AI is shaping a safer world. From self-driving vehicles to healthcare, learn from innovators who are creating the future.

Session four similarly focuses on Genetic Algorithms with applications to data mining and solving a prototypical problem applicable to production scheduling.

Those with it are at an increased risk of developing a dementia, making early detection critical. Researchers at Apple and the University of Tübingen believe the key might lie in iOS app usage habits. To this end, they propose a machine learning approach to reveal differences in patterns between users with and without cognitive impairment. It achieves an area under the receiver operating characteristics of 0.79, they report, indicating that it's able to correctly spot symptomatic subjects about 80% of the time. "The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state," wrote the research team in a preprint paper.

The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state. In this work we analyze 12 weeks of phone usage data from 113 older adults, 31 with diagnosed cognitive impairment and 82 without. We develop structured models of users' smartphone interactions to reveal differences in phone usage patterns between people with and without cognitive impairment. In particular, we focus on inferring specific types of phone usage sessions that are predictive of cognitive impairment. Our model achieves an AUROC of 0.79 when discriminating between healthy and symptomatic subjects, and its interpretability enables novel insights into which aspects of phone usage strongly relate with cognitive health in our dataset.

Microsoft has publicly committed $50 million over 5 years for artificial intelligence projects that support clean water, agriculture, climate, and biodiversity. But our true vision extends beyond a grant program: our data science team is working with partners and nonprofits to build a set of APIs to transform these environmental issues. Imagine machine learning models exposed through services which could differentiate between various species of animal for conservation purposes, predict agricultural yields, estimate the probability of floods, super resolve climate predictions, or classify aerial and satellite imagery into actionable maps of natural resources that could empower land use planners to optimize the use of our planet's scarce resources. In this session, we will share an initial set of APIs in private preview, as well as demonstrate the process whereby additional APIs can be added to the growing portfolio of AI for Earth APIs. Join us to learn about APIs that could literally change the way society monitors, models, and ultimately manages Earth's life support systems.

Learn how to simplify your Machine Learning workflow by using the experimentation, model management, and deployment services from AzureML. We'll demonstrate a real-world machine learning scenario using TensorFlow and Keras. You'll walk away with a clear picture of each of the AzureML services and the supporting Cloud AI infrastructure.

In September, we launched a huge set of updates for Azure Machine Learning to let you manage the end to end lifecycle of Machine Learning Development. We made it easy to scale to the cloud, deploy through containers to the cloud and the edge, use great frameworks like Tensorflow, PyTorch, Scikit, and more, and gave you a great monitoring and management experience. In this session, you'll learn how you can easily use Azure Machine Learning to build and deploy models for any of your applications.

Windows Ink is an extremely powerful feature in Windows 10 and learn how your application gets the fastest ink on Windows across even more devices. Learn to easily incorporating inline handwriting in to your application and use Ink Analysis to differentiate your application on Windows.

In today's world of multi-layered applications generating a huge amount of telemetry data, getting Insights that will significantly shorten your Root Cause Analysis of an Incident, or even prevent it is a challenging task. In this session we'll show how we're harnessing the power of Artificial Intelligence & ML technology combined with Azure domain knowledge to drive Insights to Azure Management and Monitoring customers, and provide out of the box interactive experiences.

AI/ML expert Paige Bailey takes you on a tour of the powerful services available on Azure. You'll see how to take your predictive model to production, dynamically train it online with streaming updates, and add realtime data to your models from IoT sources. Paige covers Azure DataBricks, Batch AI, KubeFlow AKS, Stream Analytics, and Event Hub in a session you won't want to miss.