Australia’s Far North Queensland has been declared essentially dengue-free for the first time in about a century, following an intensive release campaign. Other targeted efforts are making strong progress in Asia and South America, where authorities have long tried to wipe out mosquito populations with insecticides.
“Our Wolbachia method is natural and self-sustaining,” Green says. “As a large-scale public health intervention, we believe that this a cost-effective way. The evidence so far is that it can sustain itself in local populations for up to seven years. And we expect it will continue.”
How machine learning and AI will help take the fight global
The Program’s data science partner, Gramener, is developing machine learning for the AI model. It will tap the Program’s existing release point records as well as many other datasets on human population densities, land use, industrial sites, weather, and other variables. Satellite imagery will be a big part of mapping out large urban areas with strategic and granular accuracy.
The aim is to have the ability to pinpoint multiple impactful release points within blocks of as little as 100 square meters.
“We want to target the areas where our intervention is needed most,” Green says. “We will be able to release Wolbachia mosquitoes where they will have the most effect with analysis at a countrywide scale instead of at a neighborhood scale. Our ambition is to be able to look at a whole country and run the model over all its urban areas and let it give an unprecedented snapshot of where we can have the most impact.”
Joppa says machine learning and AI are potent tools for not-for-profits that want to tackle big challenges but have limited resources.
“The World Mosquito Program started with the objective of figuring out how to attack a problem. In this case, they worked out how to neutralize the disease-carrying ability of mosquitoes. Then they worked out where they needed to release these mosquitoes.
“They started collecting tons of data. It then became a really messy data problem as they tried to compare a bunch of different data sets to work out where they could be most efficient.
“Ultimately, this is where machine learning comes in. It allows you to take all of that data, abstract it down to a single estimate of probability and map it out. It is cost-effective, and it is super scalable. Instead of figuring out data visualization and analytics for one particular area, you can now do it for an entire city, for an entire country, for the entire world.
“That is because the data sets they are using are globally generalizable. One model that works here can work everywhere.”
Top imagery: Wolbachia mosquitoes are released in rural Fiji.
It’s one thing for a Microsoft researcher to use all the available bells and whistles, plus Azure’s powerful computing infrastructure, to develop an AI-based machine translation model that can perform as well as a person on a narrow research benchmark with lots of data. It’s quite another to make that model work in a commercial product.
To tackle the human parity challenge, three research teams used deep neural networks and applied other cutting-edge training techniques that mimic the way people might approach a problem to provide more fluent and accurate translations. Those included translating sentences back and forth between English and Chinese and comparing results, as well as repeating the same translation over and over until its quality improves.
“In the beginning, we were not taking into account whether this technology was shippable as a product. We were just asking ourselves if we took everything in the kitchen sink and threw it at the problem, how good could it get?” Menezes said. “So we came up with this research system that was very big, very slow and very expensive just to push the limits of achieving human parity.”
“Since then, our goal has been to figure out how we can bring this level of quality — or as close to this level of quality as possible — into our production API,” Menezes said.
Someone using Microsoft Translator types in a sentence and expects a translation in milliseconds, Menezes said. So the team needed to figure out how to make its big, complicated research model much leaner and faster. But as they were working to shrink the research system algorithmically, they also had to broaden its reach exponentially — not just training it on news articles but on anything from handbooks and recipes to encyclopedia entries.
To accomplish this, the team employed a technique called knowledge distillation, which involves creating a lightweight “student” model that learns from translations generated by the “teacher” model with all the bells and whistles, rather than the massive amounts of raw parallel data that machine translation systems are generally trained on. The goal is to engineer the student model to be much faster and less complex than its teacher, while still retaining most of the quality.
In one example, the team found that the student model could use a simplified decoding algorithm to select the best translated word at each step, rather than the usual method of searching through a huge space of possible translations.
The researchers also developed a different approach to dual learning, which takes advantage of “round trip” translation checks. For example, if a person learning Japanese wants to check and see if a letter she wrote to an overseas friend is accurate, she might run the letter back through an English translator to see if it makes sense. Machine learning algorithms can also learn from this approach.
In the research model, the team used dual learning to improve the model’s output. In the production model, the team used dual learning to clean the data that the student learned from, essentially throwing out sentence pairs that represented inaccurate or confusing translations, Menezes said. That preserved a lot of the technique’s benefit without requiring as much computing.
With lots of trial and error and engineering, the team developed a recipe that allowed the machine translation student model — which is simple enough to operate in a cloud API — to deliver real-time results that are nearly as accurate as the more complex teacher, Menezes said.
Improving search with multi-task learning
In the rapidly evolving AI landscape, where new language understanding models are constantly introduced and improved upon by others in the research community, Bing’s search experts are always on the hunt for new and promising techniques. Unlike the old days, in which people might type in a keyword and click through a list of links to get to the information they’re looking for, users today increasingly search by asking a question — “How much would the Mona Lisa cost?” or “Which spider bites are dangerous?” — and expect the answer to bubble up to the top.
