Welcome to the session on applying A I with zero machine learning expertise. My name is Li Thomas. I'm a manager with the LAUM Reply. I'm also an MVP in A I. It's a Microsoft most valuable professional in the A I space.It been w my team and I have been working in this space for quite a few years and I'm happy to share my uh expertise and experience in this space with you. So in the interest of time, let's dive right in. So we are at a stage in uh in history where the opportunity with A I is now real. It's no longer a hype and industries and business organizations across the world have started to realize that at length um and in quantifiable terms. So apparently there is a lot of research and studies by the one that you see on screen here is one by Forester, which is, which quantifies the benefits that uh A I will bring into every kind of application in terms of increased automation, business agility, revenue, customer experience, et cetera.

Now, the question is if all this is true, what is the friction or what are the challenges that organizations face with infusing A I into applications at a very high level. More than 50% of leaders, tech decision makers believe that these are the major challenges that they are dealing with. One on the data side. They don't have the adequate data, the quality of data to, you know, build A I solutions a development skills. It's one of the hardest um you know, skill sets that they require and they feel that their teams are not ready, their resources are not adequate data, science, skills and machine learning expertise as we call it, you know, teams would require to have advanced machine learning expertise and knowledge of statistical models and mathematics to be able to build algorithms ground up.

So with three primary challenges facing them, while they're building A I solutions, several leaders in this ecosystem have come up with what is now called as cloud A I services, uh cloud A I services. The promises of cloud A I services is to provide prebuilt A I services or models that any application developer or any, you know, everyday developers will be able to invoke using simple API calls just like if you are familiar with calling arrest API or you are familiar with working in a programming language of your choice.

C# Java Python, you will be able to invoke A I services into your applications starting today. So though they are prebuilt, they are configurable and you have ongoing support from their partners, you can deploy it on the cloud, you can deploy it on the edge, you know, they're very responsibly built and they tested and these are services that you can actually trust. So you have a lot of benefits from these cloud A I services adoption as you see on screen. So it's quick to to build A I solutions. You faster to market faster to deploy, you've got better troubleshooting expertise and you don't need to have machine learning expertise or data science skills to get started with or to use these services. So Microsoft's offering in this space before we go into that, I'm happy to tell you a little back history of story around this. So even before A I was a thing and started gaining all distractions decades before Microsoft started investing in this space. And we have uh so that's basically based out of the Microsoft research centers across the globe and research advanced research that was done in these research centers uh came up with these A I services that today have even achieved human parity.

What that means is uh say for example, we talk about image captioning, image captioning services that Microsoft uh is has built. The Microsoft research teams have built can caption an image as good as a human can and these services, what they did with them initially is that they infused it into their own applications. So if you're used to using any of the Microsoft applications on a daily basis, like Word or powerpoint or Excel or themes or whatnot. I mean, even Microsoft Edge for that matter, the image captioning feature that we're talking about right now is a part of Microsoft Edge. So not everyone adds alternative text to their images. But what Microsoft edge does is it automatically captions uh a text even if the author has not added an alternative text. So in terms of accessibility, if someone's using a narrator to have that experience with uh browsing, you know, you don't have to rely on an author, the browser that themselves because of this image captioning feature is able to automatically caption an image for you. So long story short, what they did with that after that is that they decided that we will, they will open it up for the developer community, the very A I services that they use inside these products, they will democratize it, they will open it up so that you and I will be able to use those services and build A I applications.

So before we go into that, I want to give you a very high level understanding of the landscape here. When we talk about A I services in Microsoft or in Azure specifically, there are various flavors. One is this pre built A I service is the one that we're talking about in this session. We also have other flavors which includes conversational A I and custom A I. So yes, you can build this using the machine learning expertise from scratch. But if you don't want to and you want to get started today, you have the option of choosing Azure Cognitive Services. So these are the prebuilt A I services that we're talking about and that in a few lines of code, we're able to, you know, you'll be able to call, call uh invoke API services and you're able to interact with uh applications just the way that a human would. Because what gives us our intelligence is our ability to see, hear, speak, understand and make intelligent decisions. Now, imagine giving your apps your applications that same ability. So this is the promise of Azure cognitive services. And this is a huge uh you know, a lot of the breadth of services and capabilities across each of this is very interesting and it's very, very powerful.

