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Artificial Intelligence

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Artificial Intelligence

Planning compute and deployment for an AI project

Picking where and how to run an AI workload is a strategic decision: it affects development speed, unit cost, latency, privacy, and long-term operational complexity. Before committing to build, buy, or subscribe to compute capacity, clarify the workload profile (training vs inference, model size and update cadence), operational priorities (latency, resilience, cost), and device constraints (battery, thermal, network).

AR
Alex Rivers
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Popular Articles

Artificial Intelligence

Transformers and large language models - how they work at a high level

Modern systems that generate or understand natural language are built on the same underlying idea: a network that learns relationships across words or tokens and can use those relationships to predict, rank, or produce text. For someone deciding how these systems behave, it helps to separate three things: the basic architecture that makes the behavior possible, the way models are trained before any task-specific use, and the runtime mechanics that turn internal representations into visible text. The following explains those layers at a practical, non-technical level and highlights the tradeoffs you'll encounter when using large language models.

AR
Alex Rivers
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Transformers and large language models - how they work at a high level
Artificial Intelligence

Adopting AI in an organization - a practical pre-decision checklist

Leaders deciding whether to adopt AI should start by naming the specific outcomes they expect: which tasks to automate, what office automation will change, and how those changes translate to measurable business value. Vague ambitions—"becoming more data-driven"—make procurement and success measurement difficult. Instead, capture a handful of target improvements such as cycle-time reduction, error-rate reduction, faster decisions for a named team, or capacity to handle increased volume without hiring headcount.

AR
Alex Rivers
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Adopting AI in an organization - a practical pre-decision checklist
Artificial Intelligence

How organizations and public figures shape AI research and policy

AI development and regulation don't happen in a vacuum: a mix of private labs, corporations, academic groups, funders, and public personalities steer what gets built, released, and discussed. When a high-profile model or statement appears, it quickly shapes research priorities, hiring, investment, and even new lawmaking efforts. Understanding who plays which role and why their incentives differ makes it easier to read announcements critically and follow real progress rather than hype.

JC
Jordan Carter
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How organizations and public figures shape AI research and policy

More Guides

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Neural networks and deep learning explained

People often encounter neural networks when they hear about computers recognizing images or translating text, but at a basic level a neural network is a mathematical model that learns to map inputs to outputs by adjusting internal parameters. The model is assembled from simple units called neurons (or nodes) arranged in layers: an input layer, zero or more hidden layers, and an output layer. Each neuron receives numbers from the previous layer, combines them with weights, applies an activation function, and passes the result forward.

JC
Jordan Carter
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Agents, agentic AI, and conversational assistants - core concepts

People often use terms like agent, agentic AI, AGI, intelligent assistant, chatbot, copilot, and interactive interchangeably, which makes it hard to understand what a system can actually do and what it risks. The key distinction to keep in mind is whether a system is designed to pursue goals autonomously across multiple steps (agentic behavior) or to respond to single-turn or multi-turn prompts under close human direction (assistant behavior). Clearer definitions help set realistic expectations for capabilities, limits, and oversight needs.

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Ethan Harris
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Data foundations for AI - big data, embeddings, and vectors

When planning an AI project, the most common bottleneck is not the model itself but the data feeding it. Understanding what people mean by big data, how data analysis and data science differ, and how embeddings, vectors, and indexes work will help decide where to invest time and resources so models actually solve real problems rather than creating new ones.

JC
Jordan Carter
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Classical and tree-based algorithms plus sequence models

Linear methods—ordinary least squares for regression and logistic regression for classification—are a strong starting point when relationships are roughly linear, features are well-behaved, and interpretability matters. They are fast to train, require little hyperparameter tuning, and produce coefficients that are easy to inspect for sign and relative magnitude.

JC
Jordan Carter
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Open-source model hubs and toolkits - what they offer and how to use them

If you want to experiment with pretrained machine learning models, you will encounter many open model hubs and companion toolkits that promise ready-made models, conversion tools, and example code. Those resources lower the barrier to trying new models, but they come with varied quality, licensing, and operational expectations. A practical view of what hubs provide, how toolkits fit into the ecosystem, how to judge maturity, and how to run safe experiments helps newcomers move from curiosity to sensible evaluation.

EH
Ethan Harris
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Conversational AI landscape - product types and interaction models

Choosing or evaluating a conversational system is less about one single technology and more about a set of tradeoffs: what the system can understand, how it composes responses, how it fits into a user's workflow, and how safely it handles sensitive inputs. Different product types and interaction models produce very different user experiences even when they share similar underlying capabilities. Understanding those differences helps teams pick the right approach for a use case and helps users form realistic expectations about what the assistant can and cannot do.

EH
Ethan Harris