This summer, young women in San Francisco and Seattle spent a weekend taking their creative problem solving to a whole new level through the power of artificial intelligence. The two events were part of a Microsoft-hosted AI boot-camp program that started last year in Athens, then broadened its reach with events in London last fall and New York City in the spring.
“I’ve been so impressed not only with the willingness of these young women to spend an entire weekend learning and embracing this opportunity, but with the quality of the projects,” said Didem Un Ates, one of the program organizers and a senior director for AI within Microsoft. “It’s just two days, but what they come up with always blows our minds.” (Read a LinkedIn post from Un Ates about the events.)
The problems these girls tackled aren’t kid stuff: The girls chose their weekend projects from among the U.N. Sustainable Development Goals, considered to be the most difficult and highest priority for the world.
The result? Dozens of innovative products that could help solve issues as diverse as ocean pollution, dietary needs, mental health, acne and climate change. Not to mention all those young women – 129 attended the U.S. events – who now feel empowered to pursue careers to help solve those problems. They now see themselves as “Alice,” a mascot created by the project team to represent the qualities young women possess that lend themselves to changing the world through AI.
Organizers plan to broaden the reach of these events, so that girls everywhere can learn about the possibility of careers in technology.
As part of our AI for Earth commitment, Microsoft supports five projects from Germany in the areas of environmental protection, biodiversity and sustainability. In the next few weeks, we will introduce the project teams and their innovative ideas that made the leap into our global programme and group of AI for Earth grantees.
Measuring and improving sustainable air quality in cities with transparant results is Hamburg-based startup Breeze’s mission. Founded in 2015, the award-winning company develops small, low-cost sensors that can be installed in almost any location, measuring pollutants such as soot, nitrogen oxides, ammonia, ozone or particulate matter, while also identifying their sources.
While vehicles are a common source of pollution, large construction sites, for example, can also greatly increase air pollution in short periods of time. A local portal publishes Breeze’s collected data in real time, so that affected residents can learn about the current situation at any given moment. In addition, Breeze has developed a comprehensive catalog of measures that helps cities and communities specifically improve the situation on the ground.
From the beginning, Breeze has processed the data of its fully networked sensors in the Azure cloud. Breeze founder Robert Heinecke now wants to take his project to the next level with the help of AI – relying on the support of AI for Earth. Breeze has already received $ 12,000 in cloud credits which will be used to set up a new machine learning development environment in Azure. “We have now set up our own AI experimental laboratory to test how AI can support us even better in our added value,” explains Heinecke.
So far, Heinecke and his team have identified four areas in which AI can be used. Firstly, AI should significantly improve the quality of the measurement data and draw an even more accurate picture of the data on site by measuring both measurement errors of individual devices as well asexcluding environmental influences from the sensor data. At the same time, AI will also be deployed in a predictive maintenance capacity, to forecast when sensors need to be serviced or even replaced.
AI will also help make precise predictions about the development of air quality in the future, such as linking weather data to information from its own measuring stations. By doing so, ill or particularly sensitive people, such as those with asthma, can prepare for harsher conditions in advance. Lastly, AI will also help to streamline Breeze’s consulting offer by accurately calculating which of the 3,500 identified measures can improve the air quality at a particular location the best.
Currently, pilot projects are already running in Hamburg, Moers and Neckarsulm (Germany), and Heinecke and his team are already in negotiations with numerous other cities, although there can sometimes be friction. In Heinecke’s words, “the mills of the administration grind slowly. Some cities may also prefer not to know exactly how bad the air really is, because then they would have to act.”
AI for Earth The AI for Earth program helps researchers and organizations to use artificial intelligence to develop new approaches to protect water, agriculture, biodiversity and the climate. Over the next five years, Microsoft will invest $ 50 million in “AI for Earth.” To become part of the “AI for Earth” program, developers, researchers and organizations can apply with their idea for a so-called “Grant”. If you manage toconvince the jury of Microsoft representatives, you´ll receive financial and technological support and also benefit from knowledge transfer and contacts within the global AI for Earth network. As part ofMicrosoft Berlin´s EarthLab and beyond, five ideas have been convincing and will be part of our “AI for Earth” program in the future in order to further promote their environmental innovations.
Transform recently sat down with several Microsoft customers at an event highlighting emerging trends in data and artificial intelligence (AI). We spoke with Jean Lozano, chief technology officer at MediaValet, a cloud-based digital asset management (DAM) system, which helps customers of all sizes manage their growing digital media libraries.
