Tag: Artificial Intelligence

More than 140,000 business leaders have immersed themselves in AI Business School since Microsoft introduced the online leadership series two months ago. Now, the school is adding materials designed specifically for government agencies.

The goal: To help government leaders, policy makers and administrators learn more about how technologies infused with artificial intelligence can help their constituents, particularly when it comes to the delivery of important services ranging from applying for a construction permit to getting access to health assistance.

On May 28, AI Business School will launch a new learning path that describes important considerations and potential opportunities for government organizations as they evaluate how AI can help governments become more agile, consistent and efficient, and also better deliver services to their citizens.

“Leaders in the public sector are often faced with unique challenges when considering how to apply AI to improve the speed and quality of the government services they offer their citizens,” says Mitra Azizirad, corporate vice president for Microsoft AI marketing. “The opportunities and scenarios for AI in the public sector are ever increasing, which can make deciding where and how to apply it quite daunting. This is precisely why we expanded Microsoft’s AI Business School to now include a specifically tailored and targeted public sector curriculum to help these leaders address their citizens’ unique needs.”

Anthony Salcito, Microsoft’s vice president for government, said the new government learning path is a win for both government agencies and the constituents they serve.

“Helping governments reach and serve people through cloud services is a key priority for us,” Salcito said. “Citizens want to be able to interact with governments in real time and from any device. AI enables a two-way conversation where citizens can contribute information and receive insights in return.”

“It’s really thinking about 21st century workforce skills,” he added. “The fact is that government workers across the board – and especially decision makers – don’t necessarily have that familiarity or depth on AI. This new learning path is a way to get them introduced to the concept and to understand why it’s important in the context of government work.”

The new content for government leaders will include:

• A video lecture from Peter Zemsky, Eli Lilly chaired professor of innovation and strategy at INSEAD graduate business school, on why and how governments can identify the right opportunity to use AI.
• A case study illustrating how the city of Espoo, Finland, is working to modernize life for its residents.
• A demo showcasing how governments can use intelligent bots to help citizens access resources.

With the assistance of AI, experts say governments can do things like find ways to reduce the time people spend waiting in line for services or find ways to improve public safety.

AI tools also can help government organizations analyze data to find better ways of helping constituents. They also can be used to create intelligent assistants that get people answers to simple questions faster, freeing up government employees to handle more complex requests.

For example, the city of Espoo has been using AI to analyze how its citizens access services, with the goal of figuring out how to serve people more efficiently and effectively. Päivi Sutinen, Espoo’s director of city-as-a-service development, said the experimentation revealed that by using AI and customer data more effectively, they were able to allocate resources more efficiently. That’s helping put the city on a path towards a more sustainable future.

“We launched our experiment because we wanted to find out whether AI can help us target our services proactively,” Sutinen said. “The answer is a strong yes.”

Microsoft says governments of all sizes can benefit from the AI Business School’s new learning path.

“We believe this course is valuable for government decision makers at all levels — from small municipalities to large cities,” said Salcito. “The beauty of artificial intelligence technologies is their scalability.”

Jaime Pereña is a director of AI marketing at Microsoft.

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Today, Microsoft is releasing The Future Computed: AI and Manufacturing. This new book provides an in-depth look at how artificial intelligence (AI) is transforming the manufacturing sector by optimizing digital operations and driving efficiencies, enabling new products and services, and allowing for safer work environments. The book also offers a timely look at how society can respond to some of the challenges AI creates, and the need to develop new laws and regulations to address workforce disruption and develop AI in an ethical and responsible manner.

Written by Greg Shaw – co-author of Hit Refresh with Microsoft CEO Satya Nadella and The Ability Hacks about technology for people with disabilities – this book is the second in a series that began with the release last year of The Future Computed: Artificial Intelligence and its role in society.

In an era when digital technology is changing almost every aspect about how people live, work, play, and learn, we believe it is important to think carefully about the complex questions that AI raises. Through our Future Computed series, we hope to encourage a broad set of conversations about how AI can empower people in a trusted and respectful environment that benefits everyone.

We decided to focus the second Future Computed book on manufacturing because the industry plays such a central role in the global economy, and because, as in previous industrial revolutions, it is once again at the forefront of adopting new transformational technologies.

This means manufacturing is one of the industries that is leading the way in exploring the full potential of AI. It also means manufacturers already find themselves responding to some of the challenges of AI — challenges like how to optimize processes to keep up with the new speed of business, how to ensure their existing and incoming workforce has the right skills to build and leverage AI, and how to create the right organizational culture to realize the benefits of data-driven decision making.

