Maxar Spatial Challenge The Maxar Spatial Challenge is about to launch! This event is a virtual hackathon to solve Australia's urban and regional challenges using the world's highest resolution satellite imagery. The Maxar Spatial Challenge aims to solve Australia's urban and regional challenges. Two years ago Consilium Technology student-interns, Liam Mellor and Lucas Sargent, participated in the previous hackathon event and won the innovation prize! Their submission was a...
Adelaide (13 November, 2017) – In an Australian first, Consilium Technology Pty Ltd (Consilium), a Machine Intelligence business, announced today that it has partnered with DigitalGlobe, the global leader in Earth imagery and information about our changing planet, to leverage DigitalGlobe’s Geospatial Big Data platform (GBDX). Consilium, headquartered in Adelaide, South Australia, will couple its machine learning technology with GBDX, a powerful, cloud-based platform with access to DigitalGl...
Consilium Exhibits at International Artificial Intelligence Conference
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22 Aug 2017
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2 minute read
Melbourne (21 August 2017) – Consilium Technology Pty Ltd (Consilium), an Australian Machine Intelligence Business and Research Services Provider, are exhibiting their machine intelligence and simulation capability at the International Joint Conference on Artificial Intelligence (IJCAI). The conference is being held from 21-24 August at the Melbourne Exhibition and Conference Centre. Dr Sebastien Wong (Director of Machine Learning, Consilium): “Our goal is to help businesses leverage a...
Feature learning will allow machine learning to revolutionise many industries, without needing years of domain expertise. “What is a good feature?” This is a three-decade old question in machine learning. Before we can understand the question, we must first understand what is a feature. In machine learning, features are any observations of a sample that are used to make a prediction or classification. For example, consider the problem of estimating the age of a child (our sample) using a...
... continued from part 1 Data Augmentation with SMOTE What about the case when we don't know how to peturb the data to ensure that label information is preserved? Well the Synthetic Minority Over Sampling Technique (SMOTE) can be used. Imagine every sample is a point in a multi-dimensional graph, where each dimension of the graph is one of the features. This is commonly referred to as feature space. Select two random samples fromthe same class. Now imagine drawing a line between them in feature...
One of the key components to a successful machine learning product is having sufficient, good quality data to train the classifier. The data samples should be representative of the entire population distribution. Increasing the number of samples reduces the risk of your model over fitting the data. That is, the model is too complex for the data set. The best way to get more samples is to simply go out and collect them. This might mean expensive and time consuming experimental data collection, al...