“This is really about giving the customers the right information and saving them time,” said Rangan Majumder, partner group program manager of search and AI in Bing. “We are expected to do the work on their behalf by picking the most authoritative websites and extracting the parts of the website that actually shows the answer to their question.”
To do this, not only does an AI model have to pick the most trustworthy documents, but it also has to develop an understanding of the content within each document, which requires proficiency in any number of language understanding tasks.
Last June, Microsoft researchers were the first to develop a machine learning model that surpassed the estimate for human performance on the General Language Understanding Evaluation (GLUE) benchmark, which measures mastery of nine different language understanding tasks ranging from sentiment analysis to text similarity and question answering. Their Multi-Task Deep Neural Network (MT-DNN) solution employed both knowledge distillation and multi-task learning, which allows the same model to train on and learn from multiple tasks at once and to apply knowledge gained in one area to others.
Bing’s experts this fall incorporated core principles from that research into their own machine learning model, which they estimate has improved answers in up to 26 percent of all questions sent to Bing in English markets. It also improved caption generation — or the links and descriptions lower down on the page — in 20 percent of those queries. Multi-task deep learning led to some of the largest improvements in Bing question answering and captions, which have traditionally been done independently, by using a single model to perform both.
For instance, the new model can answer the question “How much does the Mona Lisa cost?” with a bolded numerical estimate: $830 million. In the answer below, it first has to know that the word cost is looking for a number, but it also has to understand the context within the answer to pick today’s estimate over the older value of $100 million in 1962. Through multi-task training, the Bing team built a single model that selects the best answer, whether it should trigger and which exact words to bold.
Earlier this year, Bing engineers open sourced their code to pretrain large language representations on Azure. Building on that same code, Bing engineers working on Project Turing developed their own neural language representation, a general language understanding model that is pretrained to understand key principles of language and is reusable for other downstream tasks. It masters these by learning how to fill in the blanks when words are removed from sentences, similar to the popular children’s game Mad Libs.
You take a Wikipedia document, remove a phrase and the model has to learn to predict what phrase should go in the gap only by the words around it,” Majumder said. “And by doing that it’s learning about syntax, semantics and sometimes even knowledge. This approach blows other things out of the water because when you fine tune it for a specific task, it’s already learned a lot of the basic nuances about language.”
To teach the pretrained model how to tackle question answering and caption generation, the Bing team applied the multi-task learning approach developed by Microsoft Research to fine tune the model on multiple tasks at once. When a model learns something useful from one task, it can apply those learnings to the other areas, said Jianfeng Gao, partner research manager in the Deep Learning Group at Microsoft Research.
For example, he said, when a person learns to ride a bike, she has to master balance, which is also a useful skill in skiing. Relying on those lessons from bicycling can make it easier and faster to learn how to ski, as compared with someone who hasn’t had that experience, he said.
“In some sense, we’re borrowing from the way human beings work. As you accumulate more and more experience in life, when you face a new task you can draw from all the information you’ve learned in other situations and apply them,” Gao said.
Like the Microsoft Translator team, the Bing team also used knowledge distillation to convert their large and complex model into a leaner model that is fast and cost-effective enough to work in a commercial product.
And now, that same AI model working in Microsoft Search in Bing is being used to improve question answering when people search for information within their own company. If an employee types a question like “Can I bring a dog to work”? into the company’s intranet, the new model can recognize that a dog is a pet and pull up the company’s pet policy for that employee — even if the word dog never appears in that text. And it can surface a direct answer to the question.
“Just like we can get answers for Bing searches from the public web, we can use that same model to understand a question you might have sitting at your desk at work and read through your enterprise documents and give you the answer,” Majumder said.
Top image: Microsoft investments in natural language understanding research are improving the way Bing answers search questions like “How much does the Mona Lisa cost?” Image by Musée du Louvre/Wikimedia Commons.
Jennifer Langston writes about Microsoft research and innovation. Follow her on Twitter.
World Childhood Foundation, launched in 1999 by Queen Silvia of Sweden, recently marked 20 years of child protection with a roundtable on leveraging artificial intelligence (AI) to assist in tackling child sexual exploitation and abuse online.
The day-long event, held last month at the Royal Palace in Stockholm, brought together 60 AI experts, representatives from technology companies, child safety advocates, academics and others to explore new ways to combat the proliferation of child sexual exploitation and abuse imagery (CSEAI) online.
“How can we use AI as a catalyst for child safety online,” asked King Carl XVI Gustaf, who, along with Queen Silvia and other members of Sweden’s royal family, presided over the day’s discussions. “New approaches are needed, so we are bringing together some of the sharpest minds in AI and child protection to share knowledge and experiences.”
