Owners of start-ups and small businesses can make artificial intelligence technologies work for their businesses by using less well-known techniques such as probabilistic methods. This is according to Philip Rooney, CEO of machine learning consultancy DataJavelin.
Rooney used as an example a project that his company did in the agricultural field using probabilistic methods. The use case was a farmer with 100 hectares of land. The cattle needed to be fed hay during the winter so the farmer had two choices. To buy hay from the market or set aside land on which to grow their own hay. Both would cost the farmer money because hay from the market is expensive, and if too much hay is produced, the excess has be sold at below market rate. “How can our farmer optimise how much land they use to grow the hay?” asked Rooney.
"We can get out something useful using just a few hundred data points."
DataJavelin had already developed a model alongside the Met Office to look at crop yields depending on temperature and precipitation. From that model they got yield measurements over 60 years. Rooney said: “These are quite small datasets so we can get out something useful using just a few hundred data points on our previous model.”
He explained that in the past if there was no access to probabilistic methods, they would have had to rely on average temperature and precipitation figures for each month. But with the probabilistic method, the noise is taken into account and it is easier to marginalise over this noisy data. “If we believe that we know exactly what the precipitation is going to be, we are going to end up believing we know exactly how much feed we will be able to grow and we can optimise to grow exactly the right amount to feed our cattle,” said Rooney. “Somewhere around 14 hectares.”
From the model, Rooney and his team plotted a graph with hectares versus the cost to the farmer along the axes. It showed that the probability of losing money was greater from underproducing hay by setting aside too little land than from overproducing. “By growing that little bit extra, we’ve de-risked the project by not having to buy as much hay from the market,” he said.
In addition to the farm example, Rooney also gave the owners inside tips about working with artificial intelligence to solve business problems. One is that AI experts are not the unicorns they once were. He went so far as to say that he could train an A Level student with three As how to work with neural networks in just three months because the learning curve for working with neural networks is far less steep than it used to be.
He warned to beware of charlatans of AI who know one technique extremely well. As a result, they apply that same technique to every problem they are faced with. This is the equivalent of a handyman with only hammers in their toolbox. “They say ‘We can solve it like this’ and actually they don’t have a deep understanding of the problems they are working on,” said Rooney.
He also suggested that business owners familiarise themselves with the languages R and Python, and made special mention of a particular book that can lead to training neural networks without any prior mathematical knowledge.
“AI isn’t a solution. It’s part of the value chain.”
Finally, Rooney said that when embarking on an AI project, it is imperative to break it down to small, achievable sub-projects and have a clear roadmap of where they want their business and their product to go. “Small projects are realistic and deliver value. A lot of projects fail because they are not well motivated. They are a vanity projects or they are doing them because they think they should.” He ended with this final nugget of advice. “AI isn’t a solution. It’s part of the value chain.”
Philip Rooney was speaking at an event held at Sussex Innovation business support incubator network.