Artificial intelligence is reshaping the world and creating a new vocabulary to capture its impact. If you've attended any recent tech discussions or panels, you’ve probably heard terms like LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), and RLHF (Reinforcement Learning from Human Feedback). Most recently, a term that’s stirred concern among AI safety experts is “opaque recurrence,” a reasoning method in OpenAI's new Astra model. The rapid evolution of terminology can leave even knowledgeable industry professionals feeling somewhat bewildered.
To help clarify this landscape, we've compiled a glossary that provides straightforward definitions for commonly encountered AI terms. Whether you're involved in development, investment, or simply keeping abreast of the latest in tech news, this resource is regularly updated to reflect ongoing advancements in AI.
Artificial General Intelligence (AGI) is a complex term commonly understood as AI that surpasses human capability in a variety of tasks. OpenAI’s CEO, Sam Altman, likened AGI to a “median human you could hire.” OpenAI defines it as highly autonomous systems excelling at economically valuable tasks, while Google DeepMind regards it as AI that matches human capabilities in cognitive tasks. This definition can be a source of confusion even for leading experts in the field.
An AI agent is a more advanced tool that leverages AI technology to perform various tasks on your behalf, surpassing the capabilities of basic chatbots. These tasks can range from handling expense reports and making reservations to coding and debugging software. The term can vary in meaning since the infrastructure to support these capabilities is still developing, but generally, it refers to an autonomous system that can integrate multiple AI technologies.
API endpoints serve as interface “buttons” that allow other programs to interact with software applications seamlessly. Developers utilize these interfaces to create integrations, enabling applications to transfer data or allowing AI agents to access third-party services directly. Many smart devices and digital platforms come equipped with these hidden endpoints, which AI agents can increasingly identify and utilize autonomously, paving the way for innovative automation possibilities.
Humans can intuitively answer straightforward questions, like comparing the heights of animals, but more complex problems often require detailed calculations. For instance, a farmer with chickens and cows may need to derive information methodically through writing equations. In AI, chain-of-thought reasoning enables large language models to deconstruct problems into manageable steps, enhancing accuracy even though it may take longer to arrive at the solution. This reasoning approach stems from traditional large language models, refined through reinforcement learning techniques.
In contrast to a general AI agent, a coding agent applies similar principles specifically for software development. This specialized form of AI autonomously writes, tests, and debugs code—streamlining tasks that usually eat into a developer's time. These agents can navigate entire codebases, identifying bugs and implementing fixes with little human intervention, similar to having a tireless, alert intern assisting in software projects.
The term compute usually refers to the essential computational power required for running AI models. This encompasses the hardware needed to train and apply sophisticated algorithms, including GPUs, CPUs, and TPUs—elements that constitute the backbone of contemporary AI technology.
Deep learning is a specific approach in machine learning characterized by using complex, multi-layered artificial neural networks (ANNs). This architecture allows AI to draw intricate correlations, providing capabilities beyond simpler models like linear regressions. These systems identify features autonomously, learning from their errors to improve outcomes, though they require substantial amounts of training data and are slower to develop.
Diffusion models form the basis of many creative AI applications, such as art and music generation. These systems degrade data by introducing noise to an extent that can only be reversed by learning a “reverse diffusion” process, enabling the retrieval of the original data from its distorted form.
Distillation is a method for extracting knowledge from a larger AI model through a teacher-student framework. Outputs from a teacher model are recorded and compared to a dataset for accuracy, subsequently training a smaller, more efficient student model. This technique has been integral in developing more nimble models, such as OpenAI's GPT-4 Turbo.
Fine-tuning refers to the additional training of an AI model to enhance its performance for specific tasks by introducing new, specialized data. Many startups utilize large language models as a groundwork for crafting commercially viable products while refining them with industry-specific knowledge.
Generative Adversarial Networks (GANs) are a framework in machine learning that facilitate advancements in generating realistic data through a competitive approach between pairs of neural networks. This dual-system structure fosters the production of high-quality outputs with minimal human intervention, although GANs tend to excel in narrower applications.
In the AI community, “hallucination” describes instances where AI generates inaccurate information, leading to potentially unsafe outcomes. Such inaccuracies arise from deficiencies in the training data and are prompting a shift toward more specialized AI models to address knowledge gaps.
Inference is the mechanism by which AI models operate, drawing conclusions based on previously gathered data. Successful inference relies on adequate training; hence, robust hardware is essential for effective model execution, particularly for larger models that may struggle on less capable systems.
