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Machine learning is everywhere today, informing our day to day lives from the way we navigate google maps all the way down to the way we check our email. But what is machine Learning exactly? When did it start to become such a big deal? Here’s a quick synopsis:
It all started back in 1959 when an MIT Engineer, Arthur Samuel described machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.” Back then, Samuel was busy creating a machine that was an autonomous checker program. It was designed to someday beat the world's top checker champion.
Another important development with machine learning was the internet. The launch of the internet presented copious amounts of accumulated data which is now called Big Data. With so much information readily available, there seemed to be one thing left to do: figure out a way to organize it into meaningful patterns.
Big Data is essentially just that: a ton of data. It's all the information accrued by social media companies, search engines and even more microphones and cameras that are constantly collecting information.
Next, vast amounts of data inform the machine learning algorithms, equipping technology with methods of predicting future patterns. These algorithms will then provide a way to predict behavior and anticipate any problems before they arise. One of the best known examples of this is Amazon’s ;suggested’ feature. It reads your preferences and the buying habits of others and then recommends other products you might be interested in.
Machine learning differs from human learning in the sense that the machine only knows what you tell it. The machine has no curiosity of inference making abilities. For example, if you watch lots of Comedy shows on Netflix, you might find Tom Segura’s new comedy special in your suggestions queue. But it's only a matter of math based on Netflix’s data alone. If you had told Netflix that you love Mysteries or rated one highly, the system will likely never show you one to watch next.
Machine Learning is all around us. It informs everything from our facebook feed, our suggested traffic routes in Waze, our auto email spam filters and even the security of our banking information. Through machine learning, technologists have mimicked the way the human brain works by producing sophisticated systems called neural networks. In turn, neural networks can enable deep learning which is an outcome that produced computer systems that supersede human intelligence.
Machine learning and artificial intelligence are oftentimes used in the same sentence, but they're not the same thing at all. Artificial intelligence refers to a machine's ability to perform intelligent tasks, whereas machine learning refers to the automated process by which machines sort out meaningful patterns in data. Without machine learning, artificial intelligence as we know it would not be possible.
In many ways, machine learning is one of the most important powerful forces in technology. Its development is shaping the forefront of the future. Today NineTwoThree Digital ventures has used machine learning to help companies make sense of their data and help them make real time decisions that benefit the business on the fly. Everything from staying vigilant from maintenance issues and fish counting & identification, to counting trucks and how much waste is removed from a construction site; NineTwoThree can help you unlock the intelligence into your own data that you never knew you had.
