Category Archives : Artificial Intelligence

12

Sep

How to extract building footprints from satellite images using deep learning

As part of the AI for Earth team, I work with our partners and other researchers inside Microsoft to develop new ways to use machine learning and other AI approaches to solve global environmental challenges. In this post, we highlight a sample project of using Azure infrastructure for training a deep learning model to gain insight from geospatial data. Such tools will finally enable us to accurately monitor and measure the impact of our solutions to problems such as deforestation and human-wildlife conflict, helping us to invest in the most effective conservation efforts.

Applying machine learning to geospatial data

When we looked at the most widely-used tools and datasets in the environmental space, remote sensing data in the form of satellite images jumped out.

Today, subject matter experts working on geospatial data go through such collections manually with the assistance of traditional software, performing tasks such as locating, counting and outlining objects of interest to obtain measurements and trends. As high-resolution satellite images become readily available on a weekly or daily basis, it becomes essential to engage AI in this effort so that we can take advantage of the data to make more informed decisions.

Geospatial data and computer

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12

Sep

Kickstart your artificial intelligence/machine learning journey with the Healthcare Blueprint

Azure blueprints are far more than models drawn on paper or solution descriptions in a document. They are packages of scripts, data, and other artifacts needed to install and exercise a reference implementation solution on Azure. The Azure Security and Compliance Blueprint – HIPAA/HITRUST Health Data and AI is one such blueprint targeting a specific scenario common in healthcare.

The healthcare blueprint

The healthcare blueprint includes a real healthcare scenario and an associated machine learning experiment for predicted patient length of stay (LOS). This use case is valuable to healthcare organizations because it forecasts bed counts, operational needs, and staffing requirements. This adds up to considerable savings for the organization using a LOS machine learning experiment.

Blueprint solution guide

A blueprint, like the one for AI in healthcare, consists of multiple components along with documentation. That said, there may be some areas that lack clarity and cause trouble in using the blueprint services after installation. To help with any pain points in the installation and usage of the Healthcare AI blueprint, we’ve developed a solution guidance document, Implementing the Azure blueprint for AI.

The article introduces the blueprint and walks through tips for installation and running the AI/ML experiments.

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12

Sep

Use AI to streamline healthcare operations
Use AI to streamline healthcare operations

The profound impact of machine learning (ML) and artificial intelligence (AI) is changing the way heath organizations think about many of the challenges they face. Making data-informed decisions based on actionable insights is improving many aspects of healthcare from patient diagnosis and outcomes to operational efficiencies.

Data-informed decision making

While making decisions with deep insight into relevant data, healthcare organizations must be especially mindful of how they implement such solutions. Regulations like HIPAA and HITRUST compliance require data be kept secure, private and anonymized for those who don’t require access to patient data.

Further, IT staff are often unprepared or understaffed to implement such solutions. This is why the Azure Healthcare AI Blueprint was created, to bootstrap AI solutions for healthcare organizations using Microsoft Azure Platform as a Service (PaaS). After installing the blueprint, organizations can learn from the reference implementation and better understand the components of a complete solution built with Azure services.

The Azure healthcare AI blueprint

The blueprint is installed to Azure via PowerShell scripts, and creates a complete environment that can run Azure Machine Learning Studio (MLS) experiments right away. In fact, there is a simple patient length of stay (LOS) experiment built right in.

Other

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11

Sep

Reduce false positives, become more efficient by automating anti-money laundering detection

This blog post was created in partnership with André Burrell who is the Banking & Capital Markets Strategy Leader on the Worldwide Industry team at Microsoft.

In our last blog post Anti-money laundering – Microsoft Azure helping banks reduce false positives, we alluded to Microsoft’s high-level approach to a solution—which automates the end-to-end handling of anti-money laundering (AML) detection and management.

AML ≠ Anti-fraud. Anti-fraud is immediate identification and halting of transactions. AML pursues the identification of suspected money laundering or other crimes. Failure to have an “adequate Transaction Monitoring System” can result in substantial fines.

Due to the growing number of fines issued, there is now an increased drive to hold compliance officers, senior executives, and board members personally liable for failing to have an adequate AML program and transaction monitoring system (TMS). Any alert generated and not closed by the TMS must be reviewed by a human. Current technologies cannot assess a transaction in context. Without human intervention, it is difficult, almost impossible to adapt to the rapidly evolving patterns used by money launders or terrorists. We have many partners that address bank challenges with fraud. Among that elite group, Behavioral Biometrics solution from BioCatch and the

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11

Sep

AI helps troubleshoot an intermittent SQL Database performance issue in one day

In this blogpost, you will learn how Azure SQL Database intelligent performance feature Intelligent Insights has successfully helped a customer troubleshoot a hard to find 6-month intermittent database performance issue in a single day only. You will find out how Intelligent Insights helps an ISV operate 60,000 databases by identifying related performance issues across their database fleet. You will also learn how Intelligent Insights helped an enterprise seamlessly identify a hard to troubleshoot performance degradation issue on a large-scale 35TB database fleet.

Azure SQL Database, the most intelligent cloud database, is empowering small and medium size business, and large enterprises to focus on writing awesome applications while entrusting Azure to autonomously take care of running, scaling, and maintain a peak performance with a minimum of human interaction, or advanced technical skill set required.

Intelligent Insights is a new disruptive intelligent performance technology leveraging the power of artificial intelligence (AI) to continuously monitor and troubleshoot Azure SQL Database performance issues with a pinpoint accuracy and at a large scale simply not possible before. Performance troubleshooting models for the solution where fine-tuned and advanced based on the learning from a massive workload pool of several million Azure SQL Databases.