The event consisted of a series of presentations, panels and small-group discussions about raising awareness among the broader global population about the “epidemic” that is child sexual exploitation and abuse, as well as the misuse of technology to share illegal imagery and enable on-demand abuse of children tens of thousands of miles away. Experts shared experiences, ideas and data, including that reports of child sexual abuse videos to the U.S. National Center for Missing and Exploited Children (NCMEC) had risen 541% in 2018 compared to the prior year. Moreover, children of all ages and backgrounds are susceptible to sexual exploitation with more than 56% of the children in Interpol’s database identified as prepubescent. “Nothing surprises us anymore,” said one law enforcement official.
More, faster needed from all stakeholders
The roundtable concluded with a series of observations and recommendations from a variety of sectors, including law and public policy, technology, and victim advocacy, including that:
Governments need to take a more active role in addressing the issue. Indeed, no country or society is immune from child sexual abuse and the vile content that makes its way online. Experts acknowledged the work of some standouts governments like the U.K., Australia and others, but called for more globalization and harmonization
Children need to be acknowledged as rights-holders, including their right to privacy, and not just as “objects in need of protection”
Speed will continue to present a challenge with technological advancements moving at internet speed; academic research occupying a distant second position; and policy, law and regulation lagging significantly behind
Civil society needs to do more and, in particular, victims’ rights groups and other organizations must inject a sense of urgency into the dialogue, and
Hope must be offered by believing in the brilliance and power of the human and the machine working together to combat such deep-rooted societal ills
I had the privilege of attending and presenting details on the progress of the development of a new method to detect potential instances of child online grooming for sexual purposes. The technique is the result of a cross-industry hackathon that Microsoft hosted in November 2018. Engineers from Microsoft and three other companies continued to develop the process for 12 months following the hackathon, and we intend to make it freely available in 2020 to enable others to examine historical chat conversations for potential indicia of grooming. (Grooming for sexual purposes takes place when someone befriends a child with the intent of gaining the child’s trust for sexual abuse, sexual exploitation or trafficking.) For more about the technique being developed, see this post.
Queen Silvia builds on Vatican remarks
The week before the Stockholm roundtable, a number of attendees also participated in a conference in Rome, Promoting Digital Child Dignity: From Concept to Action. This event was sponsored by the Vatican’s Pontifical Academy of Social Sciences, the Child Dignity Alliance and the government of the United Arab Emirates.
Queen Silvia was a featured speaker at the Rome conference, noting that when she founded World Childhood Foundation, she hoped she could use her voice to highlight the global problem of child sexual exploitation and abuse. She imagined that the foundation would soon close because it would no longer be needed, as the global scourge that is child sexual abuse would have been eliminated. “To speak about the unspeakable, and to give children back their right to a childhood,” she said. “(Yet,) 20 years later, here we are, with an ever-increasing number of children at risk of abuse and exploitation online.”
Along with several speakers that followed her in Rome, the queen called on all stakeholders to come together and do more: policymakers, technology companies, civil society and faith-based groups. “For the child who has suffered abuse; for the child who is at risk; for the child who carries guilt and shame – for this child, we have to speak with one voice and to act collectively.” (The Queen’s Rome remarks were distributed to participants of the Stockholm roundtable.)
A third landmark event on combating CSEAI will be held later this month in Addis Ababa, Ethiopia. The African Union, the WePROTECT Global Alliance and the U.K. Government will sponsor the Global Summit to Tackle Online Child Sexual Exploitation on December 11 and 12.
Microsoft and the challenge of Online Child Sexual Exploitation
This increased attention from several corners of the globe and from new and different stakeholder groups is both needed and encouraging. Additional strides will follow only when we embrace a whole-of-society approach and all stakeholders take part in this important fight.
Microsoft has been combating the spread of CSEAI online for nearly two decades. We first became aware of the magnitude of these online horrors in 2003 when a lead detective from the Toronto Police Department sent an email to our then CEO Bill Gates, asking for help using technology to track down purveyors of CSEAI and for assistance with the detective’s goal of rescuing child victims. Microsoft responded with a $1 million investment and the creation of a technology still in use today by some law enforcement agencies to share investigative information.
Our commitment to create technology to help fight CSEAI online continued with the invention of PhotoDNA, PhotoDNA Cloud Service and PhotoDNA for Video. Progress has been made over the last 20 years, but more needs to be done, including raising awareness, educating young people and the wider public, reporting illegal content to technology companies and hotlines, and continuing to create technologies and techniques to assist in online detection and reporting.
To learn more about the World Childhood Foundation, visit the organization’s website. To learn what Microsoft is doing to tackle child sexual exploitation and abuse online, see this link, and to learn more about digital safety generally, go to www.microsoft.com/saferonline, like us on Facebook and follow us on Twitter.
Working on Microsoft Azure platform, Mohanty and his colleagues used a Convolutional Neural Network model to come up with a solution that can identify and count penguins with a high degree of accuracy. The model can potentially help researchers speed up their studies around the status of penguin populations.
The team is now working on the classification, identification and counting of other species using similar deep learning techniques.
