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There’s an awful lot of marketing noise around the ‘machine learning’ and ‘artificial Intelligence’ capabilities of much consumer- and enterprise-facing software. But the interchangeable use of terms that mean different things has led to a lot of confusion.

So let’s clear up, once and for all, exactly what machine learning means, and why it’s useful.

Machine learning

In short, machine learning is akin to a human learning how to perform a task more efficiently (or to result in a better outcome) through a process of trial-and-error. Software, obviously, can perform this same process far faster than people, and thereby increase its efficiency more quickly.

The result is a piece of software, device or machine that can improve its own performance through compound statistical modelling – each time there’s a new piece of data about a decision, it adds to the overall result.

As machine learning is based on statistical analysis of data sets, combined with predictive capabilities, its potential uses are widespread.

Machine learning is already the technology that powers image and voice recognition, fraud detection, and high-profile projects such as IBM Watson.

Artificial intelligence

Regular readers of this blog will likely be ahead of us here in spotting the confusion between these terms: AI encompasses machine learning and other technologies.

It’s an umbrella term that brings together a collection of technologies that are deemed ‘intelligent’.

The problem with that, as it correlates with automation, is that it leads to confusion and mismatched expectations. If customers don’t really understand what they’re paying for, it’s going to be difficult to get buy-in from across the wider business, let alone see where AI adds true value.

The common core?

Regardless of the specific technology being defined, those you often find listed alongside AI and machine learning share a common goal – to automate or imitate human activity. That’s why at Redwood we refer to all these ‘intelligence-based’ technologies as augmenting human experience and capabilities.

‘Augmented intelligence’ might not be as catchy a name as ‘artificial intelligence’ but we like to think of a future in which employees are still at the core of businesses, albeit with the benefit of added technological superpowers as standard.

About The Author

Devin Gharibian-Saki's Avatar

Devin Gharibian-Saki

Devin Gharibian-Saki brings a wealth of knowledge and expertise on enterprise IT, the SAP ecosystem and business process automation to his current role as SVP of Business Development and Strategy at Redwood Software. Experience within product marketing, product management and enterprise software sales enables Devin to drive strategic initiatives and alliances for the organization and unlock new business models and go-to-market strategies. Acting as an executive advocate for the customer, Devin is passionate about delivering the best solutions to make the most out of a customer’s environment. His approach centers on connecting with customers, prospects and partners to better understand how Redwood can help their digital transformation initiatives, improving their automation roadmaps by leveraging a combination of his SAP and process optimization proficiencies.

Prior to working for Redwood, Devin was an SAP Technology Consultant, working directly at SAP and at EnBW, the 3rd latest utility in Germany. Devin holds a diploma in Mathematics from Karlsruhe Institute of Technology in Karlsruhe, Germany and as well as two patents.