Large language models (LLMs), like ChatGPT and Claude, serve as the backbone of popular AI conversational tools, learning from vast datasets of text to understand and generate human language. They rely on intricate neural networks comprised of billions of parameters that encode relationships between words and phrases, mapping the nuances of language.
Memory cache enhances the efficiency of inference processes in AI, optimizing response generation by retaining calculations for future use. Various caching techniques, such as KV (key-value) caching, streamline operations in transformer-based models, resulting in quicker and more efficient outcomes.
The Model Context Protocol (MCP) is a recently developed open standard that facilitates the integration of AI models with external tools and databases, simplifying interactions without necessitating custom connectors. Developed initially by Anthropic and adopted by major players like OpenAI, Google, and Microsoft, MCP is gaining rapid traction in the AI landscape.
The Mixture of Experts model architecture divides a neural network into smaller sub-networks, activating only specific components for particular tasks. This approach allows for the construction of large models that remain efficient, as only a fraction of the network is utilized at any point, like selecting the right team for a project.
Neural networks, fundamental components of deep learning, draw inspiration from the interconnected pathways of the human brain. The advancement of GPU technology has propelled the effectiveness of these networks, enabling breakthroughs in numerous applications ranging from speech recognition to autonomous vehicles.
A concept known as "necrosity" describes a theoretical situation in which a model's reasoning becomes completely opaque, inhibiting human interpretability. While the Astra model uses a method known as opaque recurrence to drive efficiency, experts express concern that such techniques could hinder oversight.
Opaque recurrence involves repeatedly processing the same query within an AI model's internal layers, rather than articulating the reasoning in comprehensible terms. Though this can boost efficiency, it complicates transparency—a critical facet for ensuring AI safety.
Open-source software—where the code is publicly accessible—facilitates collaborative advancement in AI, as exemplified by Meta’s Llama models. This contrasts with closed-source products, like OpenAI’s GPT models, restricting visibility into their functionality and fostering significant debate within the AI sector.
Parallelization refers to executing multiple processes simultaneously, essential for both model training and inference. Modern GPUs excel at parallel processing, significantly impacting the speed and cost-effectiveness of AI model development.
“RAMageddon” is a colloquial term highlighting the escalating shortage of RAM chips, exacerbated by the insatiable demand from the AI industry. Major tech players vying for powerful AI capabilities are depleting the RAM supply, driving prices up and affecting various branches of technology, including gaming and consumer electronics. Until this shortage alleviates, the costs are projected to remain elevated.
Similar to opaque recurrence, recurrent depth refers to a technique that emphasizes the repetitive processing of queries within a model. While both terms can be interchangeably used in media, recurrent depth is a more technical description of the concept.
Recursive self-improvement (RSI) involves AI systems advancing their capabilities without human intervention, raising discussions about potential future scenarios where AI enhances itself autonomously—drawing parallels to the singularity event.
Reinforcement learning entails training AI systems through a process of trial and error, rewarding correct actions—similarly to training a pet. This approach allows models to continuously refine their behaviors based on feedback and has been successful in enhancing tasks from gaming to reasoning within large language models.
Tokens are the essential components that facilitate communication between humans and AI systems, breaking down language into manageable pieces for LLMs to process. They are generated through tokenization, which translates human language into a form comprehensible to AI. In business contexts, tokens often dictate costs, as usage is typically billed on a per-token basis, highlighting the growing importance of this metric in AI applications.
Furthermore, training refers to the phase where data is ingested by an AI model to learn patterns and generate meaningful outputs. As demand for training data rises, hybrid methods, including fine-tuning approaches, are increasingly adopted to balance costs.
Transfer learning enables the leveraging of previously trained models for new but related tasks, promoting efficiency and resource conservation while allowing models to achieve strong performance without extensive retraining.
Validation loss serves as a key indicator of an AI model’s learning effectiveness, with researchers monitoring it closely to optimize training processes. A low validation loss signifies healthy model learning, reducing issues like overfitting.
Weights are fundamental to an AI model's architecture, determining the significance of various inputs during training. As models learn and evolve, these weights adjust, enhancing the accuracy of outputs based on the provided training data. In applications like real estate price prediction, weights assign importance to factors like property features, shaping the model's insights based on historical data.
This glossary will continue to grow and evolve as the AI landscape progresses, ensuring that readers can stay informed and engaged with key developments.