Building AI to save the planet
A long-time Microsoft partner headquartered in Hyderabad in India, Gramener is not new to leveraging AI for social good using Microsoft Azure. It was one of the earliest partners for Microsoft’s AI for Earth program announced in 2017.
“I believe that AI can help make the world a better place by accelerating biodiversity conservation and help solve the biggest environmental challenges we face today. When we came to know about Microsoft’s AI for Earth program over two years ago, we reached out to Microsoft as we wanted to find ways to partner and help with our expertise,” says Kesari.
While the program was still in its infancy, the teams from Gramener and Microsoft worked jointly to come up with quick projects to showcase what’s possible with AI and inspire those out there in the field. They started with a proof of concept for identifying flora and fauna species in a photograph.
“We worked more like an experimentation arm working with the team led by Lucas Joppa (Microsoft’s Chief Environmental Officer, and founder of AI for Earth). We built a model, using data available from iNaturalist, that could classify thousands of different species with 80 percent accuracy,” Kesari reveals.
Another proof of concept revolved around camera traps that are used for biodiversity studies in forests. The camera traps take multiple images whenever they detect motion, which leads to a large number of photos that had to be scanned manually.
“Most camera trap photos are blank as they don’t have any animal in the frame. Even in the frames that do, often the animal is too close to be identified or the photo is blurry,” says Mohanty, who also leads the AI for Earth partnership from Gramener.
The team came up with a two-step solution that first weeds out unusable images and then uses a deep learning model to classify images that have an animal in them. This solution too was converted by the Microsoft team into what is now the Camera Trap API that AI for Earth grantees or anyone can freely use.
“AI is critical to conservation because we simply don’t have time to wait for humans to annotate millions of images before we can answer wildlife population questions. For the same reason, we need to rapidly prototype AI applications for conservation, and it’s been fantastic to have Gramener on board as our ‘advanced development team’,” says Dan Morris, principal scientist and program director for Microsoft’s AI for Earth program.
Anticipating the needs of grantees, Gramener and Microsoft have also worked on creating other APIs, like the Land Cover Mapping API that leverages machine learning to provide high-resolution land cover information. These APIs are now part of the public technical resources available for AI for Earth grantees or anyone to use, to accelerate their projects without having to build the base model themselves.
Envision this: It’s another frenetic morning in the stock market as an army of traders at one company chat with their clients by phone – counseling and cautioning, buying and selling.
The outcomes of those calls and transactions carry no guarantees, of course. There will be some winners, some losers. But before the closing bell rings, the traders’ company – an advisory client of KPMG – is sure of one outcome: the engagements were analyzed and potential risks surfaced.
How can the company be so certain? It deployed KPMG’s trader-risk-analytics platform, a solution that applies Azure Cognitive Services to help reduce risk and meet rising regulatory requirements within the financial services industry.
The platform is just one example of a solution jointly developed in the KPMG and Microsoft Digital Solution Hub, and a testament to KPMG’s drive to digitize its customer offerings across advisory, tax and audit by implementing Microsoft’s intelligent cloud.
To accelerate KPMG’s move to the cloud, KPMG and Microsoft have signed a five-year agreement that will allow KPMG and its clients to benefit from Microsoft innovations, including a strong focus on AI, risk and cyber security.
As one of the “Big Four” organizations, KPMG’s services and solutions encompass all industries – from government to banking to health care. That wide-ranging impact means KPMG also provides a potent business case for the potential of Microsoft technology to enhance and revitalize customers’ businesses across every sector, says Microsoft CEO Satya Nadella.
“Together with KPMG, we’re accelerating digital transformation across industries by bringing the latest advances in cloud, AI and security to highly regulated workloads in audit, tax and advisory,” Nadella says.
To grasp the scope and reach of KPMG’s digital evolution, take a closer look at one of the platforms it has launched for a core business line – audit. Better yet, just meet KPMG Clara.
KPMG is bolstering audit quality by infusing the process with data analytics, AI and Azure Cognitive Services, allowing audit professionals to use company data to bring more relevance to their audit findings and continue to meet increasing regulatory requirements and standards. KPMG uses Azure Cognitive Services to provide more continuous, holistic and deeper insights and value on audit-relevant data.
The company’s smart audit platform, KPMG Clara, is automated, agile, intelligent and scalable – ushering in what KPMG calls a new era for the audit. KPMG is deploying KPMG Clara globally, allowing clients access to real-time information arising from the audit process and communication with the audit team.
In addition, KPMG Clara will integrate with Microsoft Teams, providing a platform for audit professionals to work together on a project, centrally managing and securely sharing audit files, tracking audit-related activities and communicating using chat, voice and video meetings. This will simplify the auditors’ workflow, enabling them to stay in sync throughout the entire process and drive continuous communication with the client.
“Technology is disrupting organizations across the globe,” says Bill Thomas, global chairman of KPMG International. “Clients are turning to us like never before to help them implement, manage and optimize the digital transformation of their organizations.”
