marketing tool started sending out specific campaign emails to a particular group of subscribers, and it actually increased sales. XAI would help you understand why the AI chose that group and why that campaign, thus giving you insights you can use in your business strategies. ( Not to be confused with Elon Musk’s new xAI company )

Ensemble Learning: Think of it as a team of AIs working together to solve a problem. Each one is trained to do the same task, and their outputs are combined to provide a better and more robust result.

Fine-tuning: This process involves tweaking an AI model that’s already been trained on a vast amount of data (like recognizing images or understanding text) to perform a more specific task (like recognizing your brand logo in images or understanding customer complaints in your support emails). Think of it as tailoring an off-the-rack suit to fit you perfectly.

Forward Propagation: In neural networks, this is like the domino effect. It starts with the input data (the first domino), which is then passed through different layers of the network (dominoes falling in sequence) until the output is produced (the last domino falls). Each step involves multiplication of inputs with weights and addition of bias, and an activation function is applied to this result.

Foundation Model: Foundation models are like multi-purpose Swiss army knives of AI. They’re trained on a broad range of data and can be fine-tuned to perform specific tasks. For an online course creator, a foundation model could be fine-tuned to summarize course content, generate quiz questions, or even answer student inquiries.

Federated Learning: Imagine a group of AIs, each with its own local data, collectively learning by sharing what they learn while keeping the original data private. This approach is great for privacy-preserving and efficient learning.

Feature Extraction: It’s like the AI’s way of breaking down raw data to understand its key characteristics. These extracted features help the AI make sense of the data and make better predictions.

General Adversarial Network (GAN): Imagine two artists: one trying to create perfect forgeries of famous paintings (the “generator”), and the other trying to spot the fakes (the “discriminator”). They’re in a constant battle, both getting better over time. That’s what a GAN does but with data. For an influencer, a GAN might be used to create new outfit images based on their previous fashion posts.

Generative AI: This branch of AI can create new, original content. If you’re a content creator, imagine having an AI that could generate blog topics similar to your existing ones, or create music tracks based on your preferred style. Or create all your social media content images. That’s what generative AI can do. We’re talking about things like ChatGPT, Midjourney, DALL-E2, Stable Diffusion, etc…

GPT (Generative Pretrained Transformer): GPT is like an AI author that can write human-like text. If you’re an online entrepreneur, you could use GPT to draft marketing emails, write product descriptions, or generate engaging social media posts.

GPU (Graphics Processing Unit): This is a piece of hardware designed to handle heavy computations, such as those required to train and run AI models. If you’re a content creator, you might not interact directly with GPUs, but know that they’re behind the scenes, powering the AI tools you use.

Gradient Descent: Imagine you’re trying to find the lowest point in a valley but can only see a few feet around you. You’d probably take steps downwards and adjust your direction based on the slope. Gradient descent does the same thing but with numbers, helping machine learning models improve their predictions.

Hallucinate/Hallucination: In AI terms, hallucination means the AI is creating things that aren’t based on the actual data. For instance, an AI model might “hallucinate” that an image of a cat has a hat on it, even though there’s no hat in the original picture. This also happens in Text, so for example, ChatGPT could tell you something that sounds totally plausible and confident, yet is completely wrong, or does not even exist.

Hidden Layer: When training a machine learning model, the hidden layers are like the “brain’s” processing center. They take in the input, process it, and pass on the result, helping to convert input data (like a user’s previous purchases) into output (like a recommendation for a new product).

Hyperparameter Tuning: Think of hyperparameters as the settings or controls of a machine learning model, and tuning as adjusting those settings to get the best performance. It’s like adjusting the settings on your camera to get the best photo, but in this case, the aim is to improve the model’s predictions.

Inference: This is the process where a trained machine learning model uses what it’s learned to make predictions. For example, if you’re using an AI to predict which email subscribers will buy your new product, inference is the process of the AI making those predictions.

Instruction Tuning: A bit like a teacher giving a student specific instructions to improve their performance on a task, instruction tuning involves fine-tuning machine learning models based on specific instructions given in the dataset.

