AI is rewriting the rules and inventing a new language to explain it. In pitches and product meetings, you now hear LLMs, RAG, RLHF — and, recently, “opaque recurrence,” a reasoning technique in the Astra model that worries safety researchers. The vocabulary moves so fast that even seasoned tech folks feel uneasy. This glossary fixes that with plain definitions for the terms you see in products, investing, or media. Treat it as a living document, much like the systems it describes.
What is AGI — and why do definitions differ?
AGI is a hazy term but usually means AI that exceeds an average human at many, if not most, tasks. Different organizations frame it differently, and even experts get confused. The core idea remains: systems that perform broad cognitive work at human level or above.
Some descriptions stress “high autonomy” and outperforming humans at most economically valuable work. Another view focuses on being at least as capable as humans across most cognitive tasks. The variation highlights how undefined this threshold still is.
Should that worry you? Not really. The field moves, and language lags behind. Keep the general capability level in mind, not a single-line doctrine.
AGI: AI that matches or surpasses humans across a wide range of tasks; its boundary remains blurry.
From chatbots to agents: how does AI act on your behalf?
An AI agent is a tool that performs sequences of tasks for you, going beyond simple answers. It can book things, file expenses, or work through code step by step. The concept implies an autonomous system that can harness multiple AIs for multistep jobs.
To act across services, agents need “buttons” — API endpoints. These are interfaces other programs press to make something happen: pull data or control third-party services without manual user action. Most smart devices and platforms expose such hidden “buttons,” even if users never see them.
Next comes standardization. The Model Context Protocol (MCP) lets models connect to external tools and data without a custom connector for every pairing. It’s pitched as “USB-C for AI”: an open standard handed to the Linux Foundation and adopted by several leading players. That accelerates integrations and makes agents more practical.
There’s also a coding niche. Coding agents don’t just suggest snippets; they autonomously write, test, and debug, iterating in place of the developer. Still, as with a fast intern, human review remains necessary.
AI agents + API endpoints + MCP: the three-part stack that turns answers into actions.
How do models reason and get better?
Models improve answers with chain-of-thought reasoning: they break complex problems into steps to boost accuracy. It takes longer but helps with logic and code. These reasoning models evolve from large language models and are optimized for stepwise thinking via reinforcement learning.
Reinforcement learning works through trial and “rewards” for correct behavior. Unlike rigid labeled datasets, the model interacts, acts, and updates itself from feedback. Techniques like RLHF are now central to making models more helpful, accurate, and safe.
New approaches stress transparency. “Opaque recurrence” means a model loops the same query through its internal layers repeatedly instead of reasoning in readable steps. It improves efficiency and lets smaller models punch above their weight, but it leaves fewer traces for oversight.
The technical sibling is “recurrent depth.” Safety researchers watch these techniques closely, since reasoning logs help catch misbehavior and their loss makes oversight harder. A separate hypothetical limit is “neuralese”: a case where models think only in internal numeric representations, turning the process into a total black box.
Opaque recurrence boosts efficiency while reducing transparency — a trade-off that safety researchers worry about.
What powers models: compute, parallelism, context, and tokens?
Compute is the processing power that trains and runs models. It’s often shorthand for the hardware stack: GPUs, CPUs, TPUs, and other infrastructure that underpins the industry. Without it, models neither form nor function.
Inference is a model in action. It makes predictions based on learned patterns. Different hardware performs differently, from phone chips to hefty GPUs. Very large models crawl on a laptop but move faster on cloud servers with high-end chips.
Parallelization means doing many computations at once. Modern GPUs are built for it, which is why they became AI’s backbone. As models and systems grow, spreading work across many chips and machines determines both speed and cost.
Memory cache speeds up inference. Caching saves computations so you don’t redo them. A well-known example is KV caching in transformers. It cuts compute and time needed to generate answers.
Tokens are the building blocks of human–model interaction. Tokenization breaks raw text into bite-sized units a model can process. It helps bridge the gap between human language and the model’s internal mechanics.
How do models learn and generate: from deep learning to diffusion and GANs?
Deep learning uses multilayer artificial neural networks inspired by the brain. These systems identify key features on their own, learn from errors, and improve outputs. They need lots of data and longer training, which raises costs.
Large language models (LLMs) are networks with billions of parameters, trained on books, articles, and transcripts. They build a multidimensional map of language and generate the most likely continuation for your prompt. Assistants work directly with LLMs or via tools like web browsing or code interpreters.
MoE splits one huge network into many “experts” and activates just a few per request. A built-in “router” picks the right specialists. You can build enormous models that stay relatively fast and cheap, since only a fraction works at any time.
Distillation transfers knowledge from a “teacher” to a “student.” You query the larger model, record outputs, and train a smaller one to mimic behavior with minimal loss. The result is a more efficient model grounded in a larger predecessor.
Fine-tuning is additional training for a specific task or domain. Startups take base LLMs and inject domain expertise to raise utility in target scenarios.
Diffusion powers many image, music, and text generators. The system “destroys” data structure with noise and learns the reverse process — to recover data from noise. Unlike physics, where diffusion is irreversible, here the goal is to learn reconstruction.
GANs pit two networks against each other: a generator tries to fool the discriminator, while the discriminator works to detect fakes. This contest yields more realistic outputs without constant human intervention. GANs shine in narrower use cases, like realistic photos or videos.
Open source in AI means making code or models publicly available to use, inspect, and modify. It accelerates progress and enables independent safety audits. Closed source keeps internals private while you use the product — a split that now fuels a core industry debate.
Risks and bottlenecks: hallucinations and RAMageddon?
Hallucinations happen when a model makes things up. They hurt quality, mislead users, and create real-world risks. The issue often stems from gaps in training data. The industry response is more specialized, vertical models with narrower domains and fewer knowledge holes.
RAMageddon is a mounting shortage of memory chips. AI labs and tech giants buy up RAM for data centers, driving up prices for what remains. The squeeze hits gaming, consumer electronics, and enterprise computing. Price surges may ease only after the shortage ends — and there’s little sign that will be soon.
Think that’s the end? Not quite. There’s also the strategic horizon of recursive self-improvement: models enhancing themselves without humans, or designing successors. In research terms, it’s a capability frontier that some teams already pursue — without the apocalyptic framing.
Based on the provided source.
