The realm of artificial intelligence is vast and intricate, filled with specialized terminology that can often be overwhelming. As researchers and developers delve deeper into this field, they frequently use specific jargon to articulate their findings and innovations. To aid in understanding, we have compiled a glossary that defines some of the most significant terms and phrases commonly encountered in discussions about AI.
This glossary will be updated regularly to include new terms as advancements in AI continue to emerge, along with the identification of potential safety concerns.
Artificial General Intelligence (AGI) is a term that remains somewhat ambiguous. Generally, it refers to AI systems that possess capabilities surpassing those of the average human across a variety of tasks. Some experts describe AGI as a system that could function as a competent human colleague. Others define it as highly autonomous systems that excel in most economically valuable tasks. The understanding of AGI varies among different organizations, leading to some confusion even among leading researchers in the field.
AI agents are sophisticated tools that utilize AI technologies to perform complex tasks on behalf of users, going beyond the capabilities of basic chatbots. These agents can handle various responsibilities, such as managing expenses, making reservations, or even writing and maintaining software code. However, the term ‘AI agent’ can have different interpretations depending on the context, as the infrastructure to support these capabilities is still evolving.
When posed with a straightforward question, humans can often respond instinctively. However, more complex inquiries may require a systematic approach, such as writing down equations to arrive at the correct answer. In AI, chain-of-thought reasoning involves breaking down problems into smaller, manageable steps to enhance the accuracy of the final output. This method may take longer but tends to yield more reliable results, particularly in logical or programming scenarios.
Deep learning is a subset of machine learning characterized by its use of artificial neural networks (ANNs) with multiple layers. This structure allows deep learning models to identify intricate patterns in data without needing explicit feature definitions from human engineers. These models can learn from their mistakes and improve their outputs through repetition. However, they require substantial amounts of data to perform effectively and typically involve longer training times compared to simpler machine learning models.
Diffusion technology is central to many AI models that generate art, music, and text. This process involves gradually degrading data by adding noise until it becomes unrecognizable. The goal of diffusion systems in AI is to learn how to reverse this degradation, enabling the recovery of original data from noise.
Distillation is a method used to transfer knowledge from a larger AI model to a smaller one, often referred to as a ‘teacher-student’ model. By comparing outputs from the teacher model with a dataset, developers can train the student model to mimic the teacher’s behavior, resulting in a more efficient and compact model.
Fine-tuning is the process of further training an AI model to enhance its performance for specific tasks. This is typically achieved by introducing new, specialized data that aligns with the model’s intended application. Many startups leverage large language models as a foundation and then fine-tune them to cater to particular industries or tasks.
Generative Adversarial Networks (GANs) are a type of machine learning framework that plays a crucial role in generative AI. GANs consist of two neural networks that compete against each other: one generates data while the other evaluates it. This adversarial setup allows the generator to improve its outputs over time, producing increasingly realistic data.
In the AI context, hallucination refers to instances where AI models generate incorrect or fabricated information. This phenomenon poses significant challenges for the reliability of AI outputs, as it can lead to misleading results and potential real-world risks. The issue often stems from gaps in training data, particularly in general-purpose AI models, which struggle to cover the vast array of questions users may pose.
Inference is the process of executing an AI model to make predictions or draw conclusions based on previously learned data. This step cannot occur without prior training, as the model must first identify patterns within the data to make accurate extrapolations.
Large language models (LLMs) are the backbone of many AI assistants. These models consist of deep neural networks that learn the relationships between words and phrases, creating a representation of language. When prompted, LLMs generate responses based on the patterns they have learned from extensive datasets, including books and articles.
A neural network is the foundational structure that supports deep learning and the broader advancements in generative AI. Inspired by the interconnected pathways of the human brain, neural networks have become increasingly powerful due to advancements in graphical processing hardware, enabling them to perform exceptionally well across various applications.
Training is a critical process in developing machine learning AIs, involving the feeding of data into the model so it can learn from patterns and generate useful outputs. While some AI systems operate based on predefined rules and do not require training, most modern AI models benefit significantly from this process, which can be resource-intensive.
Transfer learning is a technique that allows a previously trained AI model to serve as a foundation for developing a new model for a related task. This approach can enhance efficiency and is particularly useful when data for the new task is limited. However, models relying on transfer learning may still require additional training to perform optimally in their specific domains.
Weights are essential components in AI training, determining the significance of various features in the data. These numerical parameters adjust throughout the training process, influencing the model’s outputs based on the relationships identified in the dataset.