The first example and one of my favorites is how AI is used in search engines. Search engines such as Google and Bing have been using machine learning for over a decade and they're able to combine all kinds of intelligent algorithms to be able to perform tasks such as reading the content on a page recognizing which pages do best historically versus which ones don't and then dynamically changing which results are shown higher than others based on that performance.

Another great example is stock market high-frequency bidding systems. These types of financial technologies are moving so fast because they deal with literally billions upon billions of dollars every single hour. We as humans and the brokers on Wall Street cannot make decisions anywhere near as fast as an intelligent algorithm.

So these algorithms are analyzing all of the historical data when it comes to stock purchases and currency rates they take in all of these different parameters and they go and they buy stocks based on all of these different weights and flags they placed in the system.

Another great example of a in the real world our recommendation engines. Amazon was one of the earliest proponents to integrating AI into their e-commerce platform. They built in the ability to track purchases on a historical basis and then they had that purchase history dictate which items would be recommended to other customers and they were able to generate much higher sales because they were able to look to see what they think someone might want to purchase based on their own historical data.

The next example is one of my favorite case studies for AI and that is the self-driving car. The reason why this is one of my favorites is because self-driving cars have to combine so many different technologies and algorithms and they have to execute on them perfectly because cars can literally save or kill lives depending on how well they perform. This is one of the most critical types of AI that is being worked on right now.

Next on the list are home automation systems. Now, these have become incredibly popular over the last few years and one of my favorite examples of this is the nest product. Nest is a smart learning thermostat and it leverages machine learning in order to make your home more energy efficient.

I personally have it in my house and it has helped save quite a bit of money over the long haul because the nest system looks at how I live it looks to see when I'm in the home. It looks for the temperature ranges when it's hot when it's cold and then it dynamically adjusts the temperature based on all of those parameters and so it is a great example of how machine learning can take some historical input apply some intelligent design to that system and then give nice output that helps make our lives better.

Extending into the world of home automation. Our next example is also a device that's become very popular in many homes. I have a few of them in my own which is the natural language processing systems. These are the Amazon Alexas and Google home type devices where these systems by leveraging natural language processing make it possible for you to communicate directly to that device. Have that device interpret what you're saying and then execute commands based on what you asked it to do. And these have become very good. I am shocked at how well my Alexa devices are able to hear me, understand my commands, and then do pretty much exactly what I asked them to do.

In the last example, we're going to discuss our medical diagnosis tools. Now these are very important and they are being worked on from thousands upon thousands of machine learning developers all over the world and it's because of these types of algorithms and these implementations can literally change and save lives. They give doctors the ability to instead of the doctor having to manually go through thousands upon millions of historical records which just is not possible for a single human. These systems are able to do that very quickly.

They can go and scan a database of millions of patients and be able to generate dynamic diagnosis based on all of the parameters that the doctor is going to enter in so the doctor can take in their medical history and any kinds of family issues or any diseases anything like that. And then these systems can go cross-reference that one patient across millions of other patients and give a much better set of recommendations than a doctor may be able to do by themselves.

Those seven examples are only a small percent of all of the different implementations in case studies that people are working on right now in the machine learning community. But hopefully by going through each one of those that helps give you a frame of reference for what you can build using artificial intelligence machine learning and data science.