AI glossary updated to demystify AGI, LLMs, agents and the hardware crunch
Updated AI glossary breaks down contested AGI definitions, LLMs, AI agents, training techniques and a mounting hardware crunch in plain, practical language.
The latest AI glossary release aims to simplify a rapidly evolving vocabulary that has become essential for companies and policymakers. The glossary defines contested concepts such as AGI while clarifying how large language models, tokens and AI agents function in everyday applications. It is positioned as a living reference to help non-specialists and professionals navigate terms now common in investment memos, product roadmaps and regulatory debates.
Experts remain divided on what AGI means
Definitions of artificial general intelligence vary widely across labs and researchers, and the glossary reflects that lack of consensus. Some descriptions frame AGI as systems that outperform humans at economically valuable work, while others measure capability across a broad range of cognitive tasks. That ambiguity matters because how AGI is defined shapes research priorities, safety planning and public expectations.
AI agents moving from assistants to autonomous workflows
The glossary highlights a shift from simple chatbots to AI agents that can execute multi-step tasks without constant human input. These agents can coordinate APIs, manage schedules, file reports and, in specialized cases, write and test code autonomously. Developers and enterprises are racing to standardize interfaces and guardrails so agents deliver predictable results while respecting security and privacy constraints.
Large language models, tokens and memory caching explained
Large language models, or LLMs, are the statistical engines powering many assistants and coding tools, and the glossary describes how they learn patterns from vast corpora. It also explains tokens—the discrete text units LLMs process—and why token usage directly affects latency and cost for enterprises deploying these models.
The entry on memory caching clarifies an important efficiency tool: techniques like KV caching reduce repeated computation during inference and accelerate responses. Cached computations can dramatically increase token throughput, making models more responsive and less expensive to operate at scale.
How training, fine-tuning and transfer learning are applied
Training remains the foundational process by which models learn from data, but the glossary stresses that costs rise with dataset size and compute needs. Fine-tuning and transfer learning are presented as pragmatic shortcuts: teams start from a pretrained model and adapt it with domain-specific data to boost utility. That approach is common among startups that want high accuracy for niche tasks without redoing expensive base training.
Hallucinations persist and drive demand for specialization
The glossary treats hallucinations—when models generate incorrect or fabricated content—as a central operational risk. Hallucinations can undermine trust in customer support, legal summaries and medical triage, prompting companies to invest in verification layers and domain-focused models. As a result, many teams prefer verticalized systems that reduce knowledge gaps and narrow the scope for dangerous errors.
Hardware constraints are reshaping AI strategy
Compute, parallelization and memory limitations are no longer background technicalities; they are decisive strategic factors for projects and vendors. Training and serving modern models require GPUs, TPUs and other accelerators that are optimized for parallel workloads, and research into better parallelization remains an active field.
The glossary also calls out a phenomenon nicknamed “RAMageddon,” where high demand for memory chips has tightened supply across consumer and enterprise markets. The squeeze on RAM raises costs and can delay product launches, forcing organizations to rethink model size, caching strategies and on-premises versus cloud deployments.
The glossary serves as more than a list of definitions: it maps the trade-offs and tensions shaping AI deployment today. By connecting terminology to concrete technical and business implications, the resource aims to reduce confusion and help readers assess both opportunities and risks as the field continues to evolve.