In fact, 65% of CEOs believe that AI will create more jobs than it eliminates, according to a survey of 1,300 CEOs conducted by KPMG for its 2019 “Global CEO Outlook” report.
The survey also found that 50% of CEOs expect to see significant a return on their AI investments in three to five years, while 100% have piloted or implemented AI to automate processes.
Through its tech expansion, KPMG’s clients will benefit from “consistent global service delivery, greater speed of deployment and industry-leading security standards to safeguard their data,” the company says.
At the same time, KPMG professionals will gain access to an arsenal of cloud-based tools to build business solutions and managed services that are embedded with AI and machine learning capabilities.
And with robotic process automation (RPA), they can utilize AI-infused software that completes the types of high-volume, repeatable tasks that once drained hours from their work weeks.
“Technology and data-driven business models are disrupting the business landscape,” says KPMG global chairman Thomas. “Our multi-year investment in digital leadership will help us remain at the forefront of this shift and further strengthen our position as the digital transformation partner of choice for our clients.”
KPMG also is modernizing its workplace for 207,000 employees across 153 member firms, using the Microsoft 365 suite of cloud-based collaboration and productivity tools, including Microsoft Teams.
KPMG deployed Dynamics 365 for more than 30,000 of their professionals across 17 member firms. This equips them with modern customer-relationship applications to quickly and efficiently manage both client requests and client demand.
Says Nadella: “KPMG’s deep industry and process expertise, combined with the power of our trusted cloud – spanning Azure, Dynamics 365 and Microsoft 365 – will bring the best of both organizations together to help customers around the world become more agile in an increasingly complex business environment.”
Top photo: Two people sitting in a KPMG lobby. (All photos courtesy of KPMG)
This post is co-authored by Anny Dow, Product Marketing Manager, Azure Cognitive Services.
In an age where low-latency and data security can be the lifeblood of an organization, containers make it possible for enterprises to meet these needs when harnessing artificial intelligence (AI).
Since introducing Azure Cognitive Services in containers this time last year, businesses across industries have unlocked new productivity gains and insights. The combination of both the most comprehensive set of domain-specific AI services in the market and containers enables enterprises to apply AI to more scenarios with Azure than with any other major cloud provider. Organizations ranging from healthcare to financial services have transformed their processes and customer experiences as a result.
These are some of the highlights from the past year:
Employing anomaly detection for predictive maintenance
Airbus Defense and Space, one of the world’s largest aerospace and defense companies, has tested Azure Cognitive Services in containers for developing a proof of concept in predictive maintenance. The company runs Anomaly Detector for immediately spotting unusual behavior in voltage levels to mitigate unexpected downtime. By employing advanced anomaly detection in containers without further burdening the data scientist team, Airbus can scale this critical capability across the business globally.
“Innovation has always been a driving force at Airbus. Using Anomaly Detector, an Azure Cognitive Service, we can solve some aircraft predictive maintenance use cases more easily.” —Peter Weckesser, Digital Transformation Officer, Airbus
Automating data extraction for highly-regulated businesses
As enterprises grow, they begin to acquire thousands of hours of repetitive but critically important work every week. High-value domain specialists spend too much of their time on this. Today, innovative organizations use robotic process automation (RPA) to help manage, scale, and accelerate processes, and in doing so free people to create more value.
Automation Anywhere, a leader in robotic process automation, partners with these companies eager to streamline operations by applying AI. IQ Bot, their unique RPA software, automates data extraction from documents of various types. By deploying Cognitive Services in containers, Automation Anywhere can now handle documents on-premises and at the edge for highly regulated industries:
“Azure Cognitive Services in containers gives us the headroom to scale, both on-premises and in the cloud, especially for verticals such as insurance, finance, and health care where there are millions of documents to process.” —Prince Kohli, Chief Technology Officer for Products and Engineering, Automation Anywhere
For more about Automation Anywhere’s partnership with Microsoft to democratize AI for organizations, check out this blog post.
Delighting customers and employees with an intelligent virtual agent
Lowell, one of the largest credit management services in Europe, wants credit to work better for everybody. So, it works hard to make every consumer interaction as painless as possible with the AI. Partnering with Crayon, a global leader in cloud services and solutions, Lowell set out to solve the outdated processes that kept the company’s highly trained credit counselors too busy with routine inquiries and created friction in the customer experience. Lowell turned to Cognitive Services to create an AI-enabled virtual agent that now handles 40 percent of all inquiries—making it easier for service agents to deliver greater value to consumers and better outcomes for Lowell clients.
With GDPR requirements, chatbots weren’t an option for many businesses before containers became available. Now companies like Lowell can ensure the data handling meets stringent compliance standards while running Cognitive Services in containers. As Carl Udvang, Product Manager at Lowell explains:
“By taking advantage of container support in Cognitive Services, we built a bot that safeguards consumer information, analyzes it, and compares it to case studies about defaulted payments to find the solutions that work for each individual.”
