Wondering whether you should invest in AI and Machine Learning? That’s a question that the most innovative companies are considering. Why consider it? One good reason is because your competitors have already started.\u00a0If that doesn’t give you some reason to get motivated, I hope you get\u00a0started before you are put out of business. To make sure that doesn’t happen, there are \u00a0a few things to consider to help you start to explore an investment in machine learning.<\/p>\n
It’s the Data, Stupid<\/strong><\/p>\n Of course, as with any business initiative, you’ll want to create value. And this can be done using machine learning systems. But for those systems to provide value, companies will need to begin by evaluating their organization\u2019s data maturity, but more importantly their readiness to accomplish its data-driven goals. Company’s need to start with an audit of their data warehousing, data scientific research capabilities, data governance and data hygiene. In addition, it’s important to look at the sources, uses, volume, and veracity of all your date, meaning your first-, second-, and third-party data.<\/p>\n Garbage in, Garbage Out<\/strong><\/p>\n Why is making sure your data so clean? Machine learning is basically taking a computer and making it smart enough to learn from the data it’s fed. We are essentially programming machines to\u00a0learn<\/em>. The goal is that\u00a0after a certain point of time, the computer is able to predict further data. How so? Let’s pretend\u00a0you want to make\u00a0your computer predict the weather. So to begin, you might feed the computer weather reports of every hour of every over the\u00a0past year. What you might end up with is– because the temperature (z) depends on day of the year (x) as well as the time of the day (y), more than two-dimensional curve. In fact, weather is random, so the\u00a0equation generated by the computer won’t\u00a0just have 3 variables (x, y, z), it may also have higher powers. So depending on the number of factors in a\u00a0prediction and the randomness of the outcome, the complexity of the curve can increasingly get more complicated.<\/p>\n So back to the data… And I know you know the story about data: garbage in, garbage out. So\u00a0hopefully, now you see can why good, clean data is so important to prediction. As the computer is taking the data you feed it to make future predictions, those predictions dependent on the data you are feeding it. So you want the very best data possible. And it takes super computers which are capable of handling large\u00a0volumes of data, as well as the ability to learn fast\u00a0and to make\u00a0fast decisions based on the learning it under goes.<\/p>\n AI and ML Are Not The Same<\/strong><\/p>\n Often times\u00a0Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably. But they are actually different<\/a>. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider \u201csmart.” Machine Learning is the application of AI based on\u00a0the idea that we should be able to give machines access to data and let them learn for themselves.\u00a0Artificial Intelligence devices (devices designed to act intelligently), are often classified into one of two groups: 1) applied and 2) general.<\/p>\n Applied AI is far more common. Applied AI is about\u00a0systems designed to intelligently trade stocks and shares or drive\u00a0an autonomous vehicle.\u00a0Generalized AI is may up of systems or devices that, in theory,\u00a0can handle any task. And are less common. However, this is where some of the most exciting advancements are happening today.<\/p>\n Deep Learning is A New Area of Machine Learning Research<\/strong><\/p>\n It was introduced with the objective of moving Machine Learning closer to one of its original goals: that of being Artificial Intelligence. So essentially Deep Learning is a subfield of machine learning\u00a0<\/b>concerned with the algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning has worked it’s way into business language via Artificial Intelligence (AI), Big Data and analytics. Deep learning\u00a0is an approach to AI which shows great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries.<\/p>\n The Two Big Ideas: It May Be Possible To Teach Computers to Learn and The Internet is a Source of a Ton of Data<\/strong><\/p>\n Arthur Samuel, in 1959<\/a>\u00a0is credited as the one who came up with the big idea that it might be possible to teach computers to learn for themselves. That would be in contrast to teaching computers everything they need to know about the world and how to carry out tasks. The second big idea\u00a0was that the Internet, with huge increase in the amount of digital information being generated, stored and could be used\u00a0for analysis. So the scientists and engineers realized it would be far more efficient to code computers to think like human beings, and then plug them into the internet to give them access to all of the information in the world.<\/p>\n