In the book, you’ll hear from a range of customers including ABB, Colfax, Jabil, Tetra Pak, Toyota Materials Handling and ZF about the progress they are making on their AI journey. You’ll meet Patrick Bass, CEO of thyssenkrupp North America, who led the effort to implement a real-time cloud-based predictive maintenance solution that is maximizing uptime for 12 million elevators that move 1 billion people every day. And hear from Manuel Torres, a self-taught engineer who has used AI to transform his paper company into a leading supplier of composite material for airplane manufacturers like Boeing and Airbus.

The book also looks at how manufacturers will need to engage with governments and civil society to help craft new regulatory frameworks, guiding the use of this new technology as the industry transforms. These frameworks should address key societal challenges, including the need to retrain workers to take on new roles and be part of a talent supply chain capable of realizing the potential of AI in manufacturing. They should also address how to store the data generated from connected supply chains and digital factories safely, securely, and in ways that respect privacy and ensure that AI is used ethically. Drawing on insights from customers and policymakers from around the world The Future Computed: AI and Manufacturing offers Microsoft’s perspective on how we might move forward on these important issues.

The Future Computed: AI and Manufacturing does not pretend to have all the answers. But we hope it will play a role in furthering discussions about how to deliver this new technology responsibly and help manufacturers better understand the steps they need to take in their AI journey.

In writing the book, Greg conducted interviews with Microsoft manufacturing customers who are at the forefront of figuring out how AI can drive success for their companies. He also interviewed workforce experts, union leaders, and policymakers from around the world and identified some common themes across these conversations.

The Future Computed: AI and Manufacturing points out the critical importance of cultural change as part of the digital transformation and the need for strong senior leadership in overseeing this shift. It also highlights the way in which the managers inside manufacturing operations who are closest to the workforce are often the most sensitive to AI’s impact on employees and are focused on improving worker safety and job satisfaction. And, while there is notable positivity from leaders in manufacturers around the impact of AI on jobs, there is also an understanding that a lot of work is needed to reinvent how we prepare people for the workforce.

The advent of AI makes this an exciting time to be in manufacturing. But it is also a time filled with great uncertainty. For anyone interested in learning more about why AI is so important to the future of manufacturing, I hope The Future Computed: AI and Manufacturing offers a useful guide. I also love the final remark that suggests leaders of today should lead with optimism.

I encourage you to download a copy here. You can also find out more about the people and companies profiled in the book and access additional information by visiting Microsoft’s AI and Manufacturing website. Additional resources can also be found in the Microsoft AI Business School.

Tags: AI, Future Computed, manufacturing

From financial robo-advisors to virtual health assistants, enterprises across every industry are leveraging virtual assistants to create outstanding customer experiences and help employees maximize their time. As artificial intelligence technology continues to advance, virtual assistants will handle more and more mundane and repetitive tasks, freeing people to devote more time and energy to more productive and creative endeavors.

But like any technology, conversational AI can pose a significant risk when it’s developed and deployed improperly or irresponsibly, especially when it’s used to help people navigate information related to employment, finances, physical health, and mental well-being. For enterprises and society to realize the full potential of conversational AI, we believe bots need to be designed to act responsibly and earn user trust.

Last year, to help businesses meet this challenge, we shared 10 guidelines for building responsible conversational AI. Today, we’d like to illustrate how we’ve applied these guidelines in our own organization and share new resources that can help developers in any industry do the same.

Responsible bot guidelines

In November 2018, Lili Cheng, corporate vice president of Microsoft AI and Research, announced guidelines designed to help organizations develop bots that build trust in their services and their brands. We created these bot guidelines based on our own experiences, our research on responsible AI and by listening to our customers and partners. The guidelines are just that — guidelines. They represent the things that we found useful to think about from the very beginning of the design process. They encourage companies and organizations to think about how their bot will interact with people and how to mitigate potential risks. Ultimately, the guidelines are all about trust, because if people don’t trust the technology, they aren’t going to use it.

Designing bots with these guidelines in mind  

The bot guidelines have already started to play a central role in our own internal development processes. For example, our marketing team leveraged the guidelines while creating an AI-based lead qualification assistant that emails potential customers to determine their interest in Microsoft products and solutions. The assistant uses natural language processing to interact with customers, ensuring they receive the information they need or are directed to the Microsoft employee who can best help them. To provide a useful example, we’ve highlighted the ways in which our marketing team has approached three of the guidelines below.