Jacobian Matrix: Think of this as a tool that tells you how tiny tweaks to your online business inputs (like advertising spend, pricing changes, or post frequency) can influence your outcomes (like sales or user engagement). It’s like a super detailed road map, letting you see how slight turns (changes) could affect your destination (results).

Joint Probability: Imagine trying to figure out how likely it is that someone who reads your blog will also buy your online course. That’s joint probability – it’s about finding out how likely two (or more) things are to happen together.

JPEG (Joint Photographic Experts Group): You’ve probably saved many images in this format. JPEG is a common type of file used for images in digital marketing. It compresses image data, making the files smaller so they’re easier to share and quicker to load on your website.

Just-In-Time Compilation: It’s like having an interpreter who translates your instructions into language a computer can understand, but only does it right before the computer needs to act. This can make things run faster and smoother, like your website or mobile app.

Kernel Method: Imagine you’re trying to separate blue and red balls mixed in a bucket, but it’s really hard to do so while they’re in the bucket. But if you pour them out on a wide floor, separating them becomes easier. That’s basically what kernel methods do: they make complex problems easier by expanding them in a higher space.

K-Means Clustering: Imagine you have a mixed pile of red, blue, and yellow balls and you want to separate them by color. That’s what K-means clustering does, but with data. For example, it can group your customers based on their shopping behavior.

Key Performance Indicator (KPI): KPIs are like your business’s health check. They’re specific metrics that help you track progress towards your business goals, like number of new subscribers, revenue growth, or your website’s bounce rate.

Knowledge Graph: It’s like a massive web of related facts. Google uses one to improve search results. If you’re using SEO in your content marketing, you’re already interacting with Google’s Knowledge Graph.

Kernel Density Estimation (KDE): It’s like creating a smooth curve for your data. KDE helps you understand the distribution of your data. For instance, you could use it to understand the distribution of visit times on your website or age distribution of your customers.

Large Language Model (LLM): This is an AI model, like GPT-3 or GPT-4, that can generate text that sounds like it was written by a human. These are the models that power AI content generation tools that you can use to create blog posts, social media updates, and more.

Latent Space: Think of it as a “conceptual space” that the AI creates. In this space, similar things are closer together, and different things are farther apart. If you’re a course creator, an AI might put all your students who learn at a similar pace in the same area of latent space.

Loss Function (or Cost Function): This is a way to measure how well a machine learning model is doing during training. It’s a bit like a scorecard, and the goal is to get the score (the “loss”) as low as possible. If the AI is helping you to predict which of your followers will buy your product, the loss function might measure how far off the AI’s predictions are from the actual outcomes.

Machine Learning: This is a type of AI that can learn and improve from experience, without being explicitly programmed. So instead of needing to manually code every single rule or decision the AI should make, the machine learning model can learn these things from data.

Mixture of Experts: This is a technique where several specialized models (the “experts”) are trained, and their predictions are combined. Imagine having a team of experts who all have different skills – one is great at predicting which of your blog posts will do well on social media, another is good at predicting email open rates, and so on. By taking all of their advice into account, you can get a more accurate overall prediction.

Multimodal: In AI, this refers to models that can understand different types of data, such as text and images. For an influencer, a multimodal AI could help understand both the text and images in their posts to optimize their content.

Natural Language Processing (NLP): This is a branch of AI that helps computers understand and interact with human language. It’s like teaching your computer to understand English (or any other language), so it can read comments on your blog, understand the sentiment behind them, and even generate responses.

NeRF (Neural Radiance Fields): This is a way to create 3D scenes from 2D images using AI. Imagine being able to take your product photos and use them to create a 3D version that your customers can interact with online. That’s the kind of thing NeRF can help with.

Neural Network: This is a type of AI model that’s inspired by how the human brain works. Like how the brain is made up of connected neurons, a neural network has connected nodes that take in data, do some calculations, and produce an output. For instance, a neural network might take the text of your blog post as input and output a prediction of how many shares it will get on social media.

Objective Function: This is a function that the AI tries to maximize or minimize during training. For example, if you’re training an AI to maximize your email open rates, the objective function might measure how well the AI’s suggested subject lines perform.

Overfitting: This happens when an AI model learns from your data too well, to the point that it starts to pick up on the random noise or exceptions rather than the general trend. It’s like if you taught your AI to write blog posts based only on your most successful post ever, it might struggle to write on different topics or styles.

