AI Terms Explained: From LLMs to Hallucinations
▼ Summary
– The article introduces a glossary of AI terms to clarify technical jargon used in the industry, with plans for regular updates as the field evolves.
– AGI (Artificial General Intelligence) is ambiguously defined but generally refers to AI surpassing human capability in most tasks, with varying interpretations from OpenAI and Google DeepMind.
– AI agents are autonomous tools that perform complex tasks like booking or coding, though their definitions and infrastructure are still evolving.
– Chain-of-thought reasoning in AI breaks problems into intermediate steps for more accurate results, especially in logic or coding contexts.
– Deep learning uses multi-layered neural networks to identify data patterns autonomously, requiring large datasets and longer training times compared to simpler models.
Crossing the complex world of artificial intelligence requires understanding its specialized terminology. From foundational concepts to cutting-edge developments, the field relies on precise language to describe how systems learn, reason, and sometimes falter. This guide breaks down essential AI terms to help explain the technology defining our future.
Artificial General Intelligence (AGI) remains a debated concept, often described as AI surpassing human capability across most cognitive tasks. While definitions vary, OpenAI envisions it as outperforming humans in economically valuable work, and DeepMind sees it as matching human cognition, the goal remains elusive. Experts themselves grapple with its exact parameters, highlighting the field’s evolving nature.
An AI agent represents a leap beyond basic chatbots, autonomously handling multi-step tasks like expense reporting or restaurant bookings. These systems integrate multiple AI technologies, though infrastructure and standardization are still developing. The core idea? A self-directed assistant capable of complex workflows without constant human oversight.
Human reasoning often involves breaking problems into steps, like calculating livestock counts from head and leg totals. Chain-of-thought reasoning mirrors this in AI, where models decompose queries into intermediate stages for more accurate results. Though slower, this method significantly boosts reliability in logic-heavy domains like coding.
Deep learning, a subset of machine learning, uses layered neural networks to identify complex data patterns independently. Unlike simpler models requiring manual feature definitions, these systems self-improve through repetition—but demand vast datasets and substantial computational power, driving higher development costs.
At the core of generative AI tools lies diffusion, a technique inspired by physics. Systems gradually corrupt data with noise, then learn to reverse the process, enabling everything from photorealistic images to coherent text generation. Meanwhile, distillation compresses knowledge from larger “teacher” models into streamlined “student” versions, balancing efficiency with performance.
Specialization comes through fine-tuning, where pre-trained models adapt to niche tasks using domain-specific data. Startups frequently leverage this to enhance general-purpose AI for targeted industries, from legal research to medical diagnostics.
Generative adversarial networks (GANs) pit neural networks against each other, one creating content, the other critiquing it—to refine outputs like deepfakes or synthetic media. This adversarial dynamic sharpens realism without human intervention, though applications remain narrower than broad AI systems.
A critical challenge? Hallucinations, where models fabricate plausible but false information. These errors stem from training gaps, posing risks in high-stakes scenarios like medical advice. The push toward specialized AI aims to mitigate this by narrowing knowledge domains.
Inference marks the execution phase, where trained models generate predictions. Hardware choices, from smartphones to cloud servers, dictate speed and scalability, especially for massive models. Behind the scenes, training transforms random numerical structures into functional AI by exposing them to data patterns, though hybrid approaches can reduce costs by blending rules-based logic with targeted learning.
Transfer learning repurposes pre-trained models for new tasks, conserving resources when data is scarce. However, domain-specific adjustments often remain necessary for optimal performance.
Central to all this are weights, numerical values assigning importance to data features during training. Initially random, they evolve to reflect how inputs influence outcomes, like a real estate AI weighing bedroom counts against location data.
Large language models (LLMs) power familiar tools like ChatGPT and Gemini, processing prompts through vast neural networks trained on textual patterns. These systems predict probable word sequences, creating coherent responses—though their reliance on statistical likelihoods explains occasional inaccuracies. Underpinning them are neural networks, brain-inspired structures that revolutionized AI once GPU hardware enabled their full potential.
As AI advances, so too will its lexicon. This evolving vocabulary reflects both the field’s rapid progress and the ongoing quest to bridge machine capability with human understanding.
For more related terms check our Digital Realm Glossary
(Source: TechCrunch)