In the period

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07

Sep

Connected arms: A closer look at the winning idea from Imagine Cup 2018

This blog post was co-authored by Nile Wilson, Software Engineer Intern, Microsoft.

In an earlier post, we explored how several of the top teams at this year’s Imagine Cup had Artificial Intelligence (AI) at the core of their winning solutions. From helping farmers identify and manage diseased plants to helping the hearing-impaired, this year’s finalists tackled difficult problems that affect people from all walks of life.

In this post, we take a closer look at the champion project of Imagine Cup 2018, smartARM.

smartARM

Samin Khan and Hamayal Choudhry are the two-member team behind smartARM. The story begins with a by-chance meeting of these middle school classmates. Studying machine learning and computer vision at the University of Toronto, Samin decided to register for the January 2018 UofTHacks hackathon, and coincidentally ran into Hamayal, studying mechatronics engineering at the University of Ontario Institute of Technology. Despite his background, Hamayal was more interested in spectating than in participating. But when catching up, they realized that by combining their skillsets in computer vision, machine learning, and mechatronics, they might just be able to create something special. With this realization, they formed a team at the hackathon and have been working together since.

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06

Sep

Current use cases for machine learning in retail and consumer goods

This blog was co-authored by Marty Donovan.

Retail and consumer goods companies are seeing the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For example, the Azure cloud is helping retail and consumer brands improve the shopping experience by ensuring shelves are stocked and product is always available when, where and how the consumer wants to shop. Learn more by reading Retail and consumer goods use case: Inventory optimization through SKU assortment + machine learning.

Here are common use cases for ML in retail and consumer goods, along with resources for getting started with ML in Azure.

8 ML use cases to improve service and provide benefits of optimization, automation and scale Inventory optimization through SKU assortment + machine learning ensure shelves are stocked and best products are always available for purchase. Recommendation EngineTrain Matchbox Recommender to modernize engine capabilities for relevant product and service offerings which can generate incremental revenue. Visual Search capitalizes on mobile-first, content rich, customer-centric search capabilities. Sentiment Analysis can help companies improve their products and services by better understanding how their offering impact customers. Fraud Detection to detect anomalies and other errors that signal dishonest behavior.

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04

Sep

Describe, diagnose, and predict with IoT Analytics

In a previous blog article Extracting actionable insights from IoT data I discuss the value of collecting IoT data from machines, assets and products. The whole point of collecting IoT data is to extract actionable insights. Insights that will trigger some sort of action that will result in some business value such as optimized factory operations, improved product quality, better understanding of customer demand, new sources of revenue, and improved customer experience.

In this blog, I discuss the extraction of value out of IoT data by focusing on the analytics part of the story.

Generally speaking, data analytics comes in four types (Figure 1):

Descriptive, to answer the question: What’s happening? Diagnostic, to answer the question: Why’s happening? Predictive, to answer the question: What will happen? Prescriptive, to answer the question: What actions should we take?

Figure 1: IoT Analytics Flavors

Since IoT analytics is a subcase of data analytics, these types map nicely onto IoT analytics as follows:

Data Analytics Type

Focus

IoT Application

Representative Questions

Implementation

Descriptive

What’s happening?

Monitor the status of machines, devices, products, and assets.

Assess if things are

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04

Sep

Anti-money laundering – Microsoft Azure helping banks reduce false positives

This blog post was created in partnership with André Burrell who is the Banking & Capital Markets Strategy Leader on the Worldwide Industry team at Microsoft.

One of the biggest compliance challenges facing financial institutions today is the high rate of false positives being generated by their Anti-Money Laundering (AML) Transactions Monitoring Systems (TMS). These systems are designed to identify suspicious transactions that may involve illicit proceeds or legitimate proceeds used for illegal purposes. The predominant TMS technologies are antiquated batch rule-based systems that have not fundamentally changed since the late ‘90s, and have not kept pace with increasing regulatory expectations and continually evolving money laundering techniques. More specifically, the technology lacks:

The ability to assess a transaction in the context of the customer, and of similar customers. The agility to react and adapt to rapidly evolving patterns used by money launders or terrorists, with minimal human intervention. The ability to clearly understand why a transaction is identified as possibly suspicious.

Additionally, banks continue to struggle with a 360 view of their customers, they have difficulty accessing the rich transactional data sources held in multiple data silos, aka “dark data”.  Without the 360 view, banks are unable

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04

Sep

Delivering innovation in retail with the flexible and productive Microsoft AI platform

I often follow several publications related to trends and emerging innovations in retail and consumer goods. Artificial Intelligence (AI) continues to be touted as a key ingredient in transforming this industry. I agree with this sentiment given the critical components of cloud computing and data availability, which combined create a case for modernization.

We are seeing real application of AI resulting in positive business improvements aimed at solving a range of service to production-type problems. These examples are tangible and exemplify the merits of AI and its applicability in retail and consumer goods. Take Macy’s virtual agent that can solve customer issues via the web and transfer customers seamlessly to a live agent if necessary. More than one-quarter of customer queries are answered by a virtual agent, improving the speed of service for customers and providing valuable data that is connected to back-end systems through Microsoft Dynamics 365 AI solution for customer service.

Deschutes Brewery is another great example. It’s the seventh-largest craft brewery in the United States. By partnering with OSIsoft PI System to collect and manage production data with Microsoft Cortana Intelligence Suite, they have estimated a 20 percent increase in production capacity leveraging existing equipment by implementing

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