How Machine Learning Is Changing How We Browse the Internet
Everything we do on the internet is being tracked, and machine learning has a lot to do with it.
Machine learning is a branch of artificial intelligence (AI) that has been evolving for some time now. The concept that distinguishes machine learning from other branches of AI is the concept of building open-ended variables in the technology that allows the AI devices to recognize patterns and learn from those patterns by using core principles to develop and apply data into intelligent and useful functions.
How new is machine learning?
Machine learning is nothing new. In fact, in any automobile that you have driven since the advent of fuel injection, your vehicle has been machine learning how to create the ideal fuel-mapping for your personal driving needs. Don’t believe it? Simply disconnect and reconnect your negative battery terminal lead. You will instantly see how sluggish your vehicle runs in its default limp mode until the variables of the data are processed and adapted for ultimate engine performance.
What is machine learning based upon?
The conceptualization of machine learning is based upon the same semantics as your own learning. That is, if you were to be given large sums of data and books to read, how would you sort out the useful information from redundant filler material? Would you be able to sort the useful information in a manner that allowed you to apply to the task described? Machine learning seeks to use similar logic.
Most of our learning is through words. We learn because we have this objective value to strings of words that produce a coherent narrative. This narrative elicits creative imagination from a complex depth of conflicting and harmonious subconscious and conscious processes. For example, we may feel the fight-or-flight urge to run subconsciously but consciously decide not to because we see that the danger is exaggerated for some reason. We also have the dualism of our analytical and creative centers of our minds that operate in the contrast of tangential, metaphoric, and organized methods. The complete yin-yang phenomenon of the human mind is so quintessentially human and irreplicable by computers.
Our ability to distinguish a coherent narrative from nonsense is also what separates us from random searches that bring up keyword information on Google. But, as we can see, Google is learning with the help of mechanical volunteers who help to quash results that shouldn't fit in a query string. Yet, with some foundational understanding of how the human mind works applied to an interface so that it operates more like the human mind, the sky's the limit on the potential for machine learning.
The technology of machine learning is not quite there because machines are only being developed at the analytical level. They appeal to our literal and data analytic rationales. In this manner, computers have to learn the way that we do regarding highly technical fields, through human correction of our peers when we get it wrong.
For this reason, computer programs that even grammar check blocks of text are still very flawed despite evolutions. This is because they do not recognize enough diversity to offer the appropriate suggestions. If they have more guidance from their programmers spending meticulous amounts of time on human proofreaders to define the contours of the various languages, then they'd be closer to AI.
How is it affecting your browsing?
Google, and other search engines like Bing (Microsoft), have been adopting machine learning into their search algorithms for a long, long time—basically, since Google revolutionized search engines in the nineties. Your computer learns where you are located and changes your results based on that. It knows your age, skin color, religion, interests, and income, all based on how you search and browse the internet.
As you search, this information is gathered and stored. When internet advertisers want to target a specific audience, all they have to do is set up their advertising preferences to describe their target customer. Google will then feed those ads to you if you are in an advertiser's demographic. For example, if you were to make a Google search asking what is front end web development, then you might see answers from a trade school, an internet provider, a blogging platform, and more. It all depends on where you live and what you typically search for.
This relationship is beneficial to all parties, because the advertiser is able to reach a likely buyer, Google gets paid for each click, and you are able to see ads that are relevant to your interests. However, this comes at a cost. Are we sure we want advertisers to be able to target us that easily? That has been the subject of debate for years in and out of legal and congressional discussion.
Is Apache Spark the frontier of machine learning?
The Apache Spark tutorial is a great primer for understanding the frontier of this new field. It is the real-time machine learning and data processing tool that is being used by all the major technology corporations like Amazon, Yahoo, and eBay. This type of processing is also in use by banks, government agencies, healthcare systems, telecommunications, and Wall Street.
When we are able to develop the programming of technology to understand the key principles that we may apply ourselves in sorting data, the entire process becomes much easier. Now, so much data is filtered, organized, and sifted by processing it through many nuanced algorithms.
These nuanced functions do everything from compress cell phone communications in real-time to formatting the pages on Twitter. You can see how AI is evolving through machine learning but hot it is still only one facet of human intelligence no matter how analytically proficient it becomes.