Artificial intelligence is not merely reshaping our world; it is simultaneously forging an entirely new lexicon to articulate its advancements. Attending any contemporary product meeting, pitch, or panel will expose one to a barrage of acronyms like LLMs, RAG, RLHF, and numerous other terms that can leave even seasoned tech professionals feeling somewhat disoriented. This glossary aims to demystify these concepts, offering clear, plain-English definitions for the AI terminology you are most likely to encounter, whether you are developing AI, investing in it, or simply striving to stay informed through publications like TechCrunch or relevant podcasts. We commit to regular updates as the field progresses, viewing this as a dynamic document, much like the AI systems it elucidates.
Artificial general intelligence, or AGI, remains an elusive concept. However, it broadly refers to AI systems that surpass the capabilities of an average human across a wide spectrum of tasks. OpenAI CEO Sam Altman once characterized AGI as the “equivalent of a median human that you could hire as a co-worker.” In alignment, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind offers a slightly different perspective, viewing AGI as “AI that’s at least as capable as humans at most cognitive tasks.” If this seems confusing, rest assured that experts at the cutting edge of AI research share similar uncertainties.
An AI agent denotes a sophisticated tool that leverages AI technologies to execute a sequence of tasks autonomously on your behalf, extending beyond the limited scope of a basic AI chatbot. These tasks can range from filing expenses and booking travel or restaurant reservations to even writing and maintaining code. Nevertheless, as previously clarified, this nascent field is characterized by numerous evolving components, meaning the term “AI agent” can carry diverse interpretations. The necessary infrastructure to fully realize its envisioned capabilities is still under development. Fundamentally, however, the concept implies an autonomous system capable of integrating multiple AI systems to accomplish multi-step objectives.
Consider API endpoints as virtual "buttons" embedded within a software application that other programs can "press" to trigger specific functionalities. Developers utilize these interfaces to construct integrations, for instance, allowing one application to retrieve data from another, or enabling an AI agent to directly control third-party services without requiring manual human intervention for each interface. The majority of smart home devices and interconnected platforms incorporate these hidden buttons, even if end-users never directly perceive or interact with them. As AI agents advance in capability, they are increasingly able to autonomously discover and utilize these endpoints, thereby unlocking potent – and occasionally unforeseen – opportunities for automation.
When presented with a straightforward query, the human brain can often provide an answer almost instinctively—such as determining "which animal is taller, a giraffe or a cat?" Yet, in many scenarios, a more complex problem necessitates intermediary steps, often requiring external aids like a pen and paper to arrive at the correct solution. For example, if a farmer possesses chickens and cows totaling 40 heads and 120 legs, one might need to formulate a simple equation to deduce the precise number of each animal (20 chickens and 20 cows).
Within the context of artificial intelligence, chain-of-thought reasoning for large language models involves dissecting a complex problem into smaller, sequential steps to enhance the precision and quality of the final outcome. While this approach typically extends the time required to generate a response, it significantly increases the likelihood of a correct answer, particularly in logical or coding-related tasks. Reasoning models are derived from conventional large language models and are specifically optimized for chain-of-thought processing through the application of reinforcement learning techniques.
This concept represents a more specialized application than the general "AI agent," which describes a program capable of undertaking independent, step-by-step actions to achieve a goal. A coding agent is a tailored version specifically applied to software development. Rather than merely suggesting code for human review and integration, a coding agent possesses the autonomy to write, test, and debug code, effectively managing the iterative, trial-and-error processes that typically consume a developer's time. These agents can operate across entire codebases, identifying bugs, executing tests, and implementing fixes with minimal human oversight. One might liken it to employing an exceptionally fast and perpetually focused intern, though, like any intern, human review of its work remains essential.
While a somewhat multifaceted term, "compute" generally refers to the indispensable computational power that enables AI models to function. This processing capability is the lifeblood of the AI industry, facilitating the training and deployment of its advanced models. The term frequently serves as shorthand for the underlying hardware infrastructure that delivers this power—including components such as GPUs, CPUs, TPUs, and other forms of foundational technology that underpin the modern AI landscape.
Deep learning is a subset of self-improving machine learning characterized by AI algorithms designed with a multi-layered, artificial neural network (ANN) architecture. This structural design empowers them to identify more intricate correlations within data compared to simpler machine learning systems, such as linear models or decision trees. The framework of deep learning algorithms draws its conceptual inspiration from the complex, interconnected pathways of neurons within the human brain.
Deep learning AI models possess the inherent capability to autonomously identify crucial characteristics within data, obviating the need for human engineers to manually define these features. This architecture also supports algorithms that can learn from errors and progressively refine their outputs through a cyclical process of repetition and adjustment. However, deep learning systems necessitate an extensive volume of data points (millions or more) to achieve optimal results. Furthermore, they typically demand longer training periods compared to simpler machine learning algorithms, which often translates into higher development costs.
Diffusion technology is a core component underlying many generative AI models capable of creating art, music, and text. Drawing inspiration from physics, diffusion systems systematically "destroy" the inherent structure of data—such as photographs or songs—by incrementally introducing noise until the original information is obliterated. In the realm of physics, diffusion is a spontaneous and irreversible process; for example, sugar dissolved in coffee cannot be restored to its original cube form. However, AI diffusion systems aim to learn a "reverse diffusion" process, enabling them to reconstruct and recover the original data from a noisy state.
