AI – Duncan R Shaw – The qualitative data scientist

Where to Use Artificial Intelligence in Your Business and How to Start

(A version of this article without the links, recently appeared in the University of Nottingham Alumni Newsletter) Many companies are rushing to use AI technology. But first things first. Where should we start? There are tasks that AIs do much better than humans… but there are tasks where humans remain light years ahead of AIs.Continue reading “Where to Use Artificial Intelligence in Your Business and How to Start”

Giving AIs the (pattern) recognition that they deserve.

My new post on LinkedIn explains how AI’s help us by finding patterns in massive datasets and how these patterns actually help us to run our businesses. Clarifying this helps us to design better AIs and to use them more successfully.

Three new jobs to train for in an AI world

AIs will soon be taking over a lot of jobs and the roles of people will be changing. So, which new jobs should we be training for? My new post on LinkedIn gives a few tips on the sorts of jobs that will be created as we work more and more with AIs.

Worried about AIs taking your job? Here are four things that AIs can’t do better than humans.

My new post on LinkedIn gives a few tips on the skills you’ll need as more AIs enter your workplace.

How do you get the data you need? Even when you don’t know what’s out there or even if it exists?

My new post on LinkedIn is about how looking for the right data is like looking for a needle in a haystack. But we found a way to match data generators with data users – even for unpredictable data needs.

“Data Trusts” are a potential way to get data to train AIs. But how do we shepherd a whole flock of Trusts?

My new blog on LinkedIn is about the organisations which will deal with all the data that our AIs will need to work well. How do we make sure these organisations do not stray?

AIs need to be accountable when they makes choices.

One type of AI software uses neural nets to recognise patterns in data – and it’s increasingly being used by tech firms like Google and IBM. This type of AI is good at spotting patterns but there is no way to explain why it does so. Which is a bit of a problem when the decisions need to be fully accountable and explainable.