Assessing regulatory compliance of personal financial advice is currently a complex manual process. In Australia, only 5%- 15% of advice documents are audited annually and 75% of these are found to be non-compliant(ASI 2018b). This paper describes a pilot with an Australian government regulation agency where Artificial Intelligence (AI) models based on techniques such natural language processing (NLP), machine learning and deep learning were developed to methodically characterise the regulatory risk status of personal financial advice documents. The solution provides traffic light rating of advice documents for various risk factors enabling comprehensive coverage of documents in the review and allowing rapid identification of documents that are at high risk of non-compliance with government regulations. This pilot serves as a case study of public-private partnership in developing AI systems for government and public sector.

Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined elementary linguistic knowledge. In this work, we explore the emergence of compositional languages for numeric concepts in multi-agent communication systems. We demonstrate that compositional language for encoding numeric concepts can emerge through iterated learning in populations of deep neural network agents. However, language properties greatly depend on the input representations given to agents. We found that compositional languages only emerge if they require less iterations to be fully learnt than other non-degenerate languages for agents on a given input representation.

Predictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates the offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using Bayesian Neural Networks (BNNs) with the online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

Running ML on edge devices is growing in importance as applications continue to demand lower latency. It is also a foundational element for privacy-preserving techniques such as federated learning. As of PyTorch 1.3, PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android. Learn more or get started on Android or iOS.

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

You're interested in Computer Vision, Deep Learning, and OpenCV…but you don't know how to get started. Follow these steps to get OpenCV configured/installed on your system, learn the fundamentals of Computer Vision, and graduate to more advanced topics, including Deep Learning, Face Recognition, Object Detection, and more! Deep Learning algorithms are capable of obtaining unprecedented accuracy in Computer Vision tasks, including Image Classification, Object Detection, Segmentation, and more. Follow these steps and you'll have enough knowledge to start applying Deep Learning to your own projects. Using Computer Vision we can perform a variety of facial applications, including facial recognition, building a virtual makeover system (i.e., makeup, cosmetics, eyeglasses/sunglasses, etc.), or even aiding in law enforcement to help detect, recognize, and track criminals.

Researchers have found a way to marry human creativity and artificial intelligence (AI) creativity to dramatically boost the performance of deep learning. A team led by Alexander Wong, a Canada Research Chair in the area of AI and a professor of systems design engineering at the University of Waterloo, developed a new type of compact family of neural networks that could run on smartphones, tablets, and other embedded and mobile devices. The networks, called AttoNets, are being used for image classification and object segmentation, but can also act as the building blocks for video action recognition, video pose estimation, image generation, and other visual perception tasks. "The problem with current neural networks is they are being built by hand and incredibly large and complex and difficult to run in any real-world situation," said Wong, who also co-founded a startup named DarwinAI to commercialize the technology. "These on-the-edge networks are small and agile and could have huge implications for the automotive, aerospace, agriculture, finance, and consumer electronics sectors."

This is the companion repository for our paper titled "Deep learning for time series classification: a review" published in Data Mining and Knowledge Discovery, also available on ArXiv. All python packages needed are listed in pip-requirements.txt Our results showed that a deep residual network architecture performs best for the time series classification task. The following table contains the averaged accuracy over 10 runs of each implemented model on the UCR/UEA archive, with the standard deviation between parentheses. The following table contains the averaged accuracy over 10 runs of each implemented model on the MTS archive, with the standard deviation between parentheses.

In this episode, hear about the work that Landing AI is doing to help organizations adopt modern AI and become more effective and valuable within their industry sectors. Andrew shares his experiences in overcoming the challenges that large companies face, including scoping the right set of projects, building trust company-wide for machine learning, and the importance of cross-functional teams that can ensure investment for the right projects. Hear him provide insight into how enterprises can get the most value for their ML investment as well as what the'essential complexity' of software engineering is, and how we as a community can address that complexity head on. Filled with gold nuggets of wisdom, this exclusive TWIMLcon episode is one you won't want to miss!

This week's guest is Mark Ryan, author of the upcoming book Deep Learning with Structured Data. Get 40% off the book with the discount code podlocalmax19, at Manning Publishing. To get Deep Learning with Structured Data as a free e-book, be one of the first 5 to email localmaxradio@gmail.com and request the secret code. As of today, 5 remain to be claimed! 4 remain to be claimed 3 remain to be claimed 2 remain to be claimed 1 more remains Deep Learning with Structured Data will teach you to apply powerful deep learning analysis techniques to structured data found in the relational databases that real-world businesses depend on. Mark Ryan has 20 years of experience leading teams delivering IBM's premier relational database product.