Examples of existing AI (machine learning)
Practical Machine Learning
The course is part of these learning paths
Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. This course is part one of the module on machine learning. It starts with the basics, introducing you to AI and its history. We’ll discuss the ethics of it, and talk about examples of currently existing AI. We’ll cover data, statistics and variables, before moving onto notation and supervised learning.
Part two of this two-part series can be found here, and covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.
If you have any feedback relating to this course, please contact us at email@example.com.
Let's now consider some examples of really existing AI, which I'm gonna call machine learning. So machine learning is the core technique of how we are today deploying AI systems. And I want to start here with a side definition and then some examples. So to define this, what I'm going to say is it's the use of statistics to tune or specialize algorithms. To tune, train and another word here, or specialize algorithms to problems. Let's go through some of the mains application. Some applications and examples. Now, I think we should start at the most sophisticated form of Artificial Intelligence, in existence today. And what do I mean by that? I mean, consider having an almost infinite amount of money, and almost infinite amount of data, and almost an infinite amount of expertise. With that, what is the best possible system you could build? And here's my example, Alexa. But what is this system? What is this system? The system is one of processing natural language. This is a natural language processing system. Which tries to take audio signals, obtained with a microphone and looking at only the pattern of the variation, the volume of those signals over time. Correspond that to actions really, so you're taking this volume and time and you have all of these adventuristing little variations in volume. And you say well perhaps this means turn the light off. And really that is really bout it. Let's just say what that isn't. That isn't understanding natural language, in other words, if I say to you, "Find me the light switch in the room?" You are able to navigate and try out various things and perhaps the light switch has a odd shape and it's in an odd place but you can keep going. You know what you're looking for cause you have all of this prior knowledge that you've gained from being alive for a very long time and you know what kind of thing would meet that knowledge it would turn lights on and off and you can keep going until you find it. Now this machine had none of that background knowledge, has only the audio signal and the audio signals of other people speaking of course. And then there is some systems which correspond pieces of signal to actions the machine can take. So it's a very limited system, but it is really and genuinely the best we have in the world. Some other kind of examples include those around prediction and detection. If you can think of something to predict from something you know you may be able to use a machine learning system to solve that problem. And we can use lots of informationals, demographic information and other kinds to engage in marketing campaigns. To target ads to you based on what we have seen has been successful previously. So the applications across finance, politics for electoral campaigns, medicine, retail, marketing and so on.
Michael began programming as a young child, and after freelancing as a teenager, he joined and ran a web start-up during university. Around studying physics and after graduating, he worked as an IT contractor: first in telecoms in 2011 on a cloud digital transformation project; then variously as an interim CTO, Technical Project Manager, Technical Architect and Developer for agile start-ups and multinationals.
His academic work on Machine Learning and Quantum Computation furthered an interest he now pursues as QA's Principal Technologist for Machine Learning. Joining QA in 2015, he authors and teaches programmes on computer science, mathematics and artificial intelligence; and co-owns the data science curriculum at QA.