How many times have you tried to figure out what you are having for dinner only to become disgruntled and order a pizza? It happens to all of us from time to time, but that's often because we run out of time to prepare for dinner or, frankly, we forget what's in the fridge.
But what if we didn't have to remember what's in the fridge or even what type of meal to cook. What if our food could tell us what to make?
We often hear about Azure and how it is Microsoft's new show pony but what is it? In the simplest terms, it's a massive computer network that is made up of many smaller computers that are all linked together and have more storage than you can possibly imagine.
The cool thing here is that getting access to this massive system is quite easy and cheap, and you can use it on-demand too. The power of the Azure platform is immense and is magnitudes more powerful than the device you are using to read this post. Microsoft also makes it clear that they have the largest cloud infrastructure in the world, topping both Google and Amazon.
At Microsoft's Worldwide Partner Conference earlier this month in Washington, D.C, we had a chance to talk with some of the company's strategic partners who are using the cloud to work on big data problems, from medical diagnoses to predictive web analytics to measure changes in SEO performance based on keyword modifications.
But why now? Why is Azure and the cloud coming into fruition this year as opposed to last year, or last decade?
Machine learning will know what's for dinner, before you do
Microsoft has been working on Azure for many years and Satya Nadella, who was recently promoted to CEO, was a key figure in the development of the cloud platform, and is likely one of the key reasons he was chosen to lead Microsoft. His appointment and his vision for a cloud-first future are about taking what was built in the recent past, and pushing it to be fully utilized by the masses, thanks to it being easy to access by corporations and being supported by a wide set of developers too. But beyond that, Microsoft is making huge strides in making machine learning accessible to everyone, not only those with huge checkbooks.
That's all great, but how is this helping me decide what to have for dinner? A few years ago, machine learning was a nebulous concept where those with a Ph.D in statistics and mathematics would sit around and try to create fancy algorithms that could be used for predictive purposes. Like many aspects of technology, at first, this process was expensive and complex.
Nadella was fundamental to the creation of Azure
But, as time went on, computing power increased and machine learning has been democratized, and is quickly becoming a commodity that anyone can buy. In fact, Microsoft is already headed down this path and has announced initiatives for Azure that will soon let you buy machine-learning algorithms from a marketplace.
Imagine having a data set, going to a marketplace and picking out a machine-learning algorithm, applying it to your database and reaping the rewards of decades of mathematical research without having to leave your desk. It's happening today and it will soon help you choose your next meal.
Big data will deliver better experiences with less work
If you don't quite understand how machine-learning will change the world and more importantly, your habits, think about what you are having for dinner tonight.
In the not so distant future, Cortana could help you make your dinner. Imagine that your shopping habits are monitored in your own personal environment and based on your itemized receipts; the cloud knows what food you bought at the grocery store.
Based on the average shelf life of a food item that can be pulled in from external sources, three days later you are trying to figure out what to eat. The cloud knows that you bought charcoal last week too and a quick check of the weather says it is sunny; it's time to grill out.
Based on your purchases, Cortana could use machine learning to put together an idea for a delicious meal for you, complete with instructions on how to cook it, how many people it will feed, how long to cook it and based on your purchasing and dining habits, how to spice it perfectly for you. She could even recommend the perfect wine to go along with it.
Independently, knowing you bought steaks and that it is sunny outside offers no value. But when you combine that data intelligently, you can create experiences using machine-learning and big data that will change your habits. Instead of ordering a pizza, Cortana could tell you all the possible meal combinations you could eat tonight. Imagine if you say "Cortana, I have 20 minutes to cook, what can I make tonight?" These are the types of intelligent answers that machine-learning can provide us.
This is one very simple example but there is unlimited use of this type of data interrogation across a wide array of applications. On the London Underground, there was an instance in which machine-learning was used to better understand - based on the vibration of wheels on escalators - when they would fail. Knowing a wheel was about to fail, preventative maintenance could be done ahead of time, at a time when the tube is less busy
Machine-learning is best used when it removes computing from your workflow. By knowing what you can have for dinner, you don't have to go searching the web for recipes; the information will already be available for you in an easy-to-use format. This reduces your time in front of the computer and lets you get back to doing something besides research because the big data already answered your questions before you asked them.
So, will machine-learning change our habits? Yes. Is this some far-off and distant dream? Hardly. Microsoft and its partners are bringing machine-learning and big data processing down from the macro process to the micro decision. The funny thing about this entire process is you wont instantly notice it; it will just gradually happen. But one day, you'll look back and realize how much less time you spend searching and asking, and how much more time you spend doing and creating.
This is just the beginning.