TRANSFORM:What is a DAM and what business problem does it solve?
JEAN LOZANO: A DAM is like a video or photo library, but it can handle way more than that. It’s typically used by enterprise marketing teams to provide a single source of truth for all brand and marketing content.
The big challenge with digital media is that it’s unstructured by nature, so discoverability can be a problem. A lot of companies invest tens of thousands of dollars in creating digital media and infographics, but its lack of searchable data makes them hard to find.
A DAM implementation contains anywhere from 10,000 to several-million digital media [items] that are put into a central repository and tagged to make them discoverable. Having just one person as the curator of the digital library is way too much, and that’s where AI comes in.
TRANSFORM: How does AI help you serve your customers?
LOZANO: We’re betting big on Azure AI to make MediaValet the most intelligent DAM out there. We’ve seen it in various deal cycles that we would not have otherwise won. It’s just a matter of demonstrating capabilities.
So, for example, one of the hottest technologies that we can demo is our capability for video indexing, which other DAMs cannot offer right now. Video is the fastest growing media out there, so when our customers can actually see their videos getting analyzed and made discoverable, they can see the value it adds.
As an example, we have a huge video production house as a customer. Every time they do a shoot, it’s a 500-gigabyte upload to MediaValet. But, they also have to generate video transcriptions. With video indexing, we [provide] the transcriptions as soon as they load it on our system, so they don’t have to send the files [elsewhere] for manual transcription. Does that improve their workflow? Definitely.
TRANSFORM: Have your customers fully embraced the changes and the new technologies?
LOZANO: Many of our customers are digital asset curators, and some of them probably fear that AI is going to replace them. But the guiding principle for AI is assistance, not replacement. AI is going to make their lives easier. It will enrich the data that they’re producing, but it doesn’t replace them.
TRANSFORM: How does AI make life easier for a digital asset curator?
LOZANO: We have a couple of customers that are sports franchises that are creating millions of images. These teams have been there for decades and decades. One hockey team had about 50 petabytes of video clips. So, how do you work through millions of images and hundreds of thousands of hours of video?
Machine learning is actually helping with that now. For example, you can detect a jersey number and see who is the player that wears that number. We can actually make those images easy to use for marketers, or the communications team when they look for brand approved images.
Multiyear collaboration will accelerate AT&T’s “public cloud first” internal transformation and deliver new customer offerings built on AT&T’s network and Microsoft’s cloud
DALLASand REDMOND, Wash. — July 17, 2019— AT&T Communications and Microsoft Corp. are embarking on an extensive, multiyear alliance where the two companies will apply technologies, including cloud, AI, and 5G, to improve how people live and work today and in the future. Microsoft will be the preferred cloud provider for non-network applications, as part of AT&T’s broader public cloud first strategy, and will support AT&T as it consolidates its data center infrastructure and operations.
AT&T is becoming a “public cloud first” company by migrating most non-network workloads to the public cloud by 2024. That initiative will allow AT&T to focus on core network capabilities, accelerate innovation for its customers, and empower its workforce while optimizing costs.
As part of the agreement, AT&T will provide much of its workforce with robust cloud-based productivity and collaboration tools available with Microsoft 365, and plans to migrate non-network infrastructure applications to the Microsoft Azure cloud platform.
AT&T and Microsoft will together help enable a future of ubiquitous computing through edge technologies and 5G. AT&T was the first to introduce mobile 5G in the United States and expects to have nationwide 5G by the first half of 2020. Microsoft will tap into the innovation AT&T is offering on its 5G network, including to design, test, and build edge-computing capabilities. With edge computing and a lower-latency 5G connection enabled through AT&T’s geographically dispersed network infrastructure, devices can process data closer to where decisions are made. Recently, Microsoft and AT&T worked together to test an edge computing-based tracking and detection system for drones. With more connected devices and the growing demand for streaming content from movies to games, businesses and consumers require ever-increasing network capabilities.
The global scale of Microsoft’s Azure cloud and AT&T’s domestic 5G capabilities will enable unique solutions for the companies’ mutual customers. The companies will bring to market integrated industry solutions including in the areas of voice, collaboration and conferencing, intelligent edge and networking, IoT, public safety, and cyber security. The companies already have joint enterprise solutions for networking, IoT, and blockchain in market, and expect to announce additional services later in 2019. The two companies envision scenarios with 5G enabling near-instantaneous communications for a first responder who is using AI-powered live voice translation to quickly communicate with someone in need who speaks a different language.