One-to-one customer care at scale in data-sensitive environments has become easier to achieve.
Empowering disaster relief organizations on the ground
A few years ago, there was a major Ebola outbreak in Liberia. A team from USAID was sent to help mitigate the crisis. Their first task on the ground was to find and categorize the information such as the state of healthcare facilities, wifi networks, and population density centers. They tracked this information manually and had to extract insights based on a complex corpus of data to determine the best course of action.
With the rugged versions of Azure Stack Edge, teams responding to such crises can carry a device running Cognitive Services in their backpack. They can upload unstructured data like maps, images, pictures of documents and then extract content, translate, draw relationships among entities, and apply a search layer. With these cloud AI capabilities available offline, at their fingertips, response teams can find the information they need in a matter of moments. In Satya’s Ignite 2019 keynote, Dean Paron, Partner Director of Azure Storage and Edge, walks us through how Cognitive Services in Azure Stack Edge can be applied in such disaster relief scenarios (starting at 27:07):
Transforming customer support with call center analytics
Call centers are a critical customer touchpoint for many businesses, and being able to derive insights from customer calls is key to improving customer support. With Cognitive Services, businesses can transcribe calls with Speech to Text, analyze sentiment in real-time with Text Analytics, and develop a virtual agent to respond to questions with Text to Speech. However, in highly regulated industries, businesses are typically prohibited from running AI services in the cloud due to policies against uploading, processing, and storing any data in public cloud environments. This is especially true for financial institutions.
A leading bank in Europe addressed regulatory requirements and brought the latest transcription technology to their own on-premises environment by deploying Cognitive Services in containers. Through transcribing calls, customer service agents could not only get real-time feedback on customer sentiment and call effectiveness, but also batch process data to identify broad themes and unlock deeper insights on millions of hours of audio. Using containers also gave them flexibility to integrate with their own custom workflows and scale throughput at low latency.
These stories touch on just a handful of the organizations leading innovation by bringing AI to where data lives. As running AI anywhere becomes more mainstream, the opportunities for empowering people and organizations will only be limited by the imagination.
Visitors can explore the Mont-Saint-Michel through an AI and mixed-reality-powered experience at Seattle’s Museum of History & Industry
SEATTLE — Nov. 21, 2019— Seattle’s Museum of History & Industry (MOHAI) and Microsoft Corp. on Thursday announced the opening of a new exhibit, “Mont-Saint-Michel: Digital Perspectives on the Model,” which features a unique blend of 17th and 21st century technology.
Powered by Microsoft AI and mixed-reality technology as well as the recently released HoloLens 2 device, the interactive exhibition transports visitors into a holographic tour of the picturesque Mont-Saint-Michel, a medieval monastery perched atop a remote tidal island off the coast of Normandy, France.
The virtual experience is complemented by a physical relief map of the Mont-Saint-Michel, an intricate, three-dimensional model of the landmark. Entirely crafted by hand in the 1600s by the resident Benedictine monks, the 1/144-scale model precisely depicts the monument in such intricate detail that maps like this were considered valuable strategic tools to leaders like Napoleon and King Louis XIV, who considered the maps military secrets and hid them from public view.
“The Museum of History & Industry is honored to share this icon of world history, enhanced by leading-edge technology, to create a unique experience born of innovations both past and present,” said Leonard Garfield, MOHAI’s executive director. “More than 300 years separate the remarkable relief map and today, but the persistent human drive toward invention and creativity bridges those years, reflecting the unbroken quest for greater understanding and appreciation of the world around us.”
The opening of the exhibit is timed with the 40th anniversary of the Mont-Saint-Michel being designated as a UNESCO World Heritage Site. This is the first time the relief map, as well as the mixed-reality experience, has been in North America.
“The relief maps were technological marvels of Louis XIV and Napoleon’s time. It’s exciting to see how we can blend old and new technology to unlock the hidden treasures of history, especially for younger generations,” said Brad Smith, president of Microsoft. “This exhibit provides a unique model for preserving cultural heritage around the world, something Microsoft is committed to through our AI for Good program.”
The Mont-Saint-Michel experience is an example of Microsoft’s AI for Cultural Heritage program, which aims to leverage the power of AI to empower people and organizations dedicated to the preservation and enrichment of cultural heritage. Microsoft is working with nonprofits, universities and governments around the world to use AI to help preserve the languages we speak, the places we live and the artifacts we treasure. For example, earlier today Microsoft announced it is working with experts in New Zealand to include te reo Māori in its Microsoft Translator application, which will enable instant translations of text from more than 60 languages into te reo Māori and vice versa. This will be one of the first indigenous languages to use the latest machine learning translation technology to help make the language accessible to as many people as possible. The AI for Cultural Heritage program is the fourth pillar of Microsoft’s AI for Good portfolio, a five-year commitment to use AI to tackle some of society’s biggest challenges.
