Workday CEO Aneel Bhusri: Machine Learning More Disruptive than Cloud

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Warning that businesses that ignore machine learning will "be left in the dust," Workday CEO and longtime cloud evangelist Aneel Bhusri said yesterday that machine learning will become even more disruptive than the cloud computing he's helped turn into a global phenomenon. Those would be strong words from any executive. But when they come from Bhusri--one of the leading advocates of and evangelists for cloud computing over the past 14 years--they dramatically underscore the scale and scope of ML's impact on the business world. In his keynote address opening his company's annual Workday Rising customer conference, Bhusri pegged ML as one of the three top-priority areas at fast-growing Workday as it gets closer to topping $1 billion in quarterly revenue. Bhusri's pointed and powerful focus on the ubiquitous role machine learning is playing at Workday comes at a critical time for the rapidly growing high-flier, which is #8 on my Cloud Wars Top 10 ranking.


Preservation of Anomalous Subgroups On Machine Learning Transformed Data

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In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry.


Artificial Intelligence & Deep Learning for Medical Diagnosis

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So, how do I see the future of healthcare using AI? Well, let's just face it. AI is heading to transform medicine and somewhere even replace real-medicine workers. Every year we observe the appearance of new and more advanced solutions. This, by the way, provides a whole slew of advantages, one of the most important of them is reducing the time needed to reach a diagnosis that allows medical workers to better prioritize patient case.


How to Detect Objects with Deep Learning on Raspberry Pi

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The real world poses challenges like having limited data and having tiny hardware like Mobile Phones and Raspberry Pis which can't run complex Deep Learning models. This post demonstrates how you can do object detection using a Raspberry Pi.


Google Could Transform Its Search with ML Technology

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In a bid to improve the accuracy of results returned by its search engine, Google is now employing machine learning algorithms to determine a user's intent. Run over neural networks called transformers, the search and advertising giant's technique for natural-language processing examines the words in a query in context rather than parsing them one-by-one. That way, the search engine is better able to divine a questioner's purpose when generating links to relevant web pages. Google named its innovation Bert – short for Bidirectional Encoder Representation and Transformer – and says it can improve accuracy by 10% over algorithms currently in use when responding to queries sent in the form of questions. Given that Google's servers receive up to 5.6 billion queries each day, the reduction in processing volume and allied energy costs promise to be significant by the time Bert is rolled out for all the languages used on the service.


Google releases source code of new on-device machine learning solutions ZDNet

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Google has opened up the source code of two machine learning (ML) on-device systems, MobileNetV3 and MobileNetEdgeTPU, to the open source community. In a blog post, software and silicon engineers Andrew Howard and Suyog Gupta from Google Research said on Wednesday that both the source code and checkpoints for MobileNetV3, as well as the Pixel 4 Edge TPU-optimized counterpart MobileNetEdgeTPU, are now available. On-device ML applications for responsive intelligence have been designed with power-limited devices in mind, including our smartphones, tablets, and Internet of Things (IoT) electronics. Google says the demand for mobile intelligence has prompted research into algorithmically-efficient neural network models and hardware "capable of performing billions of math operations per second while consuming only a few milliwatts of power," such as in the case of the Google Pixel 4's Pixel Neural Core. The latest MobileNet offerings include improvements to architectural design, speed, and accuracy, Google says.


Alternatives to the Gradient Descent Algorithm

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Gradient Descent has a problem of getting stuck in Local Minima. The following alternatives are available. The following is a summary of answers suggested on CrossValided, originally posted here. There are many optimization algorithms that operate on a fixed number of real values that are correlated (non-separable). We can divide them roughly in 2 categories: gradient-based optimizers and derivative-free optimizers.


Artificial Intelligence Roadmap - Data61

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Artificial Intelligence: Solving problems, growing the economy and improving our quality of life outlines the importance of action for Australia to capture the benefits of artificial intelligence (AI), estimated to be worth AU$22.17 Published by the Australian Government in November 2019, and codeveloped by CSIRO's Data61 and the Department of Industry, Innovation and Science, the report identifies strategies to help develop a national AI capability to boost the productivity of Australian industry, create jobs and economic growth, and improve the quality of life for current and future generations. The roadmap identifies three high potential areas of AI specialisation for Australia based on the opportunity to solve significant problems at home, export the solutions to the world and build on Australia's existing strengths. This report is intended to help guide future investment in AI and machine learning, and accompanies Artificial Intelligence: Australia's Ethics Framework, a discussion paper prepared by CSIRO's Data61 and published by the Australian Government in April 2019.


The future of Chatbots

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The next proven way to cater to leads efficiently is when you assist them via your website or landing page. There are many B2B users who take the support of live chats or callback software where they immediately cater to their client's needs. For instance, with an efficient callback software available in the market, you can use a chatbot to immediately assist your leads on arrival and help them reach out to you quickly or schedule a convenient time according to their preference. This is great because even when you are on a break or are away, you still receive alerts of a new lead showing interest.


Partnership Profile: HD Maps for Autonomous Vehicles

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Ecopia is creating the first HD Map of Waterloo Region. Today, drivers use maps for way-finding and to generally orientate themselves with their surroundings, but as the task of driving shifts from the in-car driver to in-vehicle automation, the role of digital maps shifts significantly. These next generation maps for machines come in the form of a highly accurate and realistic representation of the road, generally referred to as high-definition (HD) maps. The base layers of the Waterloo Region HDMap, created by Ecopia's Global Feature Extraction services, offers a highly accurate and highly attributed representation of the road, including attributes such as lane model, traffic signs, road furniture and lane geometry, as autonomous vehicles need very different maps from those that are currently used in today's navigation systems. HDMaps of Waterloo Region will be available to SMEs and academia on a platform hosted and developed by Ecopia.