Parameters: These are the internal variables that the model learns from your data to make its predictions. In the blog post example, the parameters might be the common words, length, or style that determine how many shares a post gets.

Pre-training: This is the first stage of training a machine learning model. It’s like giving your AI a general education before specializing in a specific task. For instance, you might first train your AI on a bunch of blog posts from all over the web before fine-tuning it to write in your specific brand’s voice.

Prompt: This is the initial instruction or context that you give to the AI model. Think of it as the topic or question you want your AI to write a blog post or social media update about.

Quantum Computing: This is the next generation of computers which are still being developed. They promise to solve complex problems incredibly quickly. For example, in the future, it might help you optimise your ad campaigns, suggest the best time to launch your new online course, or analyse all the trends in your field in a flash.

Q-Learning: Imagine playing a video game where you get coins for making good moves. Over time, you learn what actions earn you coins. Q-Learning is the same, it’s a way for computers to learn what actions will get the best results. It could, for instance, learn the best times to post content to get the highest engagement.

Query Language: This is a type of computer language used to retrieve specific information from databases. For example, SQL (Structured Query Language) can help you get specific customer information from your business database.

Quantization: Imagine you’re painting a picture but you only have 10 colors to work with. You have to simplify and approximate. Quantization in AI is like that – it simplifies complex data to make it more manageable, at the cost of some accuracy.

Qubit: In the future world of quantum computing, the basic unit of information is the qubit. It’s like a super-powered version of the bits used in traditional computing, because while a bit can be a 0 or 1, a qubit can be both at the same time, allowing for more complex computations.

Regularization: This is a technique used to prevent overfitting. It’s like giving a penalty to your AI for overcomplicating things when writing a blog post. If it’s using long, obscure words when simpler ones would do, regularization helps it learn to keep things simpler and more understandable.

Reinforcement Learning: This is a type of machine learning where an AI learns by trial and error. It’s like teaching your AI to schedule posts at the best time by rewarding it when it picks a good time and not rewarding it when it picks a bad time.

RLHF (Reinforcement Learning from Human Feedback): This is a method where your AI learns from the feedback given by humans. It’s like having an editor who corrects your AI’s blog post drafts until they learn to get them right the first time.

Singularity: This is a hypothetical future point where AI becomes so advanced that it leads to rapid, unprecedented changes. It’s like imagining a future where your AI could fully run your online business without any human intervention.

Supervised Learning: This is a type of machine learning where your AI learns from examples that are labeled. It’s like showing your AI various types of social media posts and telling it which ones are good and which ones are bad, so it learns to create good ones.

Symbolic Artificial Intelligence: This is a type of AI that uses symbols and rules to solve problems. It’s like teaching your AI to recognize “sale” or “discount” symbols in competitor’s ads to analyze pricing strategies.

TensorFlow: This is a tool developed by Google that AI developers use to create AI models. For you as an online entrepreneur, it’s the behind-the-scenes magic that makes your AI work.

TPU (Tensor Processing Unit): This is a piece of hardware developed by Google that’s really good at performing the calculations needed for AI. It’s like a super-powered calculator that helps your AI process data and make decisions faster.

Training Data: This is the information that your AI learns from. It’s like the guidebook that your AI uses to learn how to write engaging blog posts or automate tasks for your business.

Transfer Learning: This is a method in machine learning where a pre-trained model is used on a new problem. It’s like hiring a new employee who already has general skills in digital marketing, and then teaching them the specifics of marketing your online course.

Transformer: A type of AI model, like GPT, known for handling sequential data such as text. Think of it as the writer in your AI tool, capable of creating articles, scripts, or even poetry.

Underfitting: This is when your AI model doesn’t learn enough from your data. If your AI isn’t catching the nuance in your product reviews, for example, it might be underfitting.

Unsupervised Learning: A type of machine learning where the AI finds patterns in the data on its own, without being explicitly told what to look for. It’s like letting your AI roam free in a field of customer data, finding interesting patterns that you hadn’t even thought to look for.

Validation Data: This is a subset of your data that you set aside to test how well your AI is learning. It’s like the pop quizzes you give your AI to make sure it’s really understanding your customers.

