Innovation That Matters

Technology trends for the world of tomorrow


data models

TemplateMark Compilation

This is part two of a series of articles on Accord Project TemplateMark. In this article I will show how TemplateMark can be statically compiled to TypeScript. I recommend you read Getting Started with TemplateMark if you have not yet done so.

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Getting Started with TemplateMark

TemplateMark is a data format from Accord Project that describes formatted natural language with embedded variables. In this article I introduce TemplateMark, the problems it solves and how to use it as an application developer.

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Custom Attributes, Properties and Concepts

In my job at DocuSign I provide pragmatic guidance to teams building various “smart” agreement features, which increasingly require a digital (aka computable aka symbolic) representation of knowledge, facts and rules; or what is called Knowledge Representation and Reasoning (KRR). In this article I introduce the high-level concepts and challenges, particularly related to “custom attributes” and machine learning.

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Breaking the Language Barrier: Why Large Language Models Need Open Text Formats

Foundational LLMs are trained on huge corpuses of text collected from the public Internet, including websites, books, Wikipedia, GitHub, academic papers, chat logs, Enron emails (!) etc. One of the better known public collections of training data is called The Pile and is an 800 GB dataset of diverse text for language modelling.

In this article I will examine how the training sets for LLMs should influence your choice of data formats and best-practices for data formats that can be generated by LLMs.

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Does ChatGPT Understand?

If you work in technology you will be aware that OpenAI ChatGPT has taken off like a 🚀, and the press is filled with people making rash prognostications, from gloomsters and doomsters to hypesters, and everything between.

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Data Model Verbalisation

I just had an interesting chat where it became clear that some people are a little confused about the differences between a data model, and how we display (or verbalise) a data model, for a given locale.

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