A framework for Artificial Intelligence (AI) using Machine Learning (ML): ISO 23053 standard

22 Nov 2023 07:00 AM By itSMF Staff

Reading time: ~ 2 min.

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Artificial Intelligence (AI) and Machine Learning (ML)

Since the announce of ChatGPT, we all have heard a lot of news about Artificial Intelligence (AI) as one of the most relevant technological innovation: but we have to keep in mind that Machine Learning (ML) is probably the real turning point on this evolution.

The Artificial Intelligence (AI) systems that are based on Machine Learning (ML) are indeed more popular in a lot of industries and are changing our daily habits, in our offices as well as at home.

If we should find a definition for Machine Learning (ML), we could consider it as a branch of Artificial Intelligence that uses computational techniques to allow IT systems to learn from data or even experience.

To better figure out an AI ecosystem with all its components and their respective functions, we can take a look at the ISO/IEC 23053 standard.

The ISO/IEC 23053 standard for AI Systems using ML

On the ISO/IEC 23053 standard we find a « Framework for Artificial Intelligence (AI) systems using Machine Learning (ML)» that helps us to understand the role of every systems component.

The ISO standard indeed applicable to every kind of organization, despite the industry and the size. The ISO/IEC 23053 standard can be apply to private or public companies, government entities, no-profit organizations which are implementing or using Artificial Intelligence systems.

Machine Learning Systems – despite their application – can be developed through the fine-tuning of the algorithms to fit to training data, or improve their performance base through maximizing a reward.

Speaking of Machine Learning methods, we have to consider that these include deep learning, a topic which is also addressed in this ISO standard.

The ISO/IEC 23053 standard table of contents

In this updated version of the document (ISO 23053:2002), we find the following chapters:

✅ 5.Overview

✅ 6. Machine learning system

✅ 7. Machine learning approach

✅ 8. Machine learning pipeline

✅ Annex A Examples data flow and data use statements for supervised learning process

To better figure out the «big picture» about what we just described in these last lines, we can bring as usual our infographic.

By Andrea Leonardi (VP @ Minerva Group Service, MP @ Alpemi Consulting & itSMF Swizerland board member).

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