But again, for the sake of time, we might just look at a few of this and I'd love to talk you through, you know, some of the capabilities on the language services. Now with language, what we would typically do is, you know, it, the ability to understand a human language or to the ability to have an intelligent conversation with a human is, is a very powerful one. So imagine being able to talk to a machine or an application just the way that you and I talk, right? So the the technology would be should be able to understand what I'm trying to ask or what I'm trying to say, should be able to go and find an appropriate response. After that. Maybe you should be able to understand my sentiments or emotions, able to understand key phrases, entity, recognize entities in that translate my text if I'm talking in my native language. And uh you know, the first the technology can help translate that across hundreds of languages and and provide me an accurate response. So these are the the several capabilities of the language service and we look at some of them in detail.

So let's say that we are trying to create a bot for the Women Tech conference, right? So one of the things that I could ask the bot is when is women tech uh 2022 happening and it would say June 7 to 10, great. So the catch with that is that each of us would ask that in different ways. And that's the beauty of the human language, right? So we do have a lot of nuances uh with the way that we, we talk and and a conversational agent of a chat board, for example, should be, is an application should be able to understand that irrespective of. However, you ask that the answer should be uh June 7th to 10th. So this is possible using the Azure cognitive Service for language. Uh take it a step further. Sometimes it's important that you understand the intent of the meaning of what a piece of text is. So that's where conversational language understanding comes. So if you're familiar with natural language processing or N LP or NL systems, you don't have to build them from scratch. Now, you should be able to use these A I services which will be able to give you the meaning of a piece of text in a single API called uh for example, uh in the same chat, for example, you know, someone could ask, when is the session on A I what we're doing right now?

Uh Is there a session on leadership? Is there a se security on inclusion on imposter syndrome? We understand that all of these are very different questions but how uh an app would deal with it is all of these irrespective of the different utterances that we've mentioned. You might note that every single speaker has the same intent here. The intention of the speaker is to find a session in the conference, right? The difference of the variable there is the session name which I call an entity, right? So these kinds of um of nuances and things can actually be done using um a your cognitive service for language. Um Another thing that they could do is sentiment analysis and opinion mining. Now, this is interesting because these are actually two different things. So sentiment analysis is what we call um fine brain. Sentiment analysis. It applies labels to a piece of text, it could even be an entire document. In fact, it could be a sentence, a paragraph or an entire document. So if it's fed a line of text, it would come back with a single score between 0 to 1. The more it is towards zero, it is indicative of a negative sentiment, the more it is towards one, it is indicative of a positive sentiment somewhere in between is a neutral sentiment.

So uh let's say the um you know, you a customer who's who's just back from AAA hotel vacation and that you know passes this customer review says I love the hotel location and the customer service was excellent. So what this api would do is it would give you uh it would return a value of one which is indicative of a positive sentiment. Now, what's tricky is, let's say the customer has entered something like this, which is most uh obviously in a practical scenario, this is what you would you would get a whole paragraph like you would see in Amazon reviews with uh mixed you know, sentiments of I love the hotel location, disappointed with the room service.

Now, if that is the case, you typically people and even applications are left wondering if that was a positive sentiment or a negative sentiment. So this is where you need to have, you know, better granularity and better control over the sentiment analysis here. So the hotel location was obviously a positive sentiment, but there was a negative attribution to the room service. Now, the promise of opinion mining, which is a feature of sentiment analysis itself is that it goes one depth, one level deep. So it is able to give you this kind of aspect based sentiment analysis, it will return value saying that you know what this part, the whole part was, the whole sentence itself was a mixed review. But then this part was positive and this part was negative. It's important for um you know, people behind that application to know such important things because then you know what to what your strengths are, you know, where your opportunities are as well. So it's, it's very powerful to have those kinds of things and products and services.

