The DailyTech AI glossary | DailyTech

Artificial intelligence is a complex and intricate field where scientists often use technical terms and jargon to explain their work. To help our readers navigate this industry, we have compiled a glossary of important words and phrases commonly used in our articles.

We will update this glossary regularly to include new entries as researchers make advancements in artificial intelligence and identify emerging safety concerns.

An AI agent is a tool that utilizes AI technologies to perform tasks beyond what a basic AI chatbot can do, such as managing expenses, making reservations, or even coding. However, the concept of an AI agent can vary, as the infrastructure is still evolving to support its capabilities. It typically involves an autonomous system utilizing multiple AI systems to complete complex tasks.

In the realm of artificial intelligence, chain-of-thought reasoning involves breaking down problems into smaller steps to enhance the accuracy of the final outcome. While it may take longer to reach an answer, the result is more likely to be correct, especially in logic or coding scenarios. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking through reinforcement learning.

Deep learning refers to a subset of machine learning where algorithms are structured with artificial neural networks to identify complex correlations. These algorithms draw inspiration from the interconnected pathways of neurons in the human brain, allowing them to learn from errors and improve their outputs over time. Deep learning systems require a large amount of data for optimal results and longer training periods compared to simpler machine learning models.

Fine-tuning involves further training an AI model to enhance its performance for specific tasks by introducing new specialized data. Many AI startups leverage large language models as a foundation for their products, refining them with domain-specific knowledge to cater to specific sectors or tasks.

Large language models (LLMs) are utilized by popular AI assistants like ChatGPT and Microsoft Copilot. These models are deep neural networks with billions of parameters that learn language patterns from vast datasets. When interacting with an AI assistant, you engage with an LLM that processes your requests based on learned language representations.

Neural networks form the basis of deep learning algorithms and generative AI tools, inspired by the interconnected pathways of the human brain. Graphical processing hardware has played a crucial role in unlocking the potential of neural network-based AI systems across various domains.

Weights are essential in AI training as they determine the significance of features in the data used to train the system, shaping the model’s output. These numerical parameters adjust during training to align the model’s output with the desired target, reflecting the influence of different inputs on the predicted outcome.