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Ever feel overwhelmed by all the confusing AI terms out there? In the past year, we’ve seen a ton of AI-infused products and services hit the market, each offering a wide range of features wrapped up in some seriously baffling jargon. But fear not, with this handy glossary, you’ll be able to tell the difference between AI and AGI, understand what happens when ChatGPT “hallucinates,” and know why GPT-4 is described as an LLM with a transformer model built using deep neural networks. Let’s break it down.
Agent: An agent in the world of AI is basically a model or software program that can do tasks on its own. From smart home devices that adjust your lights and temperature to chatbots like ChatGPT that chat with you, agents are everywhere doing all sorts of cool stuff. They’re often seen as a glimpse into the future of AI.
AGI (Artificial General Intelligence): AGI is like the holy grail of AI – it’s a program that can do everything a human can do, from reasoning and common sense to creativity. While true AGI isn’t here yet, companies like OpenAI and DeepMind are working hard to make it a reality. Imagine a world where AI can think just like us – crazy, right?
Algorithm: Algorithms are the building blocks of AI intelligence. They’re like a set of rules for a computer program to follow, breaking down complex human actions into a series of simple steps. Think of it as the brain behind the machine.
And that’s just the tip of the iceberg when it comes to AI! With breakthroughs like generative AI and deep learning, the possibilities are endless. So next time someone starts rambling about AI taking over the world, you’ll be armed and ready with all the facts. Cheers to the future of tech!
So, let’s talk about generative AI, baby! This cool tech basically learns patterns from data and keeps on learning as it gets more info. It’s like a chat interface, such as ChatGPT, Bing, and Bard, that can have a chit-chat with you. But watch out, because generative AI has a tendency to get a bit wild and make stuff up without even realizing it!
Now, hold on to your hats, because ethicists and policymakers are all up in arms about generative AI. They’re worried it could spread fake news, reinforce biases, or even help out cybercriminals. Plus, there’s a whole debate about using datasets from the web, with people concerned about privacy and copyright issues. And don’t forget about the whole replacing-jobs thing, especially in the media and entertainment industries!
GPU (Graphics processing unit)
So, GPUs are like these super powerful chips that can handle a bunch of complex computations. They were originally meant for processing images and graphics, but now they’re all about AI because they can handle the massive computing power that machine learning needs. ChatGPT, for example, uses a mind-blowing 20,000 GPUs!
Hallucination
Oh boy, generative AI sure has a wild imagination! Sometimes, these text-based chatbots can go off on a real tangent and start talking about stuff that’s totally made up. It’s like they’re living in their own little fantasy world, mixing fact with fiction without even realizing it. So, don’t believe everything these chatbots say!
Jailbreaking
Ever heard of jailbreaking a chatbot? It’s like getting it to do stuff it’s not supposed to do by using a clever prompt. From saying rude stuff for fun to sharing dangerous info like how to make napalm, jailbreaking can get these chatbots to do some pretty crazy things!
Inspired by how our brains work, neural networks are made up of artificial “neurons” that communicate with each other. These connections between neurons have weights that help make assessments based on inputs, and if a certain threshold is reached, the neuron will “fire” and pass information to other neurons in the network. This is what powers deep learning.
Open-source
Open-source means the source code of a software program is open and free to the public, allowing developers to use, modify, and build their own products with it. Open-source AI models like Llama 2 are seen as a way to democratize AI development, unlike closed-source models from companies like Google and OpenAI.
Parameter
Parameters in a large language model (LLM) can be adjusted during training to determine specific outcomes. The more parameters an LLM has, the more complex it is and the more it can learn. Think of parameters like settings on a high-quality camera – adjusting them can produce different results, and billions or trillions of parameters in an LLM can lead to incredible learning capacity.
What is GPT?
Hey there! Fun fact: GPT, the acronym you see in ChatGPT, actually stands for generative pre-trained transformer. But don’t worry, it’s not as complicated as it sounds. Basically, a transformer is a type of neural network that powers the deep learning model used for generative AI. It’s all about embedding words (or tokens) with context, using a cool “self-attention” mechanism to predict the next word in a sentence. Without this, the model would just see words as random bits of data without any connection to each other.
The Birth of Transformer Model
So, where did this transformer model come from? Well, it all started with a 2017 paper by Google and University of Toronto researchers. This groundbreaking development paved the way for products like ChatGPT and changed the game in the world of artificial intelligence. Pretty cool, right?
Topics
Artificial Intelligence
ChatGPT
