Join us on November 2, 2022, for the Global AI Developer Days


 The Global AI Developer Days 2022 is for AI enthusiasts and practitioners, of all skill levels to explore the art of the possible with AI. If you are a citizen or seasoned developer, find out what you can do with AI today.

 You can join us using the two-stream links below. Keynote &amp; Stream 1 -&nbsp;<a href="https://www.youtube.com/watch?v=ZkS2Qc1IfKc">https://www.youtube.com/watch?...</a> Stream 2 -&nbsp;<a href="https://www.youtube.com/watch?v=4i5ycdzqAlc">https://www.youtube.com/watch?...</a>

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 Our Keynote Speakers:
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Global AI Developers Days - Tri-state

Stream 1

Millions of users uses Microsoft 365 apps each day to keep track of their work, business, and data. In this session we will explore how we can improve the overall experience of our users by adding intelligence to them using Azure AI capabilities. For example, analyse the images as they get uploaded from Teams or SharePoint. Carry out the sentiment analysis from Yammer conversations to ensure employee well-being.

The session will focus on:
- Overview of Microsoft Azure Cognitive services
- Computer Vision API
- Custom vision service
- Sentiment analysis API

What will you learn from session?
- Implementing AI using cognitive and sentiment analysis.
- Make your M365 apps intelligent

Add intelligence to your M365 Apps with Azure AI

X (Twitter)

LinkedIn

Twitter

Language Understanding Intelligence Service (LUIS) is part of Azure's Cognitive Services.  It's built on the interactive machine learning and language understanding research from Microsoft Research.  Luis provides the capability to understand a person’s natural language and respond with actions specified by application code.  In this session we'll examine how this powerful feature can be integrated into applications, offering a more natural interaction with a device.

Making Apps Listen and React with LUIS (Language Understanding Intelligent Service)

When we talk about AI, we usually mean a machine learning model that is used within a system to automate something. For example, a self-driving car can take images from sensors. A ML model can use these images to make predictions (example: the object in front of us is a tree). The car uses these predictions to make decisions (example: turn left to avoid the tree). We refer to this entire system as Artificial Intelligence.

This is just an example. AI can be used for anything, from insurance underwriting to cancer detection. The defining characteristic is that there is no limited human involvement in the decisions the system makes. This can create many potential problems and companies need to define a clear approach to the use of AI. Responsible AI is a governance framework intended to do exactly that.

Responsible AI can include details about what data can be collected and used, and how the models should be implemented, evaluated and monitored. You can also define who is responsible for the negative results of the AI implementation. You can define specific approaches and others more open to interpretation. They all seek to achieve the same thing: to create AI systems that are interpretable, fair, secure, and respectful of user privacy.

The objective of this session is to talk about the responsible use of Artificial Intelligence in the generation of fair, equitable and explainable machine learning models.