And machine learning has its limits

Users of an unnamed digital product, ca 2020 CE (image credit: The Mannequin Gallery)

Take a moment to think about all the devices through which you interact with your machines: keyboards, mice, touchscreens, microphones, AR or VR goggles, or that brain implant you just ordered from Neuralink. All of these rely heavily on data and machine learning to translate physical inputs to machine instructions. This might seem trite (what’s new?), but cutting the one-on-one connection between human input and machine output and replacing those connections with machine learning approximations has some important consequences. The insight dawned on me when I attended the 2020 ACM Recsys conference virtually in September 2020. At the intersection of…

How to move machine learning from POC to a core organisational capability

Shut up Siri, just shut up (Image credit: North By Northwest)

A lot of companies are making headways with machine learning (ML) these days. The leaps in image recognition and natural language processing enabled by recent advancements in deep learning and massive increases in available data have put AI* on the corporate radar almost everywhere. ML has outperformed humans and enabled companies to process service requests at a scale quite simply not possible with a 100% human service desk. It has seen successful applications in domains ranging from fraud detection to medical image analysis, from machine translation to personalised recommendations. Since quite a number of the headlines in ML are made…

Jonas Braadbaart

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