Hello, everyone welcome to the second webinar of the LearnAI series my name is Rodrigo Souza I am a data scientist with the Microsoft research and AI team. We have a special guest today Liam do you want to introduce yourself? Yeah, thanks Rodrigo. Hi Everyone, My name is Liam Cavanagh and I am a program manager i work on the content search and intelligence team which is part of the cognitive search that Rodrigo is going to be talking about today let’s go today we’ll talk about cognitive search and how it was use it with the jfk files project we start with the microsoft vision and platform for ai then we have cognitive search constants and related technologies and for the jfk project you see architecture demo in the call At the end we have ten minutes for a q and a session is small disclaimer before we go any further this is not the session about the related technologies work especially how they work together. This is a microsoft vision for AI. we can browse and apply end-to-end solutions from the Azure AI gallery You can build your own solutions using the Azure platform. You can interact with intelligence agents like Cortana or bots. And, can use the better AI intro applications like office365 or even windows itself? And, this is the ai platform infrastructure services and tools. Can use any operational system and employ your models on frames in the cloud or even to IoT device for the jfk project we use it Pre-built AI AI ON DATA AI Computes the similarities in a few minutes? After some research i chose these definitions so we can better understand what cognitive search is. Artificial intelligence computing which simulate human-human perception. Cognitive form from a to perspective process often intellectual activity search from a web perspective experience of a natural low friction way to interact with applications and data finally the definition for cognitive search extract relevant information from big and diverse data sets in users context. Perception drives to context and we can say cognitive search is cognitive skills with a search engine. If I’m going too fast please let me know. And, what are the microsoft related technologies for cognitive search. first one we see is cognitive services the value of ai isnt about fancy algorithms but how to make it easy to use. As you can see we have api to simulate human cognitive skills. This service are prebuilt in preach changing deep learning models polished on Azure to accelerate your AI project. They have free cheers. can be used with just a few lines of code and work across platforms like ios andriod and windows. But, the jfk project we use a computer vision and entity linking api. In the cognitive service portal we have a directory for apis grouped by category. in the quality of service for row if you use a few clicks you can see pricing and documentation of all the fbi you need. Example the computer vision api use in the jfk project is free up to five thousand transactions per month is works we’ve curl or any other language capable to call a recipe api also you can download all documentation pdf format It also has demos allow you to see expected jason results for each fbi sample major is provided but you can upload your own you do these in a few minutes. with computer vision api. In the jfk project you read text from images in pdf files if the entity linking api you get the wikipedia id of the terms and expressions extracted from the computer vision processing I just image so the two technologies work together these two api work together one after the other. Azure search the second microsoft technology use it for cognitive search energy in the JFK project. It’s a cloud search path service for web and mobile applications. It’s easy to scale up and down and has ninety nine-point nine per cent SLA. It creates an index if metadata about your data it’s not data integration it’s not data replication its an index with information about your data? Provides natural language processing and have special features for same structure or unstructured data? Which features are these, spelling mistakes? Geo spatial data and not only plotting but also distance intersections so on suggestions ranking paging Highlighting and facets. However, working with relational database you can imagine how complicated it is to do all of these we’ve sequel lots of IFs. Like percent queries very bad for performance. Sub queries. Cursors Secondary indexes so it’s pretty hard to index unstructured data we have sequel work with structured data that’s where Azure search helps the most and how it works? It has corey profiles so can define ranking in relevance for each term this process has a default algorithm but you can create your own. Azure search provides process. Processing capabilities for multiple language removing stop wards or provide a concept understanding of the data you can use Lucien microsoft algorithms for this possible data sources are Azure sequel end string column. CosmosDb the files on Blob storage include including office files pdf csv xml and so on and also Azure tables are key values storage on Azure.