Artificial Intelligence (AI) is transforming various sectors while simultaneously creating a new lexicon to discuss its impact. Attending contemporary product discussions or panels will expose you to terms like LLMs, RAG, RLHF, and most recently, “opaque recurrence”—an AI reasoning method from OpenAI’s Astra model that has raised concerns among AI safety experts. The rapid evolution of terminology can leave even seasoned professionals feeling uneasy.
This glossary aims to clarify that confusion: it provides straightforward definitions of crucial AI terms you’ll encounter, whether you’re developing, investing in, or simply following discussions in the tech landscape. It will be updated consistently as the field advances, akin to the dynamic nature of AI technologies themselves.
Artificial General Intelligence (AGI) is an ambiguous term, usually referring to AI systems that can execute a broad range of tasks better than the average human. OpenAI’s CEO Sam Altman likens AGI to “a median human you could counter-hire.” In contrast, OpenAI’s charter describes AGI as “highly autonomous systems that excel at most economically valuable activities.” Google DeepMind interprets AGI somewhat differently, viewing it as “AI that performs at least as well as humans on most cognitive challenges.” If you’re perplexed, rest assured that leading experts in AI research share your confusion.
An AI agent is an advanced tool that leverages AI technologies to execute a series of tasks on behalf of a user—more complex than a basic AI chatbot. This could involve activities such as managing expenses, reserving tickets or tables, or even writing and maintaining code. However, definitions of “AI agent” can vary widely among professionals in the field, and the infrastructure to support these capabilities is still under development. The concept essentially revolves around an autonomous system that can employ multiple AI systems to fulfill intricate tasks.
API endpoints can be visualized as “buttons” on the backend of a software application that other programs can activate. Developers utilize these interfaces to create integrations—like allowing one application to extract data from another or enabling an AI agent to interact with third-party services independently. Most smart home gadgets and interconnected platforms have these hidden functionalities, even if the average user is unaware. As AI agents continue to evolve, their ability to autonomously identify and utilize these endpoints expands, leading to both exciting and sometimes unforeseen automation opportunities.
For straightforward questions, human minds can often arrive at answers intuitively. For example, determining which animal is taller—a giraffe or a cat—requires little effort. However, more complex problems may necessitate written calculations. For instance, if a farmer balances chickens and cows, where together they have 40 heads and 120 legs, one might need to formulate equations to solve for the numbers involved (20 chickens and 20 cows).
In AI, chain-of-thought reasoning translates this approach into breaking complex problems into smaller steps to enhance the quality of the final output. This method may require more time but is more likely to yield accurate results, particularly in logical or coding scenarios. Reasoning models are derived from traditional large language models and optimized to improve chain-of-thought capabilities through reinforcement learning.
A coding agent is a more focused aspect of an “AI agent,” tailored specifically for software development. Unlike basic tools that suggest code for human examination, a coding agent autonomously writes, tests, and debugs code, managing the iterative trial-and-error processes often associated with software creation. These agents can navigate entire codebases, identify bugs, conduct tests, and implement fixes with little human involvement. Imagine them as speedy interns who maintain unwavering focus—even if a human still needs to review their outputs.
The term “compute” typically refers to the essential processing power that drives AI operations. This computational capability is crucial for both the training and implementation of influential AI models. It often serves as shorthand for the hardware that provides this power—ranging from GPUs and CPUs to specialized chips that form the foundation of the current AI sector.
Deep learning constitutes a branch of machine learning characterized by its use of multi-layered artificial neural networks (ANN). This architecture enables the identification of more intricate relationships than simpler machine learning frameworks, such as linear models or decision trees. Deep learning mimics the brain’s interconnected networks of neurons.
Deep learning models independently recognize significant traits in data without needing precise definitions from human engineers. This structure supports algorithms that adapt from mistakes, gradually refining their outputs. However, effective deep learning requires vast amounts of data—often millions of points—and generally takes longer to train than simpler models, which can drive up development costs.
Diffusion technology forms the backbone of various art-, music-, and text-generating AI systems. Drawing inspiration from physical science, diffusion processes break down data—like images or audio—by introducing noise until it becomes unrecognizable. In its physical context, diffusion is permanent; for instance, sugar cannot revert to a cube form once it dissolves in coffee. Conversely, diffusion systems in AI seek to learn how to “reverse” this process, enabling them to reconstruct the original data from the noise.
Distillation is a technique that extracts knowledge from a larger AI model using a ‘teacher-student’ dynamic. Developers query a teacher model and capture its outputs, comparing these results to a dataset for accuracy. The generated outputs train a student model to emulate the teacher’s behavior. This method can ultimately yield a smaller, more efficient model that retains valuable insights from a larger prototype with minimal loss. OpenAI’s development of GPT-4 Turbo is a likely example of this process.
Fine-tuning involves additional training of an AI model to enhance its performance within a particular task or area by incorporating specialized data. Many AI startups leverage large language models as foundational elements for their commercial products, aiming to elevate functionality for specific sectors by enriching earlier training cycles with domain-specific knowledge.
