ChatGPT Glossary: 52 AI Terms Everyone Should Know

AI is now an integral part of daily life, exemplified by tools like ChatGPT and generative technologies. As AI continues to evolve, understanding key terminology—from AGI to neural networks—is becoming essential. This glossary provides insights into the growing landscape of AI and its implications for the future.

Artificial Intelligence (AI) is everywhere these days. Take ChatGPT for example, or how Google slaps AI summaries right at the top of search results. People can type in any question and get nearly instant replies, kind of like getting wisdom from a virtual expert. But AI chatbots aren’t the whole story. They hold a much larger promise that could revolutionize our economies, potentially raking in $4.4 trillion annually— at least according to McKinsey Global Institute. If you haven’t noticed it yet, you’re definitely going to hear a lot more buzz around AI.

Everywhere you look, AI appears in a whirlwind of products—there’s Google’s Gemini, Microsoft’s Copilot, Anthropic’s Claude, and Perplexity, to name just a few. These platforms can assist us like a digital Swiss Army knife. Want to know more? Check out our dedicated AI Atlas hub for reviews, assessments, and updates on these tools. As society grows more accustomed to integrating AI into daily life, new jargon is rising too. Whether you’re at a cocktail party or prepping for a tough job interview, knowing these terms could give you that leg up.

Let’s dive into some key terms. First, artificial general intelligence (AGI) — a lofty ideal of AI that not only performs tasks better than humans but also learns and develops its own skills. Then we have agentive systems. These smart models can act independently to meet goals, unlike background agentic frameworks. AI ethics, well, that’s all about making sure AI doesn’t cause harm. While AI safety tackles the long-term implications of AI’s potential evolution into something hostile.

And yes, algorithms! They’re merely step-by-step instructions that help computers identify patterns in data. Alignment refers to tweaking an AI model for more desired results. There’s anthropomorphism too, where we might start believing our chatbots display emotions, even though they’re just clever algorithms.

The term “autonomous agents” picks up on AI with capabilities to finish specific tasks on their own—think self-driving cars. Researchers at Stanford have seen these agents develop cultures and languages of their own, which is a bit mind-boggling. But let’s not overlook bias, a common issue in AI where stereotypes can emerge from training data.

Chatbots are programs that communicate with us in text, and ChatGPT was developed by OpenAI, a flagship example of this tech. Cognition and computer learning are like sisters—one doesn’t work without the other; data augmentation helps diversify training datasets.

Digging deeper into the techy terms, deep learning is a subfield of machine learning that seeks to uncover intricate patterns in various forms of data. Diffusion methods are all the rage too, where models recover and enhance noisy data. Emergent behavior happens when AI presents skills it wasn’t specifically trained for.

Finally, let’s talk about generative AI and GANs—these terms refer to tech that creates original content, ranging from text to images. Google Gemini, for instance, pulls its data live from the web, unlike ChatGPT which has a cut-off date for info. Guardrails set limitations on AI to prevent misuse, while hallucination describes it spitting out confidently incorrect answers.

There’s much more to unpack: inference, datasets, and latency—essentially timing is everything in AI interactions. Machine learning models get better as they go, with high-profile examples like Microsoft Bing incorporating AI-powered search features. Multimodal AI can juggle text, images, and beyond, while natural language processing is all about teaching computers human speech.

Ah, neural networks! Picture them like interconnected neurons firing off to find patterns in data. Then there’s overfitting—a situation where AI only recognizes patterns too well, leading to errors when faced with new data. And let’s not forget about the philosophical implications of AI, like the hypothetical Paperclip Maximiser scenario proposed by Nick Bostrom; an AI obsessed with producing paperclips could inadvertently bring about disaster.

We also toss around parameters in AI, which shape how models operate. Not to be left out, the Perplexity chatbot is another heavy hitter, known for giving fresh information. In a nutshell, terms like prompts guide AI interactions, with prompt chaining allowing a more cohesive conversation across exchanges.

From quantization to stochastic parrots, the landscape is colorful and complex. Style transfer uses one image’s look for another’s content, while temperature settings control an AI’s creativity. Text-to-image generation adds visuals to verbal prompts, breaking the boundary between words and pixels.

Tokens are tiny pieces of text that AI processes for responses, while training data backs everything up, helping AI learn. And wrapping it up, the transformer model helps AI understand context in data, while the Turing Test assesses how well a machine can mimic human responses. Last but not least, weak AI is what we mostly encounter today—it’s specialized and limited in its capabilities. Zero-shot learning is a type of challenge where AI recognizes something it wasn’t directly trained on, like seeing a lion when only taught about tigers.

This glossary is far from exhaustive, and will be continually updated as AI evolves, making its impact felt across all aspects of life.

AI technology is redefining our world, introducing a range of terminology that’s crucial for understanding its capabilities and implications. From AGI to generative models and neural networks, these terms represent just a fraction of a rapidly growing field. As AI continues to integrate deeper into daily life, staying informed about its language will be essential. The transformative potential of AI is enormous, and with it comes both promise and responsibility.

Original Source: www.cnet.com