• Articulate the purpose of your bot and take special care if your bot will support consequential use cases.

Since the assistant would be customer-facing, the marketing team recognized the importance of completely thinking through every aspect of how the bot would work. Before building the lead qualification assistant, they created a vision and scope document that outlined the bot’s expected tasks, technical considerations, expected benefits and end goals in terms of business performance. By outlining these details early in the design process, the team was able to focus on developing and refining only necessary capabilities and deploy the bot sooner. Creating this document also helped them identify and design for edge cases that the bot was likely to encounter and establish a set of effective reliability metrics.

• Ensure a seamless hand-off to a person where the person-bot exchange leads to interactions that exceed the bot’s competence.

While considering these edge use cases, the marketing team identified a couple of scenarios in which a handoff to a person would be required. First, if the assistant can’t determine the customer’s intent (for example, the response is too complex or lengthy), then the assistant will flag the conversation for a person. The person can then direct the assistant to the next best course of action or respond directly to the customer. The person also can use key phrases from the conversation to train the assistant to respond to similar situations in the future.

Secondly, the customer may ask something that the assistant doesn’t have pre-programmed. For example, a student may request information about our products and solutions but not be interested in making a purchase. The assistant would flag the conversation instead of forwarding it to sales. A person can then reply through the assistant to help the student learn more.

• Ensure your bot is reliable

To help ensure the bot is performing as designed, the marketing team reviews a set of reliability metrics (such as the accuracy of determining intent or conversation bounce rate) through a regularly updated dashboard. As the team updates and improves the bot, it can closely analyze the impact of each change on the bot’s reliability and make adjustments as necessary.

Helping developers put the guidelines into practice

We have taken lessons learned from experiences like this one and important work from our industry-leading researchers to create actionable and comprehensive learning resources for developers.

As part of our free, online AI School, our Conversational AI learning path enables developers to start building sophisticated conversational AI agents using services such as natural language understanding or speech translation. We have recently added another module, Responsible Conversational AI, to this learning path. It covers how developers can design deeply intelligent bots and also ensure they are built in a responsible and trustworthy manner. In this learning path, developers can explore topics such as bot reliability, accessibility, security and consequential use cases and learn how to mitigate concerns that often arise with conversational AI. We have also created a Conversational AI lab in which a sample bot guides developers through a responsible conversational AI experience and explains its behavior at each point of the experience.

Learn more

We encourage you to share the AI lab and the Responsible Conversational AI learning module with technical decision-makers in your organization.

You can also go to our new AI Business School to learn more about how Microsoft has integrated AI throughout our business and how your organization can do the same.

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The summer of 2009 was a nightmare for my family. Our 3-month-old son Sergio was ill, and doctors could not diagnose his condition. Our family went on a long journey to find an answer. Two years after his symptoms began, in 2011, we learned that he had Dravet Syndrome.

Neurologists were working to find a diagnosis and Sergio was being treated while they searched. At nine months of age, he was given a drug that was contraindicated for what ultimately was his condition. Soon after receiving it, he started to have dozens of seizures per day.  We stop counting the seizures, and wondered why, in the age of computers, neurologists don’t use computers and data to improve the diagnosis process.

This event changed my life. I founded the Dravet Syndrome Foundation in Spain in 2011 after learning of Sergio’s condition, and later, Foundation 29. I spent seven years trying to find a cure. Unfortunately, finding a cure for a rare disease is very challenging. Instead, using resources available to me as a Microsoft employee, and with foundation volunteers, created a diagnostic program for children with Dravet Syndrome, called Dx29. To date, it has provided diagnoses for more than 700 patients worldwide. It is now available for clinicians to use free of charge.

The Long Wait for a Rare Disease Diagnosis

Patients live an average of 4.8 years for a rare disease diagnosis. In the meantime, they contend with the risk of medical errors and severe side effects.  Patients visit an average of 7.3 specialists, with 40% of patients reporting that a delayed diagnosis had a significant or very marked impact on their condition. It is estimated that 6-8% of the world is affected by a rare disease, meaning that improvements in diagnosis procedures could impact 460-620 million people.

The Need for Clinical Data Integration

The conventional diagnosis process is not designed for the complex biology behind rare diseases. It usually starts with a clinical consultation. A physician requests a genomic test, sending along the biological samples and the symptoms (phenotypes) already identified. The sequencing is performed and bioinformaticians (often manually) analyze the large amount of data produced. To carry out this complex analysis, they use the symptoms identified by the physician to guide their search.

