The Comprehensive Glossary on Generative AI - Part 1

This glossary is here to help out developers, data wizards, and folks who know their stuff in different fields. It's all about breaking down the crazy amount of jargon around Gen AI that's everywhere these days.

The Comprehensive Glossary on Generative AI - Part 1
Photo by Andy Kelly / Unsplash

If you're scratching your head over stuff like FMs and LLMs or trying to figure out what the heck fine-tuning and few-shot learning mean, our glossary is like your map through this jungle of terms. It's like turbocharging your skills in generative AI.


Adversarial Autoencoder (AAE) - Fancy Name for Nerdy Stuff So, imagine mixing the power of GANs (those cool adversarial networks) and the structure of an autoencoder. That combo creates an Adversarial Autoencoder, a champ at understanding complex data patterns.

Adversarial Machine Learning - Making Models Tough Here's the deal: to make AI models stronger, we feed them tricky inputs on purpose. This helps them shine in tasks like spotting spam, catching viruses, or telling who you are from your fingerprints. By facing these challenges, the AI becomes super reliable in real-life situations.

Agents - Smart Software Helpers Think of agents as digital sidekicks. They're like software heroes that can tackle tasks without waiting for humans to step in. From crunching numbers to surfing the web, they've got the tools to get the job done.

AGI (Artificial General Intelligence) - AI's Big Dream Imagine AI that's as smart as a human across the board. Some brainy folks at Microsoft call this AGI, where AI masters all sorts of tasks. It's a big deal in the AI world, even if everyone isn't totally on the same page about what it exactly means.

ASI (Artificial Super Intelligence) - AI on Steroids Now, take that AGI and crank it up a notch. ASI is like AI turned superhero, outsmarting even the brightest human minds. It's the AI that can handle just about any task you throw its way, from science to socializing.

Audio Synthesis - Making AI Music... and More Ever heard AI-produced sounds? It's like AI is DJ-ing. From simple beeps to human-like voices, it's all in the AI's playlist.

Auto-regressive Models - Predicting the Future (Sort of) These models predict stuff by looking at what came before. Like how you'd predict the next word in a sentence based on the words before it. So, for AI making music or images, it's all about understanding what's been created so far.

Autoencoder - Data Decoder Ring Imagine an AI that learns to shrink and expand things. That's an autoencoder. It crunches data down and then magically brings it back to life.

Autoregressive Generative Models - AI's Crystal Ball These models guess what's next by checking out what happened before. They're like AI fortunetellers that use past events to predict the future, whether it's with fancy math or cool patterns.


BERT (Bidirectional Encoder Representations from Transformers) - Language Superstar 🚀

Meet BERT, the language model from Google that's making waves. It's like a language champ that's been trained using transformers. But what makes it special is that it's super good at understanding words not just by looking left or right, but by considering the whole context around them.

Bloom - Language Model Extraordinaire 🌸

Check out Bloom, the brainchild of The BLOOM project. This model is like a language wizard on steroids. It's so good that it can handle a ton of language tasks, from understanding to creating, with pinpoint accuracy.


Chatbots and How They Talk Like Us

So, you've got these computer programs that are like virtual chit-chat partners. They can chat with you through text or even speak back in a human-like way. How do they pull this off? Well, they've got this nifty thing called natural language processing up their sleeves. It helps them understand what you're saying and come up with fitting replies, like a texting buddy that never sleeps.


The Texting Pro Now, here's the star of the show - ChatGPT. This baby is cooked up by the smart folks at OpenAI. It's a mega language model that spits out text that sounds just like something a human would say. That's why it's all the rage for creating AI chatbots. They use it to make their bots sound so good at chatting, it's almost like they're reading your mind.


AI Getting Creative with Sounds Hold onto your hat, because CLIP is like AI on a whole new level. It's short for Contrastive Language—Image Pretraining, but we can just call it CLIP. This beast is all about mixing sounds and pictures in wild ways. Picture this: AI making brand new sounds from scratch - it could be a simple beep or even a total mimic of human speech. Basically, AI showing off its creative side!

Conditional GANs (cGANs)

Adding a Twist to GANs Alright, let's dive into some GAN talk. You've got these things called Conditional GANs, or cGANs for short. They're like a fancy flavor of Generative Adversarial Networks. What makes them cool is that they bring a special guest to the party - a conditional variable. Think of it as giving the model a secret ingredient at the start. This lets the model cook up data based on certain conditions you set. So, it's like telling it, "Hey, make me some data that's like this, but with a twist!"


Mixing It Up Across Domains Now, crossmodal stuff is like when AI gets a bit of a Sherlock Holmes vibe. It's all about learning from one type of information and using that to solve mysteries in another type. Imagine taking what you know from pictures and using that to understand text, or the other way around. It's like magic, where the AI learns to connect the dots between different things.