The relief map is on loan to MOHAI from the Musée des Plans-Reliefs in Paris, which houses more than 100 historically significant and well-preserved relief maps. The relief map of Mont-Saint-Michel is considered the museum’s crown jewel.
“One of the challenges in the history of art is the relationship with the public. To gain the attention, to capture the view or the interest of the public, is not always evident,” said Emmanuel Starcky, director, Musée des Plans-Relief. “With the HoloLens technology, you have now the possibility to realize immersive experiences in art, where you still see the reality but have more information about it. It will be a unique experience for the American public to discover the relief map, its condition in the 17th century and its evolution through three centuries, as well as reflect on the purpose of those relief maps.”
Drawing from hundreds of thousands of detailed images, Iconem, a leader in the digital preservation of cultural heritage sites, used Microsoft AI to create a photorealistic 3D digital model of the historic structure. Then, French mixed-reality specialists at HoloForge Interactive developed a unique Microsoft HoloLens experience to draw people into the artifact like never before.
The “Mont-Saint-Michel: Digital Perspectives on the Model” exhibit, including both the original relief map and mixed-reality experience, will be on display at MOHAI Nov. 23, 2019 through Jan. 26, 2020.
MOHAI is dedicated to enriching lives through preserving, sharing, and teaching the diverse history of Seattle, the Puget Sound region, and the nation. As the largest private heritage organization in the State of Washington; the museum engages communities through interactive exhibits, online resources, and award-winning public and youth education programs. For more information about MOHAI, please visit mohai.org, or call (206) 324-1126. Facebook: facebook.com/seattlehistory Twitter: @MOHAI
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NIWA and Microsoft Corp. are teaming up to make artificial intelligence handwriting recognition more accurate and efficient in a project that will support climate research.
The project aims to develop better training sets for handwriting recognition technology that will “read” old weather logs. The first step is to use weather information recorded during a week in July 1939 when it snowed all over New Zealand, including at Cape Reinga.
NIWA climate scientist Dr. Andrew Lorrey says the project has the potential to revolutionise how historic data can be used. Microsoft has awarded NIWA an AI for Earth grant for the artificial intelligence project, which will support advances in automating handwriting recognition. AI for Earth is a global programme that supports innovators using AI to support environmental initiatives related to water, climate change, sustainable agriculture and biodiversity.
Microsoft’s Chief Environmental Officer, Lucas Joppa, sees a project that could quite literally be world-changing. “This project will bring inanimate weather data to life in a way everyone can understand, something that’s more vital than ever in an age of such climate uncertainty.
“I believe technology has a huge role to play in shining a light on these types of issues, and grantees such as NIWA are providing the solutions that we get really excited about.”
Dr. Lorrey has been studying the weather in the last week of July 1939 when snow lay 5 cm deep on top of Auckland’s Mt. Eden, the hills of Northland turned white and snow flurries were seen at Cape Reinga. “Was 1939 the last gasp of conditions that were more common during the Little Ice Age, which ended in the 1800s? Or the first glimpse of the extremes of climate change thanks to the Industrial Revolution?”
Weather records at that time were meticulously kept in logbooks with entries made several times a day, recording information such as temperature, barometric pressure and wind direction. Comments often included cloud cover, snow drifts or rainfall.
“These logs are like time machines, and we’re now using their legacy to help ours,” Dr. Lorrey says.
“We’ve had snow in Northland in the recent past, but having more detail from further back in history helps us characterise these extreme weather events better within the long-term trends. Are they a one-in-80-year event, do they just occur at random, can we expect to see these happening with more frequency, and why, in a warming climate, did we get snow in Northland?”
Until now, however, computers haven’t caught up with humans when it comes to deciphering handwriting. More than a million photographed weather observations from old logbooks are currently being painstakingly entered by an army of volunteer “citizen scientists” and loaded by hand into the Southern Weather Discovery website. This is part of the global Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, which aims to produce better daily global weather animations and place historic weather events into a longer-term context.
“Automated handwriting recognition is not a solved problem,” says Dr. Lorrey. “The algorithms used to determine what a symbol is — is that a 7 or a 1? — need to be accurate, and of course for that there needs to be sufficient training data of a high standard.” The data captured through the AI for Earth grant will make the process of making deeper and more diverse training sets for AI handwriting recognition faster and easier.
“Old data is the new data,” says Patrick Quesnel, Senior Cloud and AI Business Group Lead at Microsoft New Zealand. “That’s what excites me about this. We’re finding better ways to preserve and digitise old data reaching back centuries, which in turn can help us with the future. This data is basically forgotten unless you can find a way to scan, store, sort and search it, which is exactly what Azure cloud technology enables us to do.”
Dr. Lorrey says the timing of the project is especially significant. “This year is the 80th anniversary of The Week It Snowed Everywhere, so it’s especially fitting we’re doing this now. We’re hoping to have all the New Zealand climate data scanned by the end of the year, and quality control completed with usable data by the end of the next quarter.”