“AT&T and Microsoft are among the most committed companies to fostering technology that serves people,” said John Donovan, CEO, AT&T Communications. “By working together on common efforts around 5G, the cloud, and AI, we will accelerate the speed of innovation and impact for our customers and our communities.”
“AT&T is at the forefront of defining how advances in technology, including 5G and edge computing, will transform every aspect of work and life,” said Satya Nadella, CEO, Microsoft. “The world’s leading companies run on our cloud, and we are delighted that AT&T chose Microsoft to accelerate its innovation. Together, we will apply the power of Azure and Microsoft 365 to transform the way AT&T’s workforce collaborates and to shape the future of media and communications for people everywhere.”
In addition to their technology collaboration, AT&T and Microsoft will work together on technology-enabled approaches and solutions aimed at social good. Both companies have been focused on addressing sustainability, accessibility, and community challenges such as homelessness and see an opportunity to support each other’s work to address urgent social needs, including Microsoft’s affordable housing initiative and the AT&T Believes campaign.
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Information set forth in this news release contains financial estimates and other forward-looking statements that are subject to risks and uncertainties, and actual results might differ materially. A discussion of factors that may affect future results is contained in AT&T’s filings with the Securities and Exchange Commission. AT&T disclaims any obligation to update and revise statements contained in this news release based on new information or otherwise.
This news release may contain certain non-GAAP financial measures. Reconciliations between the non-GAAP financial measures and the GAAP financial measures are available on the company’s website at https://investors.att.com.
*Based on analysis by Ookla® of Speedtest Intelligence® data average download speeds for Q2 2019. **GWS OneScore, September 2018.
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MvdB: Interesting. It sounds like they aren’t aiming to become banks, then, but are looking to own the customer journey and end up as competitors for part of the business.
SM: That’s correct. We’re seeing the whole insurance business travelling towards a direction where interaction with consumers becomes the key focus. The more you interact with the customer, the closer they become accustomed to your brand.
MvdB: There are a lot of companies waiting for the technology revolution, but in my opinion, it’s already here. I see lots of examples, including artificial intelligence. What are your thoughts on the perception of digital transformation in the finance industry?
SM: In banking, I’ve noticed that it’s much easier to make better decisions when investing smaller amounts of money. Similarly, you have better performance and agility as a small business with a startup mentality. It’s the same with financial institutions. They have a legacy that needs to be maintained, renewed and sometimes politics and corporate culture make it very hard, almost impossible, to be agile – and that includes adopting new technology to help them transform.
We’ve seen large financial institutions show that it’s possible to manage their legacy operations and daily business, while at the same time, almost separately, fostering a more agile startup mentality for transformation. New business ventures mean that this startup mentality must be separated which, of course, also means that more money has to be spent.
MvdB: It’s interesting to hear how other large institutions are reflecting and managing their transformation, particularly with regards to harbouring different mentalities for different areas of the business. At Microsoft, we’re still in the middle of our transformation process, and while we’re not there yet, we’re making progress and learning all the time.
SM: I honestly think that Microsoft has done a fantastic job at transforming itself with its cloud and many other services – and that’s reflected by professionals and the market in general. It’s amazing to see how you struck lightning twice. You re-invented the business and grew, when you could have chosen to remain stagnant.
MvdB: Having been here for many years, I’ve witnessed our shift from a ‘know it all’ culture to one that encourages a growth mindset, and it boils down to culture and mentality. I think we’re in the middle of a learning experience and learning mentality, where all of these small things start to matter. Do you think these two things are becoming more of a core factor of transformation, beyond simply introducing new technology?
SM: Yes absolutely. At startups, we see a culture where, if there is a problem, people sit down, and solve it. Compare this to some large corporation’s politics and red tape, and you can see how the wrong culture can discourage people, regardless of what technology is available. People like to see that their input makes a difference, and that’s much harder in huge organisations.
MvdB: If you’re operating 100,000+ people globally it’s certainly a challenge, but at the same time, you need to be sure that you spark that startup mentality in people. I think this is where the culture element in the leadership becomes a super-critical factor in changing an organisation.
SM: It comes from the top.
MvdB: Absolutely. This is true for us as well. If you take Satya as an example, he is living what he wants this company to be.
If we focus on the technological side of transformation, specifically data – do you feel that data is the most important currency today in commerce?