Shape-Shifting GANs Hold onto your hats, because CycleGAN is like a shape-shifting magician among GANs. It's a type of GAN that can do something really impressive - it can turn one type of image into another, even without any matching pairs. You could take a drawing and turn it into a painting, without needing to have a pair of matching drawings and paintings. It's super handy for things like jazzing up photos, adding colors, or giving pictures a new style without needing a perfect match.



Supercharged Image Magic So, you know about DALL-E? OpenAI's creation that can whip up images based on text descriptions? Well, they've kicked it up a notch with DALL-E 2, the upgraded version. It's like the Picasso of AI, creating visuals from words and making it look easy. This whole thing is a shining example of a multi-talented AI system.

Data Distribution

Where the Data Party's At In machine learning, data distribution is like the dance floor of your dataset. It's how your data points are spread out. Now, in the world of GANs, the generator's job is to copy this data dance moves. It's like it's trying to learn the cool steps that your data does.


Hollywood Meets AI Alright, brace yourself for this one. Deepfakes are like Hollywood-level AI tricks. They take an existing picture or video of someone and swap their face with someone else's, using some nifty AI moves. Imagine seeing your favorite actor's face on someone else's body - that's a deepfake. They're cool for fun stuff, but they can also play tricks on folks and cause all sorts of confusion.


Mixing Things Up Now, in the realm of AI mixing, we've got diffusion. It's like starting with a legit data slice and then slowly adding some randomness to it. But here's the twist - AI learns to do the opposite, to predict how that randomness got added in the first place. It's like AI playing Sherlock Holmes with your data.


The Real vs. Fake Detector Alright, in the world of GANs, you've got the discriminator. It's like the AI bouncer at a club, trying to spot the fake IDs from the real ones. In this case, it's looking at the data the generator is making and saying, "Real or fake?" This helps the generator learn to make stuff that looks more and more real. It's like AI art school, but with a tough critique.


Emergence/Emergent Behavior

Unpredictable AI Surprises Okay, picture this: AI doing the unexpected, like making a sharp left turn out of nowhere or having a brainpower explosion. In AI talk, this is called emergence. It's when simple rules or processes cook up some seriously complex stuff. And those wild concepts of "sharp left turns" and "intelligence explosions"? They're like fireworks in the AI world, signaling sudden and huge leaps in intelligence, often tied to AGI's rise.


The Data Shape-Shifter Time to meet the chameleon of AI - embeddings! They're like data transformers, turning information into a whole new form. Think of them as data makeovers, giving numbers to things so AI can compare and play with them. If two things are alike, their embeddings will be like cousins. These tricks are gold for AI jobs like suggesting stuff or understanding human talk.


Foundation Model

AI Powerhouses Ready to Customize Alright, let's talk about these AI heavyweights known as foundation models. These bad boys are trained on a massive mix of data, covering all sorts of topics and styles. They're like the big buffet of AI knowledge. But the coolest part? You can tweak them to do specific jobs. It's like starting with a sturdy base and adding the finishing touches to create your masterpiece.


AI Tweaks for New Tricks Now, imagine you've got a super-smart AI model that already knows a bunch of stuff. Fine-tuning is like giving that smartie a new gig. You don't have to teach it everything from scratch. You just tweak it a bit, so it becomes a pro at something new. It's like turning a general expert into a specialist without all the groundwork. Super handy for getting things done faster and smarter.

Few-Shot Learning - AI Quick Learner Alright, listen up! Imagine an AI that's a fast learner, like seriously fast. Few-shot learning is like magic for these quick learners. Instead of bombarding them with tons of examples, they just need a few. We're talking a couple of samples from each category, and boom! They're ready to rock and roll. They can figure out new stuff with just a small taste of what's what. It's like turning an AI into a superhero that gets the job done with just a few training rounds.


Generative Pre-trained Transformer (GPT)

AI Wordsmiths Alright, let's talk about GPT, the superstar of neural network models. These guys are like the word wizards of the AI world. They're trained to whip up content like pros. They learn from a ton of text, which makes them masters at creating text that makes sense and fits the context. So, when you give them a nudge with a prompt, they'll come up with text that's on point. These smart cookies can write stuff, analyze what customers are saying, and make personalized connections.

GPT-1, GPT-2, GPT-3, GPT-4

Now, meet the GPT family. We've got GPT-1, GPT-2, GPT-3, and even GPT-4. These guys are like siblings, but they've been getting smarter with each version. GPT-2 brought more pizzazz, GPT-3 cranked up the sophistication, and GPT-4? Well, it's the Einstein of text generation. With each upgrade, they can whip up text that's even more impressive. GPT-3, especially, is the rockstar here. It's like a Swiss Army knife for text tasks - it can translate, answer questions, and fill in the blanks like nobody's business. These models keep leveling up, bringing us better and more versatile text skills.


The Language Model Marvel Say hello to GPT-J, the cool kid on the language model block. Developed by EleutherAI in 2021, it's like a word wizard with a twist. It's got a whopping 6 billion parameters and is a cousin of GPT-3, but with its own unique touch. Instead of mom's recipe, GPT-J was trained on something called The Pile, which is a mix of different sources all thrown into the same pot.