Weight Decay: This is like a coach reminding an overzealous team player to slow down and think before taking action. In the context of AI, it helps ensure that the system doesn’t put too much emphasis on any single input (like one type of ad or a particular social media platform), leading to a more balanced and effective strategy.

Word Embedding: This technique is like translating human language into a secret code that computers can understand easily. It allows a machine learning system to understand the context of words in your blog posts, social media updates, or product descriptions, helping things like search engine optimization and automated content tagging.

Wide and Deep Learning: It’s a machine learning technique that can help you get the best of both worlds – remember all the details of your data (going “deep”) while not forgetting about the big picture (going “wide”). For instance, it can help your recommendation system to suggest products that a customer hasn’t bought before but might be interested in.

Whitening: In AI, whitening is a data pre-processing step that can make it easier for a machine learning model to learn. It’s a bit like tidying up your workspace before you start work – by making the data cleaner and more organized, the model can work more efficiently.

Weight Initialization: It’s like setting up the starting blocks for a race. In machine learning, it helps to start training the model from a good place, so the training process is more efficient and leads to better results. For instance, better initialization could result in your AI customer service chatbot learning to handle queries faster.

XAI (Explainable AI): This is a branch of AI that focuses on making AI decisions understandable to humans. It’s like having a transparent AI employee who can clearly explain why they made a particular recommendation or decision.

XGBoost: This is a powerful tool that allows machines to learn from data more effectively. It’s like having a super-smart business analyst who can quickly find patterns and trends in your data, like customer purchase patterns or the effectiveness of your content strategy.

XML (eXtensible Markup Language): XML is a language that structures data in a way that both humans and computers can understand. If you’ve ever used a RSS feed to share your blog content, or sitemaps to improve SEO, you’ve used XML.

X-Validation (Cross-validation): Imagine you’re practicing for a game by scrimmaging against your own team. This is a bit like X-validation, where you split your data into a training set and a test set. This allows you to build your model with the training data, and then check how well it works with the test data.

XOR (Exclusive OR): XOR is a basic function in computing. It could be used in determining whether an event should happen based on two conditions. For example, an email campaign system might use XOR to decide if a promotional email should be sent to a customer based on their purchase history and email open rates.

Yolo (You Only Look Once): Imagine you’re watching a video and you can instantly recognize every object in it. That’s what YOLO does for computers. It can be used, for example, to recognize and tag items in your Instagram posts, making it easier for potential customers to find and purchase them.

YOLOv4 (You Only Look Once version 4): It’s a highly efficient AI tool that can analyze video footage and recognize objects, like differentiating between a cat and a dog in a video. If you’re an influencer, YOLOv4 could help automate tagging objects or people in your videos.

Yottabyte: A measure of digital information storage, a yottabyte is huge – it’s about a trillion gigabytes. To put it in context, all the digital data in the world only amounted to a few yottabytes as of 2020.

Yield Optimization: In the context of online advertising, yield optimization is the process of continually improving the performance of your ad campaigns, to get the best return on investment. It involves strategies like adjusting bid prices, selecting the best-performing ad formats, or targeting specific audience segments.

Zero-shot Learning: This is a type of machine learning where the model makes predictions for situations it hasn’t seen during training, without any fine-tuning. It’s like an AI that can successfully handle customer inquiries it has never encountered before, based solely on what it has learned from other, different inquiries.

Zero-Day Attack: This is a type of cyber attack that exploits a software vulnerability that’s unknown to those who should be interested in its mitigation (like the software company). As an online business entrepreneur, you need to ensure your software is regularly updated to protect against potential zero-day attacks.

Z-Test: It’s a statistical test used to determine whether two population means are different when the variances are known and the sample size is large. For instance, it can help you determine if the average spend of your male customers is significantly different from your female customers.

Zipf’s Law: It’s an observation that in many sets of data, the most frequent item occurs twice as often as the second most frequent item, three times as often as the third most frequent item, and so on. For instance, in your website traffic, a few pages might get most of the visits.

What am I missing? Did I screw a definition up ? Do you want to contribute? Is it oversimplified? or confusing?

Don’t be shy, let me know. This is for you at the end of the day: Contact me