Explainable AI, or XAI, refers to a collection of ways we can analyse machine learning models. It is the opposite of the so-called ‘black box’, a machine learning model whose decisions can’t be understood or explained. Here’s a short video we have made about explainable AI.
Technical Due Diligence on companies with AI products and technologies Are you thinking about making an investment in a startup that allegedly uses AI or machine learning and would like a completely impartial assessment of their actual AI technology or products?
Key data science concepts from A to Z I’ve put together a short selection of some intermediate-level data science concepts which give you a good grounding in the field. A lot of these are based on a series of articles which I wrote for the excellent data science resource deepai.org. I’ve biased the list of data science concepts a bit towards natural language processing, because that’s the area I mainly work in.
What is explainable AI? Explainable AI, or XAI, is a set of methods and techniques that allow us to understand how a machine learning model works and why it makes the decisions it does. Without XAI, a machine learning model might be a “black box”, where even the developers cannot understand it they arrived at a certain decision.
What is Natural Language Processing? Natural Language Processing (NLP) is the area of artificial intelligence dealing with human language and speech. It sits at the crossroads between a diverse number of disciplines, from linguistics to computer science and engineering, and of course, AI.
We built a machine learning model to predict which shipping vessels are likely to be held in detention. The model made it into the shortlist of the Singapore Ocean of Opportunities AI Track, an internationally renowned event where competing companies aim to build AI solutions for the shipping industry. So what can machine learning deliver for the shipping industry?
How we explain how a neural network can recognise an image? Sometimes as data scientists we will encounter cases where we need to build a machine learning model that should not be a black box, but which should make transparent decisions that humans and businesses can understand. This can go against our instincts as scientists and engineers, as we would like to build the most accurate model possible.
One challenge that large organisations face today is the problem of understanding and predicting which employees are going to leave the business, called employee turnover prediction or workforce attrition prediction.
Earlier I wrote another post about predicting the spend of a single known customer. There is a related problem which is predicting the total spend of all your customers, or a sizeable segment of them.
Why do we need to predict customer spend? You may have read my previous post about customer churn prediction. Another similar problem that’s just as important as predicting lost customers, is predicting customers’ daily expenditure.
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