The DIY GPT-3 Now, let me introduce you to GPT-Neo. It's like a sibling of GPT-3, but from a different family, EleutherAI. It's a fancy language model built on the same architecture as GPT. It's like an open-source version of GPT-3, giving us two models - one with 1.3 billion parameters and another with 2.7 billion. They're like the chefs of natural language, cooking up sentences using deep learning. And just like GPT-J, these models were trained using The Pile.

Generative Adversarial Networks (GANs)

The Dueling Duo Imagine a face-off in the AI world. That's GANs for you! Cooked up by Ian Goodfellow and pals in 2014, GANs are like two rivals - the generator and the discriminator. The generator makes stuff up, while the discriminator decides if it's real or fake. It's like a creative showdown, and the generator gets better with every round.


The Creative Machine In the world of GANs, the generator is the Picasso. It's the one creating all the cool new data, learning from the real stuff and mimicking it.

Generative Models for Images

Painting with AI Alright, let's talk about generative models that can whip up images. They're like digital artists, trained on tons of pictures to create their own masterpieces. We're talking about models like GANs, VAEs, and DALL-E. They're like AI painters that can make images based on what they've seen before. It's like Picasso, Monet, and da Vinci rolled into one, with a touch of binary magic.


Hallucination - AI Daydreaming Gone Wrong

Alright, let's talk AI hallucination – it's like when your AI buddy starts having wild daydreams, but they're way off from reality. Picture this: your AI model goes on a creative spree and creates stuff that's just not right. It's like a surreal art show, but with mistaken strokes. These strange results point out glitches in how the AI is doing its thing. It's important to keep an eye on your AI pal and fix those oddities to make sure it's still playing by the rules and giving you the right stuff.


Image Translation

Image processing and generation A task in computer vision where the goal is to map or translate one image into another, often using a model known as GANs. For example, translating a daytime scene into a nighttime scene. Inpainting - Image processing and generation A generative task where the AI is meant to fill in missing or corrupted parts of an image. Typical applications include photo restoration and the completion of unfinished art.ChatGPT

Image Translation

Transforming Pictures Like Magic Alright, let's talk about a cool trick in computer vision – image translation. Imagine taking one picture and turning it into a completely different one. Like, making a sunny daytime scene look like it's nighttime. This is where GANs, those nifty models, come into play. They're like the magicians of the AI world, making these transformations happen.


Fixing Images with AI Brushstrokes Now, imagine you've got a picture, but parts of it are missing or messed up. Inpainting is like an AI brush that comes in to fix the problem. It fills in the gaps, making the picture whole again. Think of it as digital photo restoration or finishing up an art project that was left unfinished. It's AI's way of playing the role of an art restorer!


Langchain - Nodes and Prompts Langchain is a concept in AI and machine learning that affects reasoning capability. When prompting an LLM, the "chain-of-thought" technique improves the model's reasoning by breaking tasks into smaller, discrete steps. A more complex approach, "tree-of-thought," allows logical steps to branch and backtrack.

Untangling AI's Thought Process Alright, let's dive into Langchain, a big deal in the world of AI and machine learning that messes with how reasoning works. Imagine you're asking an AI model a question - Langchain influences how it reasons things out.

So, there's this trick called the "chain-of-thought." It's like breaking down a big problem into smaller steps. This way, the model can reason things out step by step, making its answers more solid. But wait, there's more! There's also the "tree-of-thought" approach, which is even fancier. It's like when you're solving a puzzle and you go in different directions, and sometimes you have to backtrack to find the right way. This "tree-of-thought" lets the model branch out and backtrack logically, making its reasoning more complex and sophisticated. It's like giving the AI a mental map to navigate through problems.

Large Language Models (LLMs)

Natural Language processing Large-scale AI models trained on extensive text data, such as GPT-3 and BERT. They can respond to prompts, generate text, answer questions, create poetry, and even generate code. This ability can enable personalized and authentic customer interactions and assist in automating customer- facing content. Latent Space- Embeddings and latent spaces In generative models, latent space refers to a compressed input data representation. It is the transition medium between the noise injected into the GAN’s generator and its output.ChatGPT

Large Language Models (LLMs)

The Big Brains of Language Alright, listen up! You've got these language models that are like the brainiacs of the AI world. We're talking about the big guns like GPT-3 and BERT. These models are trained on tons of text and have super-sized knowledge. They're like the answer machines – you ask them questions, they generate text, and they can even write poems and code. They're the AI pals that make chatting and automating content a breeze, giving you personalized and real interactions with customers.

Latent Space

The In-Between Magic Now, let's talk about latent space - it's like the middleman in the world of generative models. Imagine you're making something with a GAN (Generative Adversarial Network). You inject some noise to jazz things up, and then the generator works its magic to create something new. That in-between magic where the noise meets the generator's output? That's the latent space. It's like the backstage of a magic show, where all the transformation happens.

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