SM: Yes, it is. But there’s so much data, so much noise. It’s not just the data, but more about how you use that data. Everyone, including big banks, has data, but it’s the thinking behind it that counts – using data scientists, algorithms, machine learning AI and more – to get something out of it. That’s where AI, machine learning, and the people behind the algorithms, come in.
MvdB: I agree. I’m asking this question because I think there’s a lot of misperception about data being the new oil for this century and it’s a great tagline on paper, but the question, of course, is that data alone, is just data, and that’s what it will be forever.
In our last two blogs in this series, we discussed how governments are using digital assistants—often with cognitive services such as language translation built in—to engage their community in more accessible ways and support their teams.
For example, to keep Alaska’s highways open and safe during severe winter weather, the Alaska Department of Transportation and Public Facilities uses the FathymWeatherCloud solution and Microsoft Azure IoT technologies to make better, hyper-local decisions about deploying road crews. Being able to make more informed decisions with better data is helping Alaska save lives and significantly reduce road maintenance costs.
“The information we get from WeatherCloud puts us miles ahead in creating accurate forecasts,” says Daniel Schacher, Maintenance Superintendent at the Alaska Department of Transportation and Public Facilities, in this article. “We’ve become much more proactive in our responses.”
Another example of keeping vital infrastructures up and running with insight from AI and IoT solutions comes from our partner eSmart Systems. With its Connected Drone portfolio, utilities can send smart drones out on beyond-line-of-sight missions to inspect power lines and pinpoint faults and weaknesses.
Utilities are using Connected Drones to stay ahead of power grid maintenance issues and help them prevent or reduce blackouts in the communities they serve. And by using drones to inspect lines, which can be dangerous for personnel, they can keep their teams safer.
Utilities are also using Connected Drones to get power back up and running after a disaster, as was the case in Florida after Hurricane Irma. Watch this video to see how the drones helped to assess the damage quickly—inspecting hazardous areas so human inspectors wouldn’t have to be put in harm’s way. With insight from the Connected Drones, the utility company was able to know not only precisely where repairs were required, but also which crew and equipment were needed to get power restored as quickly as possible in the affected communities.
Those are just a few examples of how governments can gain insight with AI and IoT that can help them keep the infrastructures their citizens rely on up and running. To learn about more vertical and horizontal areas where your government agency can benefit from AI, read the Gartner report: “Where you should use artificial intelligence—and why.” It provides research on the potential of various use cases and offers recommendations on the most effective strategies for applying AI.
The future of mobile banking is clear. People love their mobile devices and banks are making big investments to enhance their apps with digital features and capabilities. As mobile banking grows, so does the one aspect about it that can be wrenching for customers and banks, mobile device fraud.
Problem: To implement near real-time fraud detection
Most mobile fraud occurs through a compromise called a SIM swap attack in which a mobile number is hacked. The phone number is cloned and the criminal receives all the text messages and calls sent to the victim’s mobile device. Then login credentials are obtained through social engineering, phishing, vishing, or an infected downloaded app. With this information, the criminal can impersonate a bank customer, register for mobile access, and immediately start to request fund transfers and withdrawals.
Artificial Intelligence (AI) models have the potential to dramatically improve fraud detection rates and detection times. One approach is described in the Mobile bank fraud solution guide. It’s a behavioral-based AI approach and can be much more responsive to changing fraud patterns than rules-based or other approaches.
The solution: A pipeline that detects fraud in less than two seconds
Latency and response times are critical in a fraud detection solution. The time it takes a bank to react to a fraudulent transaction translates directly to how much financial loss can be prevented. The sooner the detection takes place, the less the financial loss.
To be effective, detection needs to occur in less than two seconds. This means less than two seconds to process an incoming mobile activity, build a behavioral profile, evaluate the transaction for fraud, and determine if an action needs to be taken. The approach described in this solution is based on:
Azure Machine Learning to create a fraud classification model.
Azure PaaS services for real-time event processing and end-to-end workflow.
The architecture: Azure Functions, Azure SQL, and Azure Machine Learning
Most steps in the event processing pipeline start with a call to Azure Functions because functions are serverless, easily scaled out, and can be scheduled.
The power of data in this solution comes from mobile messages that are standardized, joined, and aggregated with historical data to create behavior profiles. This is done using the in-memory technologies in Azure SQL.
Read the Mobile bank fraud solution guide to learn details on the architecture of the solution. The guide explains the logic and concepts and gets you to the next stage in implementing a mobile bank fraud detection solution. We hope you find this helpful and we welcome your feedback.