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Boosting Productivity with AI in Maple
Thursday, September 24, 2026 02:00 PM
Language:
English
AI is opening up new ways to work with mathematics, from generating code and visualizations to turning existing mathematical content into something you can compute with. But when mathematical accuracy and repeatability matter, an AI-generated response on its own may not be enough.
In this webinar, we’ll explore the latest AI-based features in Maple, designed to support mathematical work while maintaining access to reliable computation. Central to this is the Model Context Protocol (MCP), which gives an LLM access to Maple’s mathematical computation, allowing it to produce results that are accurate and repeatable.
We’ll also explore how back-and-forth interaction with an LLM can make Maple’s capabilities easier to access, lowering the learning curve for generating usable code and visualizations. Beyond conversational AI, we’ll look at AI-powered features in Maple that can import mathematical expressions from images and digitize handwritten documents so the mathematics can be explored, manipulated, and computed with in Maple.
Whether you use mathematics in teaching, research, or professional work, this session will provide a practical look at how AI and Maple can work together.
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The AI Lever Arm: 9 Workflows to Multiply Your Maple Flow Output
Thursday, October 08, 2026 04:00 PM
Language:
English
AI coding assistants — Claude Code, GitHub Copilot, Codex, Antigravity — are already fluent in Maple Flow. Just install the agentic Maple Flow skill, and you open up a surprising range of AI-powered calculation workflows.
In this webinar we walk through nine of them, grouped into three themes.
Create — generate design calculations from a prompt, complete with diagrams and documented provenance; turn an Excel spreadsheet into visible live math; rewrite a MATLAB script as a Flow worksheet.
Share — push a worksheet back out as a spreadsheet, desktop application, or browser-based web app, leaning on the Maple engine for reliable math.
Analyze — impose mathematical structure on a technical image; build math models from technical papers; distil a library of hundreds of worksheets into a single searchable index, complete with summaries and key results.
The caveat, stated plainly: you still need an expert human in the loop to verify and validate, and your results are only as good as your prompt, your AI model, and the data you feed it.
But as a starting point, these workflows will save you hours.
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