So if we have time, I'd love to show you a quick demo of some of these things in, in action. Um Obviously, you know, there's, there's a little bit of technology that is involved here. But if you'd like, I would suggest that um all that you would need to even try this out right now is an Azure subscription. You can even, you know, sign up for a trial Azure subscription and you get some free credits along with that. And then what we need to do is go to this uh URL which is language.cognitive.azure.com. So it's basically you just need to browse for language studio. Language studio is a new offering wherein without writing a single piece of code you can check out these services in action, all of these that we discussed right now and possibly a few more as well. So there's a lot of services in here that you can, you can play around with. You can understand the capabilities. Um We will try try and look at a few of them like let's try sentiment analysis, right? So, um at this point in time, like I said, I'm not writing any piece of code. I'm just trying to demo and show you the capabilities of this service.

So if you're interested, you can go on and read technical documentation, there's samples on github, there's additional SDK links in that you can use a programming language of your preference as well. You can use C# Java Python, you know they're all supported. So here, what I would do is I would select my text language. This is the resource that I have created in Azure. So there is AAA billing that is associated with this and therefore I have just created my free time, but I will just select that Azure resource. So it runs against the service. But this is how this is where you know the action happens. Now, we can choose from any of these samples, you can drag and drop your piece of text or your document if you want to try it out on your um on your custom piece of text as well. Um I will just go with one of these. And let's see how this behaves. So I will just run this sample and you'll see that uh immediately it has identified in that piece of text, you know what the target is. Uh And so I did want to show you that I have enabled opinion mining here. Now, that is an optional thing. If you would like to have that level of granularity, you may enable opinion mining again, not mandatory. So because I have enabled opinion mining as you can see what I get out of the box is I get very granular information.

So on a whole, it says that my document sentiment is mixed. I have already 85% positive and 15% negative comments. And that so this is what I would get if I would go with plain sentiment analysis alone. But because I have chosen uh opinion mining, see what I get, I get uh uh about the uh uh an opinion about the spot. And I also get an opinion about the um contours of food place. So this is this is again see if you see the entire text. So this is the short answer. This is just the first sentiment. But if you want to look at the entire um you know, piece of text that we provided there, you'll see that there's a lot of assessment that has happened with each and every target. It is coming back and giving you AAA label, which is between 0 to 100% 0 to 1. So it, it is very clearly indicating, you know, what was, what was working for you, what was not working uh so on and so forth. So you can use this now how you use this depends on your specific application on your specific use case. Uh You can be very creative with this and uh uh you know, use that in your applications to the to, to suit your business needs.

So we have a couple of minutes left. But yeah, I wanted to wrap it up by saying that, you know, technology aside, any system that we build, especially an A I system contains not just the technology. So there's people, there's an environment, there's people who are using it, people who have created it, there's a whole system in place and it's therefore important that we build A I solutions very responsibly. Um So there are A I responsible A I principles that um all tech giants um you know, evangelized and the one that particularly from Microsoft is they do a good job at uh you know, evangelizing about building fair, reliable, safe, uh private, secure, um inclusive and accountable applications, especially when you're in the hot seat building A I applications.

So um a couple of preferences there uh especially for the Forest study and the Transparency Note, et cetera. So um feel free to reach out if you have any other questions or uh would like to learn more about uh uh the capabilities of um A I on the Microsoft side or the Azure side. Um Happy to continue the conversations and um I will see if we have any quick questions, if we have a minute to take them. Uh But thank you for your time and uh that's all I've got. Yes, Harita. I plan to share the presentation with reference links uh in my uh linkedin. Um So I just need some time to, you know, to polish that and to make it accessible. So, if I may put my linkedin uh uh is here uh so that um uh you can connect and be happy to. Uh All right, what do you consider to be the most used swift in the industry to work in A I? Um I'm sorry, Jess, I don't know if I understood that. Right. Uh Well, the most used swift in the industry to work in A I if you'd like to clarify that little, sorry. Uh Thank you, Laurie. I'm glad it worked out well for you. You're welcome Usha.

Thank you all for joining and I really appreciate you taking time and your patience and your attention this morning. All right, I think I'm up for time but uh hope you have a good rest of the conference and uh take care everyone. Thank you.