Distillation is a sophisticated technique employed to transfer knowledge from a larger "teacher" AI model to a smaller "student" model. In this process, developers submit requests to the teacher model and meticulously record its generated outputs. These responses are sometimes benchmarked against a reference dataset to ascertain their accuracy. Subsequently, these recorded outputs are utilized to train the student model, which is specifically optimized to emulate the teacher's behavior and performance.
Distillation offers the advantage of creating a more compact and efficient model based on a larger counterpart, with minimal loss of performance. This method is widely believed to be instrumental in OpenAI's development of GPT-4 Turbo, a significantly faster iteration of GPT-4. While all AI companies employ distillation internally, it may also have been utilized by some to rapidly catch up with leading-edge models. It is important to note, however, that distilling from a competitor’s model typically constitutes a violation of the terms of service for AI APIs and chat assistants.
Fine-tuning refers to the subsequent training of an AI model, specifically aimed at optimizing its performance for a more focused task or domain than initially emphasized during its foundational training. This process typically involves feeding the model with new, specialized, or task-oriented data relevant to the desired area of expertise.
Many AI startups are leveraging large language models as a foundational platform for developing commercial products. Their strategy involves enhancing the utility of these models for a specific target sector or task by augmenting the initial training cycles with fine-tuning, incorporating their proprietary domain-specific knowledge and expertise.
A Generative Adversarial Network, or GAN, constitutes a machine learning framework that has underpinned significant advancements in generative AI, particularly in the production of highly realistic data, including—but not limited to—deepfake technologies. GANs operate through the interaction of two distinct neural networks: one network, the generator, draws upon its training data to produce an output, which is then passed to the second network, the discriminator, for evaluation.
These two models are fundamentally programmed to engage in a competitive dynamic. The generator strives to produce outputs that can successfully deceive the discriminator, while the discriminator's objective is to accurately identify artificially generated data. This structured adversarial contest effectively optimizes AI outputs, making them progressively more realistic without requiring additional human intervention. However, GANs are generally most effective for narrower, specialized applications, such as generating realistic photos or videos, rather than for general-purpose AI tasks.
Hallucination is the term favored by the AI industry to describe instances where AI models fabricate information, literally generating content that is factually incorrect. This phenomenon represents a significant challenge to the overall quality and reliability of AI outputs.
Hallucinations result in generative AI outputs that can be profoundly misleading and potentially introduce real-world risks, leading to dangerous consequences—consider, for example, a health query that yields harmful medical advice. The problem of AI systems fabricating information is widely attributed to gaps or biases within their training data. Consequently, hallucinations are a driving force behind the development of increasingly specialized and/or vertical AI models—that is, domain-specific AIs requiring narrower expertise—as a strategy to minimize knowledge gaps and reduce the proliferation of disinformation risks.
Inference refers to the operational phase of an AI model, essentially putting it into action to generate predictions or draw conclusions from previously encountered data. It is crucial to understand that inference is entirely dependent on prior training; a model must first learn patterns within a given dataset before it can effectively extrapolate and make informed judgments from that training data.
A diverse range of hardware is capable of performing inference, from the processors found in smartphones to powerful GPUs and specialized custom-designed AI accelerators. However, their performance in running models varies significantly. Very large models, for instance, would take an inordinate amount of time to make predictions on a standard laptop compared to the rapid processing capabilities of a cloud server equipped with high-end AI chips.
Large language models, or LLMs, are the advanced AI models that power popular AI assistants such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, and Mistral’s Le Chat. When engaging with an AI assistant, you are interacting with an LLM that processes your requests, either directly or by integrating with various available tools like web browsing or code interpreters.
LLMs are sophisticated deep neural networks composed of billions of numerical parameters (often referred to as weights) that are trained to discern the intricate relationships between words and phrases. Through this learning, they construct a comprehensive representation of language, akin to a multidimensional map of linguistic elements.
These models are meticulously constructed by encoding the vast patterns discovered within billions of books, articles, and transcripts. When a user provides a prompt to an LLM, the model then generates the most statistically probable pattern that aligns with that input.
Memory cache denotes a critical process that significantly enhances inference, which is the mechanism by which AI generates responses to user queries. Essentially, caching serves as an optimization technique engineered to boost inference efficiency. AI operations are intrinsically driven by intensive mathematical calculations, and each execution of these calculations consumes considerable power. Caching is designed to mitigate the number of computations a model must perform by storing specific calculations for reuse in future user queries and operations. Various forms of memory caching exist, with Key-Value (KV) caching being particularly well-known. KV caching functions within transformer-based models, improving efficiency and yielding faster results by reducing the time and algorithmic effort required to formulate answers to user questions.
Model Context Protocol, or MCP, is an open standard that facilitates the seamless connection of AI models to external tools and data—including your files, databases, or applications like Slack and Google Drive—without requiring developers to build custom connectors for each individual pairing. It can be conceptualized as a universal USB-C port for AI. Anthropic introduced MCP in 2024 and subsequently transferred its governance to the Linux Foundation; since then, it has been rapidly adopted by major players such as OpenAI, Google, and Microsoft, establishing itself as one of the fastest-spreading standards in recent AI history.
Mixture of Experts (MoE) is a model architecture that partitions a neural network into numerous smaller, specialized sub-networks, known as “experts,” activating only a select few for any given task. Rather than channeling every request through the entirety of the model—analogous to consulting your entire office for every question—an MoE model incorporates an internal “router” that intelligently selects the most appropriate specialists
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