By now, you’ve likely heard a thing or two about machine learning. But what exactly does that mean? The key question is: what problem is machine learning meant to solve? What does it do well that other branches of artificial intelligence can’t? The answer is simple. Machine learning handles big data much more efficiently than either human brains or other approaches to artificial intelligence.
But that’s what it does. What is it? Roughly speaking, machine learning is teaching a computer to program itself.
In a traditional program, data is input into the program, and the result is some form of output. Learning algorithms instead produce programs from the input. The key is to get the machine to learn by itself without being explicitly programmed.
In other words, we want machines to think more like people do. That means being able to generalize from examples and apply learned experience to a broad range of information.
Why is Machine Learning Important?
Machine learning will revolutionize what we can do and what we thought was possible. Self-driving cars are built on concepts directly derived from learning algorithms that can parse massive quantities of data.
For businesses, there are a number of applications, some of which are being implemented successfully now, and others that are still in development. It’s the ability to process and make use of massive amounts of data that is key to the process. Beyond that, it’s the ability of a computer to make intelligible use of that data, make predictions using that data, and improve the quality of those predictions over time.
This means that much of the work that had to be done by humans in the past can now be streamlined and automated. That frees up human minds to control the results on a much larger scale.
Machine Learning for Businesses
There are a number of ways that machine learning has been adapted to e-commerce. For instance, have you ever noticed Netflix making recommendations based on other shows that you’ve enjoyed in the past? Netflix accomplishes this by using your past selections to make predictions about what your future selections will be.
The same basic method is used by sites like Amazon and eBay to advertise products that you’re likely to want based on prior purchases.
For e-commerce businesses, understanding the basics of big data can help them devise a tactical strategy for targeting customers that are interested in their goods or services. It also means having a good handle on the rudiments of SEO (search engine optimization) and marketing for the digital world.
Advertising and Marketing
Machine learning can also be helpful for businesses that want to mine platforms like Twitter for customer responses. That, in turn, can be useful for creating a profile of the company’s customer base and applying marketing techniques that speak directly to those folks. If a company is looking to branch out, they may be interested in targeting different demographics, and they can do this by applying data science.
In fact, data science has impacted advertising and marketing in ways that we could not have predicted a decade ago and the future of retail will be found in the algorithms and self-learning processes that computer engineers are devising today.
Do you seem like the kind of person that would pay thousands of dollars for hunting gear in Australia? Have you ever been to Australia? Have you ever been hunting? If the answers to these questions are anything other than “yes” across the board, then that will throw up a red flag with today’s financial institutions. The purchases will be flagged as suspicious and you will be contacted immediately.
Using the same basic technology that predicts which products you’re likely to buy and which shows you’re likely to watch your bank can determine whether or not you’ve made a purchase or if your credit card has been stolen.
The Future of Machine Learning for Businesses
As data science evolves, so too do the possibilities for teaching machines to learn by themselves. One of the problems that the modern world presents is that we have so much data to work with and no easy to way to work with it. This is precisely the problem that machine learning is currently trying to solve. How does it do that? By generating useful ways to categorize and visualize the data.
Data science routinely deals with massive amounts of data. This means more data could fit on your hard drive or even 100 of your hard drives combined. Our human brains would be overwhelmed by that quantity of information. But with the aid of computers, we can sift through it and make sense of it much easier.
These algorithms can find boundaries and patterns in data that would take humans years, if not centuries, to discover.
Content analysis is geared toward determining which words, phrases, and techniques translate to sales is a large task that lies before data science. Much of this information can be gleaned statistically, but the amount of information that individuals had to sift through in order to make these determinations was daunting, to say the least.
In the past, machine learning was relegated to determining whether or not a campaign was successful. In the future, it will be able to make predictions based on that data and devise strategies for running successful campaigns.
Incremental Machine Learning
Data science has devised ways to make machine learning more accurate the more data you have. How often is this the case with humans? Humans operate in a Goldilocks zone with just enough information to make an intelligent decision, but not so much that it becomes overwhelming. Machines have no ceiling when it comes to too much information.
We are, however, excellent at adapting ourselves to new information, and this is precisely what data science is all about. Machines can gather and interpret information much faster than we can, but knowing what to do with it is where humans excel. Hence why teaching machines to teach themselves is such a great leap forward for artificial intelligence.
Incremental machine learning utilizing the flow of new data into a predictive model as it becomes available. This means real-time analysis and the ability to re-model as new information without the gathering of data being separated from interpreting of data.
Once this particular nut has been cracked, we’ll be one step closer to bridging the gap between machine and human learning.
Machine Learning Holds the Future
Managing data on this level has evolved to the point where it’s become accessible to laypeople. The better something is understood, the better it can be communicated. If you go back a couple years, it’s difficult to find any two data scientists that can agree on a precise definition of machine learning. That being said, the best way to understand something is by observing what it does.
Whereas cars, trucks, cranes, and planes were an extension of the mechanical powers of our body, machine learning is an extension of the analytical powers of our minds. The problem of too much information has always been a stumbling block for even our greatest minds. With models generated by data science and machine learning, we are on the verge of turning that stumbling block into a cornerstone of future innovation.
Search R-Algo Engineering Big Data’s blog for machine learning articles.