Applying machine learning to business problems
The fundamental idea of machine learning is to take some part of a software system that we used to program explicitly with a set of rules and instead, have the machine learn to do that task.
Machine learning is good, both at automating processes, and also at making processes more efficient. The kind of businesses that profit from the sort of machine learning we have today are businesses where there's some kind of simple informational process that you want to deliver. For example, at Google, we use machine learning across our products and services. We've used it for everything from automatically translating web pages into different languages, to helping users search for their photos, to automatically composing email responses. But there may be places in your business where you're doing something fundamentally similar that's about information. It could be anything from a bank trying to detect fraud in its transactions, or someone running an Etsy store trying to recommend art to its customers.
It's still fundamentally about the process of taking something that's repetitive and kind of frankly boring to humans, and automating it. But it's now operating at a different level. Machine learning can do a lot of things, but it doesn't mean it's the right answer to every problem. If you're an accounting firm, you don't need to use machine learning to discover how to add two numbers. You already have software that does that perfectly.
If you are trying to decide how to use Machine Learning in your business, if you have a data science team, that's the right set of folks to start talking to first. But if you are a small business, you actually probably want to focus on a single exciting idea, an opportunity for how machine learning could make a really big difference in what you offer or how you offer it.
One of the most important things is having examples of the behavior that you want the machine to learn. So in practice, machines learn best from correct examples being demonstrated to them, and then learning to follow suit. So you're looking for an opportunity where you have some task that has been done hundreds or thousands of times, and you have very good records of exactly how it should be done correctly and then you can use machines in order to automate doing that same behavior for millions or billions of times.
Given that machines learn from examples, that they learn from data, you have to have some amount of data to even get started. Machines learn much more slowly than humans do. So where as it might only take having to show a human how to do something ten times, it would probably take a machine a hundred or a thousand times to learn to do that same task.
Until recently, one of the major barriers to entry for companies who wanted to explore machine learning was the availability of good software. TensorFlow is an open-source software package that Google developed internally for our own machine learning systems, and we've released that externally for other companies and academic institutions to use.
The idea is that rather than having to build machine learning from scratch, we provide a sort of erector set of the basic elements of machine learning that you can use to build your own products and services.
Machine learning may be great at automating existing processes, or making existing processes more efficient, but it's not able to discover, or decide what the right next step is. That kind of creativity and leadership is what you, as business people, have to bring to the process.
For every new tool set you have to give yourself the time to explore and to experiment with it. The first time you try it, it's not going to work perfectly and just magically solve all of your problems. The opportunities to use machine learning are unique to every business, and there are many possibilities that we haven't even imagined yet.
Machine learning is a powerful tool for businesses. It can unlock new opportunities to grow, as well as help automate existing processes to make them more efficient and effective. In this video, we'll explore how businesses can get started with machine learning and apply this technology to their needs.