The next extension of machine learning is on its way. Machine teaching promises to bring the power of AI to those unskilled in data science. What could possibly go wrong? Prefer to listen to this story? Here it is in audio format.

We derive an efficient method to perform clustering of nodes in Gaussian graphical models directly from sample data. Nodes are clustered based on the similarity of their network neighborhoods, with edge weights defined by partial correlations. In the limited-data scenario, where the covariance matrix would be rank-deficient, we are able to make use of matrix factors, and never need to estimate the actual covariance or precision matrix. We demonstrate the method on functional MRI data from the Human Connectome Project. A matlab implementation of the algorithm is provided.

Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (i) changes in vertex degree, (ii) changes in community membership of vertices, and (iii) unusual increase or decrease in edge weight between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.

Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We compare how few-shot approaches (matching networks and prototypical networks) perform for this task. Since this is essentially a one-class problem, we also demonstrate how a modified one-class version of prototypical models can be used for this application.

--Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender . Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail. In this paper, we evaluate how the fairness and effectiveness of the learned models are affected by the removal of the sensitive attribute, the encoding of the categorical attributes, and instance selection methods (including cross-validators and random undersampling). We used the Adult Income and the German Credit Data datasets, which are widely studied and known to have fairness concerns. We applied each data preparation technique individually to analyse the difference in predictive performance and fairness, using statistical parity difference, disparate impact, and the normalised prejudice index. The results show that fairness is affected by transformations made to the training data, particularly in imbalanced datasets. Removing the sensitive attribute is insufficient to eliminate all the unfairness in the predictions, as expected, but it is key to achieve fairer models. Additionally, the standard random undersampling with respect to the true labels is sometimes more prejudicial than performing no random undersampling. Software systems based on machine learning (ML) are being used at an increasingly higher rate and on a multitude of scenarios that have a significant impact on people's lives. Their ubiquity raises several legal and societal concerns, as decisions based on the output of ML models may introduce or perpetuate historical bias against some individuals, based on their intrinsic characteristics, such as race, gender or age. The use of automated decision-making systems is often appealing due to the gains associated with it, and might even be perceived as a step towards the eradication of personal bias from the process. Nevertheless, many are the risks associated with a careless adoption of decisions supported by these systems. In this context, fairness emerges as a key property in terms of the reliability and trustworthiness of software systems based on ML. These receive nowadays increased attention from regulatory institutions, with the recently approved European Union General Data Protection Regulation (GDPR) demanding organisations to handle personal data in a privacy-preserving, fair and transparent manner [1].

Meta-learning is a paradigm in machine learning which aims to develop models and training algorithms which can quickly adapt to new tasks and data. Our focus in this paper is on meta-learning in reinforcement learning (RL), where data efficiency is of paramount importance because gathering new samples often requires costly simulations or interactions with the real world. A popular technique for RL meta-learning is Model Agnostic Meta Learning (MAML) (Finn et al., 2017, 2018), a model for training an agent (the meta-policy) which can quickly adapt to new and unknown tasks by performing one (or a few) gradient updates in the new environment. We provide a formal description of MAML in Section 2. MAML has proven to be successful for many applications. However, implementing and running MAML continues to be challenging. One major complication is that the standard version of MAML requires estimating second derivatives of the RL reward function, which is difficult when using backpropagation on stochastic policies; indeed, the original implementation of MAML (Finn et al., 2017) did so incorrectly, which spurred the development of unbiased higher-order estimators (DiCE, (Foerster et al., 2018)) and further analysis of the credit assignment mechanism in MAML (Rothfuss et al., 2019).

The University of Queensland (UQ) is leveraging the power of its new supercomputer to analyse human skull models, with the work conducted being dedicated towards delaying the onset of one of the world's most debilitating illnesses -- Alzheimer's Disease. Courtesy of Dell Technologies, UQ's new high performance computer (HPC) system is being used by the Research Computing Centre (RCC). The system, dubbed Weiner, is capable of processing massive amounts of computational tasks in parallel, including data visualisation and machine learning, which allows for the modelling of possible treatments for illnesses. See also: Photos: The world's 25 fastest supercomputers (TechRepublic) Speaking with media at the Dell Technologies Forum in Sydney on Tuesday, UQ RCC chief technology officer Jake Carroll said the centre boasts a wide range of employees, from physicists through to computer scientists, even those specialising in humanities. "People from all walks of research need to be able to participate and integrate with these things," Carroll said.

But as these organizations grapple with growing demand, erratic temperatures, aging infrastructure, and the threat of cyberattacks, many struggle to maintain a high level of service in an uncertain and unpredictable landscape. Artificial intelligence (AI) and machine learning (ML), as powered by big data, have the potential to modernize energy and utilities organizations by identifying ways to reduce waste and redundancy, protect and manage assets, and detect performance anomalies – all while realizing valuable cost savings, both for the organization and the customer. In this blog, we explore the three main areas where AI is making a mark on the energy and utilities sector today and how such investments may impact the future. Each year in the U.S. alone, trillions of gallons of water are lost due to aging pipes, broken water mains, and faulty meters. Replacing the entire system would be massively expensive, time-consuming, and impractical, which means that utility companies must take a localized approach to repairs.

Fior Markets offers a latest published report on Global Machine Learning in Finance Market Growth (Status and Outlook) 2019-2024, providing key insights and giving a competitive advantage to consumers through a detailed report. The researchers have included essential figures associated with the production and consumption forecast for the major regions that the market is separated into consumption forecast by application and production forecast by type. The research study is a source of methodical information rich in both quantity and quality. It shows upcoming as well as future opportunities, revenue growth, pricing, and profitability, focusing on both global and the regional market. The report identifies the key trends related to the different sectors of the market. Various important players have mentioned in the report are: Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance A top-to-bottom research wraps the market dynamics such as growth drivers, threats, opportunities, and challenges.

CEO Nicholas Therkelsen co-founded Max Kelsen in 2015 to provide big data and machine learning services to clients large and small. In his position as CEO, Nicholas is responsible for designing and executing strategic vision, project design and management, fiscal and legal governance, and team building. Prior to this, Nick was consulting for companies across a range of industries to assist with their technology requirements. Nick holds a Bachelor of Laws and Bachelor of Economics from the University of Queensland. Nick has a broad range of expertise spanning business, economics, sales, management and law.