The physician’s and bioinformation’s data are not integrated, so these professionals are disconnected. Those conducting the gene filtering have partial phenotypic information but are unable to collect more data because only physicians have full access to patients and their records. The genetic report the physician receives would likely be different if more patient information was available during gene filtering. Clinical decisions made based on the genetic data could be different with more data. How can bioinformaticians check if a given gene variant in the patient is producing a concrete phenotype? How is the patient information put in the hands of bioinformaticians? There is an information gap issue.

Satya Nadella Empowers Employees to Help

At the 2017 Microsoft employee hackathon in Spain, one of my best friends, and Microsoft colleague, Sacha Arozarena, suggested we create a bot to diagnose patients with rare diseases. After just three days of intensive work, our prototype was able to suggest symptoms and navigate the user to a potential diagnosis. It was still a proof of concept, but we won the Spanish hackathon. The most important achievement of this work was the connections the prototype created for us.

That same month, I heard Satya Nadella discuss his son’s medical condition while he presented at Microsoft Ready, so I sent him an email asking for help. He replied within five minutes, connecting me with Microsoft’s Research team. Through this connection, I learned about Microsoft’s efforts in several areas:  a new Genomics team, a team working on medical natural language processing, and the company’s investments and efforts towards bringing artificial intelligence to health science.

Using the Cloud and AI to Speed Diagnosis

One year ago, colleagues and I founded Foundation 29, a non-profit organization with the mission to improve the lives of patients with rare diseases through faster, better diagnosis. The foundation is developing solutions to facilitate diagnosis, with the intention of distributing them to every physician in the world. Dx29 is the name of this effort. The goal is to reimagine and democratize diagnosis.

The tool we developed uses Artificial Intelligence (AI) to close the information gap. The gene filtering of most routine low-level cases can be automated with AI, allowing bioinformaticians and specialists to focus on the most challenging cases where human intervention is required. Physicians can drive automatic genetic analysis simply by identifying symptoms in the tool. The physician’s role comes back to the center of the process, focusing on the patient and doing symptom identification and differential diagnosis.

Dx29 does not make a diagnosis, but enhances the physicians’ skills. It gives physicians a tool that augments their capabilities by hiding the complexity of genomics and allowing them to focus on clinical diagnosis, something they are already experts on.

The process starts by performing automatic symptom identification and codification from medical records. It then allows physicians to navigate the complexity of gene identification by simply selecting identified symptoms in the tool.  In the final step, once enough symptoms have been matched with the genetic information, Dx29 presents a ranked list of potential conditions for the physician to further evaluate and decide how to proceed. The foundation did the first medical tests last December with promising results. Our goal is to make the tool available to the medical community this spring and find a business model to secure the continuity of the project.

Thanks to Microsoft and Global Organizations

Dx29 is possible because of help from Microsoft and its employees. It is impossible to list all Microsoft employees who joined forces to collaborate on this initiative. Foundation 29 and in particular, Dx29 are honored by the privilege of working with Microsoft software engineers, product groups, and consultants. Architects from Microsoft Services, data engineers, data scientists and the legal department provided us with advice on privacy and data protection.

I am proud to work for a company that empowers employees to achieve more. A lack of diagnosis is not only a stressful situation for patients and families, but also for healthcare professionals. Without a diagnosis, an appropriate treatment is not possible. With all the help received so far, Foundation 29 aims to empower physicians with the right tool to provide an accurate diagnosis.

I would like to thank the following organizations for their contributions to the development of the Dx29 tool and its pilot:

• Centro de Investigación Biomédica en red de Enfermedades Raras (CIBERER), Madrid, Spain
• Hospital La Paz de Madrid, Madrid, Spain
• NIMGenetics, Madrid, Spain,
• Idibell, Barcelona, Spain
• The Global Commission on Rare Diseases

Rare disease patients are exceptions in clinical routines, but they drive the medical community towards precision medicine. Precision medicine should not be limited to exceptional cases, but spread to all patients, improving the standard of care for all.

Time is ticking. I know I won’t be able to find a cure for Sergio and he will have to live with Dravet Syndrome all his life. But having the possibility of creating a tool to speed up and improve diagnosis process for other children is a strong motivation for me, my family and the community around us. Helping others is sometimes the only way to heal your own wounds.

Learn more about Dx29, the Global Commission and how AI can support diagnosis and read more from the Microsoft In Health Blog on the Global Commission.