Chirag Dayani, Senior Product Manager at Microsoft, gave this presentation at the Product-Led Summit.

My name is Chirag Dayani, and I'm a Senior Product Manager at Microsoft. I work in AI and ML innovation in the identity and access management space, which is part of the Microsoft Security division.

I’ve worked for over 9+ years. I started my career as a software engineer, then transitioned to consulting, and then finally into product management. And I've worked with companies like Deloitte, Accenture, ServiceNow, and now Microsoft.

Today, we'll be talking about how we can master AI and ML in product-led growth. And we've been hearing more about data, metrics, and how we can trust the data. So we'll be covering more of these topics today.

AI is everywhere

Every day, day in and day out, we're using AI in some way or another. The AI could be from a recommendation source on Netflix, Spotify, or YouTube, or any voice assistant platform like Google Home or Amazon Alexa. Or if you're ordering something from Amazon, your order’s being fulfilled by Amazon's robots, which are actually using image recognition to get your order fulfilled.

In some situations, you might not even know that you're using AI, like when you’re doing a transaction with your bank and you get a notification that says, ‘Hey, did you just make this transaction?’ That's where AI’s doing the work on the back end using a fraud detection algorithm.

And how can we forget about chatbots? We all talk to customer support about issues with our bank accounts or our e-commerce orders. We use chatbots day in and day out, which are also based on a supervised machine learning algorithm on the back end.

One of my favorite examples is self-driving cars. We’re using AI very heavily by incorporating cameras and computer vision to come up with the areas where you can drive the car with less pain, and then go to that space where you can choose a fully self-driving car option.

AI vs. machine learning vs. deep learning

I’ll now talk about the definitions of AI and ML so that we know exactly what these mean.

AI is the ability to mimic human intelligence. And we're leveraging AI to use the previous amount of data that we have to also improvise how we can predict the future.

Machine learning and deep learning are subsets of AI. Machine learning is explicitly about how we can apply AI and create some kind of mechanism to automatically learn and improve on our experiences. Deep learning is a way we can apply the machine learning concept and convert that into any kind of training model or use those complex algorithms.

The application of AI in self-driving cars

Now, I’ll provide an example of an application of AI in self-driving cars. I’ve chosen an example of Mitsubishi Electric Car Labs; they were trying to accomplish how we can explain the applicability of AI in self-driving cars.

They’re trying to create a system where you not only get the navigation of any car, but you also understand how data can be used to give you an instruction, almost as if a co-passenger is sitting next to you.

If you look at the first image below, we’re tracing a lot of data by looking at the image and using some image recognition. There's a car, there's a tree, there's a traffic signal which is approaching, there's a building, and there are cars that are at the intersection, so it detects that this is an intersection that’s approaching.

If you look at the second image below, it tells you which direction the car’s going in. We're using some physics concepts, like using vectors, to understand the speed and the velocity at which the car is moving in front of you, so you can get that guided experience. For example, ‘Take a left turn behind a black car,’ or, ‘Take a left turn where there’s a billboard on a building which is on the left side.’

It may also caution you. For example, ‘While taking a left turn, watch out for the incoming bus on the other side of the road.’

In the third image below, you can see how we’re labeling the data, and how we’re applying everything we’re collecting. We’re giving that a label in the form of how we can detect that object.

It detects that there's a person walking, and there’s a bike which is coming on the other side. It can even give specific details about objects, for example, that it's a blue bike. So you can go very deep into it by looking at the image and trying to understand what that means. And this is more about how you can train your model using a large corpus of data so that this machine learning can give the right outputs.

Generative AI

Gartner defined AI as having four main categories and defined a hype cycle for AI. They define the four categories as data-centric AI, model-centric AI, application-centric AI, and human-centric AI.

In this cycle, they said that we’re at the inflection point of the AI phase, where we’re at the peak of inflated expectations. And in that, we’re now looking at generative AI and responsible AI as the most up-to-date and peak hype cycle categories.

Generative AI has become very popular, with OpenAI recently releasing ChatGPT. It processes huge amounts of data, which now is also being used on the back end. They're now using GPT-4, which was recently launched this year.

They're using the capabilities of large language models, which is at the next level of how and where you can use a chatbot. With chatbots, you're using supervised machine learning, but you're training a corpus of data to provide some labels and get some curated responses on top of it.

Whereas in large language models, you're actually giving contextual summaries and responses and using GPT-4 to convert your text into images and more text.

So it's using huge amounts of data, and that data processing is happening on the back end. But it's generating a prediction of what you're going to ask and using that information to look at how other users may have asked that question or how the entire internet space is gathering that information.