From the course: Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

AI processing - Amazon Web Services (AWS) Tutorial

From the course: Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

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AI processing

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- [Instructor] So why do machines need to think? Well, first and foremost, it makes our life better. We can do things and operate in more effective ways, such as having a ride sharing app figure out where we're going to need a ride. Or the ability for a streaming service to, in essence, provide recommendations as to movies that we're going to like. Or finally, and more importantly, the ability for a hospital to spot a fatal disease early and suggest an effective treatment, ultimately saving a life. So, where did this come from? Well, we've had artificial intelligence around for the last 30 years, it's something that has been a part of computing since shortly after there was computing. The idea of having computing systems that think like people, were able to gain knowledge through experience, and were able to react to that knowledge. Machine learning is really a business instance of that, the ability to deal with transaction-oriented business applications and do so in very sophisticated and thinking ways. So it's a sub-component of machine learning, but ultimately it's more related to the way in which we use learning-based systems in business. And that's going to be the primary focus of this course. Then we have the concept of deep learning, the ability to, in essence, have a very deep understanding of something and the ability to analyze petabytes of information and have insights into that information that typically, humans can't have, we just don't possess the capacity for absorbing and dealing with that much data. And then where do we go from here? I mean, obviously, the jumping-off point for deep learning and machine learning and artificial intelligence is the need to provide business with the ability to do things better and faster. The ability to serve the human race with better compute services that are able to adapt to our needs. And so, we're going to have a lot of technology that's going to spring from this. So keep in mind, no matter what we talk about in this course in terms of machine learning, there is always going to be a basic process. We have data that's inputted into a learning system, and that could be petabytes of information from a structured and unstructured database, it could be information being inputted directly from a user interface, but some way, somehow, patterns of data, or data itself, is getting into the learning system. Then we come out with a conclusion, or in other words, the ability to look at the learning-based system that we have and look at the information that's fed into it, then make conclusions based upon the information. You know, such as the ability to recommend movies from your favorite streaming service, or the ability to recommend discounts from your favorite online shopping place. The ability to, in essence, look at all kinds of anecdotal information, lots of details, and discern conclusions that we can use in a business process, and that's called the output.

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