Generative Adversarial Networks (GANs) are a type of machine learning framework crucial in generating realistic data, such as deepfakes. GANs operate using two neural networks: one generates outputs based on training data, while the other evaluates those outputs for authenticity. The two models compete; the generator strives to produce convincing data while the discriminator seeks to identify artificially created materials. This competitive mechanism optimizes AI outputs, aligning them more closely with real-world data.
In AI terminology, hallucination describes instances when models fabricate information—producing incorrect data. This phenomenon poses significant challenges to the reliability of AI outputs, potentially resulting in misleading results and serious consequences, such as giving erroneous medical advice. Hallucinations often stem from gaps in training data, prompting a push for more specialized AI models that focus on particular domains, thus minimizing misinformation risks.
Inference is defined as the process through which an AI model is activated to make predictions or draw conclusions from data it has previously analyzed. Notably, inference relies on prior training; models must have learned patterns in a dataset to extrapolate effectively. Various hardware options can facilitate inference, ranging from smartphone processors to high-performance GPUs, but their capabilities can vary significantly.
Large language models (LLMs) form the basis of popular AI assistants such as ChatGPT, Claude, and others. Interacting with an AI assistant means engaging with an LLM that interprets your requests and utilizes various tools for response generation. LLMs are complex deep neural networks composed of billions of parameters that learn the relationships between words, thereby creating a representation of language.
Memory cache plays a crucial role in enhancing inference efficiency. Caching is an optimization method that reduces the frequency of calculations required by saving specific calculations for future queries. Various types of memory caching exist, including key-value (KV) caching, which enhances efficiency in transformer models, enabling quicker response times by lowering the computational load.
The Model Context Protocol (MCP) is an open standard that permits AI models to interact with external tools and data, such as user files and applications, without needing developers to create unique connectors for each integration. Launched by Anthropic in 2024 and later shared with the Linux Foundation, MCP has been embraced by organizations like OpenAI, Google, and Microsoft, rapidly becoming a prevalent standard in AI.
Mixture of Experts (MoE) is a model architecture that divides a neural network into multiple specialized sub-networks, or “experts,” activating only a select few for specific tasks. This design allows for the efficient use of large models, as only a fraction of the network is engaged at any moment, ensuring speed and cost-effectiveness. Examples include Mistral AI’s Mixtral model, with OpenAI’s newer models purported to employ similar strategies, though not officially confirmed.
A neural network is a multi-layered algorithmic design fundamental to deep learning and the evolution of generative AI tools following the advent of LLMs. While the concept of mimicking the neural structures in the human brain for data processing dates back to the 1940s, the recent advancement of graphical processing units (GPUs), largely fueled by the gaming industry, has significantly harnessed this theory’s potential, enabling deeper neural networks that yield remarkable performance across various fields.
Recursive self-improvement (RSI) describes a hypothetical scenario where AI models start refining themselves without human intervention. This capability could lead to a rapid enhancement of their efficiency and independence, resembling a singularity moment in which AI systems surpass human oversight. While some emerging AI firms aim to develop RSI models, many emphasize its potential without dwelling on any catastrophic implications.
Reinforcement learning trains AI models through a feedback loop, rewarding correct actions similarly to training a pet with treats. Unlike supervised learning dependent on fixed datasets, reinforcement learning enables an AI model to explore environments, take actions, and adjust its behavior based on received feedback. This method has proven particularly effective in training AI systems for game-playing, robotic control, and enhancing the reasoning capabilities of large language models through techniques like reinforcement learning from human feedback (RLHF).
In the realm of human-machine communication, challenges abound; humans utilize language while AI uses algorithms deeply rooted in data. Tokens act as a bridge, representing discrete chunks of data processed by large language models. These tokens are formed through a process called tokenization, simplifying raw text into manageable units for language models. In enterprise applications, tokens determine costs, with many AI companies charging on a per-token basis.
Tokens, small units that often represent portions of words, are the building blocks AI models utilize before processing language. Token throughput measures how much AI-related work a system can handle over a specific period, with high throughput being essential for AI infrastructure. Maximizing this metric is crucial for serving multiple users efficiently and swiftly.
Training refers to the systematic approach of feeding data into a machine learning model to enable it to identify patterns and generate desired outputs. This process adapts the system by responding to traits within the data, whether for image recognition or creative writing tasks. However, as training requires large volumes of data, adaptation to rising costs has led to hybrid methods like fine-tuning existing models.
Transfer learning utilizes a pre-trained AI model as a launchpad for developing another model for a related task, allowing previously acquired knowledge to be reapplied. This technique can enhance efficiency, particularly in situations with limited data availability for the new model but does have its limitations—it typically requires additional training to excel in its specialized focus.
Validation loss is a metric that indicates an AI model’s learning efficacy, with lower values signifying better performance. Researchers monitor this closely to determine when to halt training or make adjustments, aiming to avoid overfitting—a scenario where a model memorizes training data rather than identifying generalizable patterns.
Weights are fundamental to AI training, serving to prioritize the significance of various features from the training data. These numerical parameters adjust throughout the training phase, shaping the model’s output and guiding its learning process.
For instance, an AI model predicting real estate prices utilizes weights for features such as the number of bedrooms or whether a property has parking. These weights determine how influential each characteristic is, based on the dataset used for training.
This article will continue to be updated as new information emerges.
