Information and digital literacy in education via the digital path

Research report: The impact of AI, machine learning, automation and robotics on the information profession

The purpose of this independent research is to help our professional community to understand how AI, machine learning, process automation and robotics are either already impacting the daily work of healthcare information professionals or likely to do so in the near future.

In it author Dr Andrew Cox from Sheffield University, calls for a joined-up and coherent response from information professionals, enabling us to maximise the benefits of AI, machine learning, automation and robotics for information users while mitigating the emerging risks.

The full research report, published by CILIP with the support of Health Education England, sets out a detailed and methodical analysis of the challenges and opportunities presented by this new generation of technologies.

Report at: https://drive.google.com/file/d/19gWoLV_rSP1qKS9Z8KOoorRAQuHmFN4u/view 

A small, family-owned grocery store in the heart of a downtown district has been providing goods to their community for over ten years. Customers come and go, and products fly off the shelves in waves. For the last two years, the store has seen a decline in sales and wonders if they will be able to weather the latest storm of dropping sales and over-bloated inventory. The owner relies on his intuition, but lately has noticed that his business is running inefficiently.

Researchers have known that “artificial neurons” could carry out logical functions—i.e., learn the way humans do—since 1943. The term “artificial intelligence” has been around since its introduction at a science conference at Dartmouth University in 1956. But only in the past several years have we started seeing theory put into practice the way those researchers imagined. We now have machines that can translate languages, compose music, write novels, and operate vehicles.

At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple.

A short video from the University of Oxford explains the concept of machine learning.