From pattern recognition to tool-using agents with million-token memory, here’s what AI is—and what it’s becoming.

A Working Definition
Artificial Intelligence refers to computer systems that perform tasks that typically require human intelligence—perception, language understanding, reasoning, decision-making, and content generation. Modern AI is largely machine learning (ML): models learn patterns from data. Deep learning powers speech, vision, coding assistants, and creative tools.

Core Approaches You’ll Meet
• Supervised learning: models trained on labeled examples (e.g., email → “spam” or “not spam”).
• Self-supervised learning: models learn structure from raw data.
• Reinforcement learning: models learn by acting and receiving rewards (robotics, game-playing).
• Retrieval-augmented generation (RAG): combines an LLM with search over trusted documents to ground outputs.
• Fine-tuning/adapters: light updates that specialize a foundation model for industry or task.

What LLMs Actually Do
Large language models are foundation models trained on vast corpora to predict tokens across languages and code. At inference they condition on a prompt (and often retrieved documents) to produce text, call tools, or emit JSON. Increasingly they are multimodal: they accept text, images, audio—even video—and output a mix of those formats. OpenAI’s 2025 o-series (o3, o4-mini) emphasized deliberate “thinking” and integrated tool use—including browsing and Python—while improving vision-aware reasoning.

Multimodal: Beyond Text
By 2026, “multimodal” is the default. Models jointly process text, images, audio, and short video, keeping objects consistent and aligning narration with visuals. That unlocks practical use cases: meeting copilots that listen and read shared screens, document systems that parse tables and charts, and agents that inspect UIs to carry out steps when no API exists. Google’s Gemini 2.5 line makes “thinking” multimodal a first-class capability for developers.

The Long-Context Shift
Another quiet revolution is context length. Where 2023 apps fought brittle chunking, 2026 systems often pass whole codebases, data rooms, or hours of transcripts into a single call. Anthropic’s Claude Sonnet 4 now supports up to a million tokens of context across its API and cloud partners, enabling “one-shot” analyses that previously demanded complex pipelines. Teams are redesigning workflows accordingly.

From Answers to Actions: Agents
Instead of only responding, top models can decide when to “think longer,” when to browse, when to run Python, and when to click through a UI—what product teams call computer use. This shift turns chat into execution: research tasks, QA runs, filings, or onboarding flows. It also raises governance needs: permissions, audit trails, test harnesses, and rate controls so agents remain observable and reversible.

On-Device Intelligence
Not all AI runs in the cloud. Apple’s Foundation Models framework gives developers an on-device foundation model—the core of Apple Intelligence—for private, offline features that feel instant and incur no inference fees. Microsoft’s Phi-4 multimodal extends capable small models to kiosks and wearables. Together, they push a hybrid pattern: heavy lifting in cloud, frequent assistive tasks on device.

Why AI Sometimes Fails
Models are probabilistic and can hallucinate when prompts are ambiguous or when the needed fact isn’t in memory. Long chain-of-thought can compound error if not paired with tools or reference data. Safety matters too: models can reflect bias or be coaxed into revealing secrets. That’s why NIST’s AI Risk Management Framework and its Generative AI Profile are appearing in procurement, providing shared language for risk identification, measurement, and mitigation.

What To Expect in 2026 Roadmaps
• Better control: editable video/audio layers, style references, deterministic transforms.
• More trustworthy agents: standardized “computer use” APIs with permissions, logging, and sandboxes.
• Enterprise governance: model/system cards by default, with evaluations aligned to NIST.
• Hybrid runtimes: long-context cloud calls paired with on-device micro-models for privacy and responsiveness.

Common Misconceptions
AI is not a single thing, and it is not magic. LLMs don’t “understand” like humans—they map patterns with utility. They are also not guaranteed to replace every job; evidence so far points to productivity boosts, especially for junior workers. The truth in 2026 is practical: organizations that define clear tasks, wire models into systems of record, and measure outcomes are the ones seeing real gains.

Closing Thoughts
Artificial Intelligence in 2026 is less about a chatbot and more about a stack: multimodal models, long-context memory, tool-using agents, and a runtime that spans cloud and device. Get the vocabulary right and the rest follows—where to place compute, what to log, which tasks to automate first. Clarity beats hype. The direction is clear for builders: systems that perceive, reason, and act, safely and at human pace.

References

• OpenAI — “Introducing o3 and o4-mini” — https://openai.com/index/introducing-o3-and-o4-mini/

• Google DeepMind — “Gemini 2.5: Our newest Gemini model with thinking” — https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/

• Anthropic — “Claude Sonnet 4 now supports 1M tokens of context” — https://www.anthropic.com/news/1m-context

• Apple Newsroom — “Foundation Models framework unlocks new intelligent app experiences” — https://www.apple.com/newsroom/2025/09/apples-foundation-models-framework-unlocks-new-intelligent-app-experiences/

• NIST — “AI Risk Management Framework & Generative AI Profile” — https://www.nist.gov/itl/ai-risk-management-framework

Authors
Serge Boudreaux – AI Hardware Technologies
Montreal, Quebec

Peter Jonathan Wilcheck – Co-Editor
Miami, Florida