Ends. About NIWA The National Institute of Water and Atmospheric Research (NIWA) is New Zealand’s leading provider of climate, freshwater and ocean science. It delivers the science that supports economic growth, enhances human well-being and safety and enables good stewardship of our natural environment. About Microsoft Microsoft (Nasdaq “MSFT” @microsoft) enables digital transformation for the era of an intelligent cloud and an intelligent edge. Its mission is to empower every person and every organization on the planet to achieve more.
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Delivering over 40 million shipments every year, Romania’s preferred courier company Urgent Cargus is taking it one step further. With its system managing between 110,000 – 150,000 shipments at any given time and a fleet of over 2,600 courier vehicles, Urgent Cargus found itself handling millions of different requests. In such a large operation, with an even larger volume of orders to fulfill, the company needed a way to predict how much data processing and storage was required to deliver on its commitment to customers.
“We operate in an unpredictable industry. Depending on the season or festivity, we often find ourselves needing to handle a very high volume of deliveries throughout the year,” said Chief Information Officer, Marian Pletea at Urgent Cargus. “There can be as many as more than one million new requests every 24 hours, which makes it impossible to predict what data storage resource we require.”
“We also wanted to mitigate the risk of system downtime. Without the right availability, we don’t ship parcels at the right rate, nor receive the customers’ bookings, which could lead to serious business blockages.”
To address this issue, Urgent Cargus moved its customer job booking application onto the cloud and adopted a Platform-as-a-Service approach – purchasing additional resources when and if they need it. A move that gives a self-service option to customers, from booking to invoicing, as well as minimizing the risk of downtime in the event of an unpredictable peak.
According to Marian: “We recognize AI as a transformative technology, which is why we’re testing it to automate certain services to make our customers’ lives easier. This will also free-up our employees to tackle more.”
Data driving cross-border deliveries
With parcels placed into the system, sorted and distributed, companies like Budapest-headquartered Waberer’s International specialize in delivering goods and supplies over long-distances across Europe – getting packages to their destination as quickly as possible. Racking up nearly 500 million kilometres of road travel each year, the company meets demanding customer requirement with a fleet of over 4,300 modern vehicles and its bespoke logistics warehouse.
Needing to move away from hand-written notes and paper-based spreadsheets, Waberer’s International digitized its scheduling processes to better account for every journey and the over 1,000 transactions they process each day. With the help of AI, the company developed a planning platform called WIPE. WIPE uses complex algorithms to allocate drivers, load and journey schedules in the most efficient way. It’s a one-stop-shop for Waberer’s International to oversee all of their logistical operations in one place. This automated process means the business can track deliveries at all times and decide where and when resources are best placed.
In addition to driving efficiencies in resourcing and deliveries, the company has also been able to improve bottom-line performance. Thanks to the automated truck scheduling function, Waberer’s International achieved a better than an industry-average loaded ratio of 92%.
With an ever-increasing and demanding customer base, and some retailers promising same-day delivery, there’s no doubt that postal services and logistics companies face many challenges.
By digitizing processes and gleaning predictive insights with technology like the cloud and AI, these examples show how data-driven decisions make processes more efficient and customers more satisfied. Perhaps AI should be top of everyone’s wish list this year!
For all three, Microsoft’s aim is to play a supporting role to help doctors and researchers find ways to improve health care using AI and machine learning.
“The health care providers are the experts,” said Prashant Gupta, Program Director in Azure Global Engineering. “We are the enabler. We are empowering these health care consortiums to build new things that will help with the last mile.”
In the Forus Health project, that “last mile” started by ensuring image quality. When members of the consortium began doing research on what was needed in the eyecare space, Forus Health was already taking the 3nethra classic to villages to scan hundreds of villagers in a day. But because the images were being captured by minimally trained technicians in areas open to sunlight, close to 20% of the images were not high quality enough to be used for diagnostic purposes.
“If you have bad images, the whole process is crude and wasteful,” Gupta said. “So we realized that before we start to understand disease markers, we have to solve the image quality problem.”
Now, an image quality algorithm immediately alerts the technician when an image needs to be retaken.
The same thought process applies to the cardiology and pathology consortiums. The goal is to see what problems exist, then find ways to use technology to help solve them.
“Once you have that larger shared goal, when you have partners coming together, it’s not just about your own efficiency and goals; it’s more about social impact,” Gupta said.
And the highest level of social impact comes through collaboration, both within the consortiums themselves and when working with organizations such as Forus Health who take that technology out into the world.
Chandrasekhar said he is eager to see what comes next.
“Even though it’s early, the impact in the next five to 10 years can be phenomenal,” he said. “I appreciated that we were seen as an equal partner by Microsoft, not just a small company. It gave us a lot of satisfaction that we are respected for what we are doing.”
Top image: Forus Health’s 3nethra classic is an eye-scanning device that can be attached to the back of a moped and transported to remote locations. Photo by Microsoft.
Leah Culler edits Microsoft’s AI for Business and Technology blog.