Many people are beginning to realize that artificial intelligence (AI) is not a topic of the future but a reality of today.
Think about Siri. She uses AI to hold a conversation with you and seem like a real person. But, of course, there are issues with Siri. She uses language processing software to compare the input (your voice), and compare it to the most likely output (what she thinks you said), which is how she appears to be having a conversation with you. If you were to ask her the same question repeatedly, she would always choose from the same preset responses – there would never be an instance where siri would respond with a different set of commands to the same question for different people. No matter the person, the response(s) will always be from the same few algorithms. This happens because Siri isn’t using Machine Learning.
Machine Learning is a subset of AI, but it takes the data that the user in inputting and changes its behavior accordingly. Essentially, it is the ability for an artificially intelligent robot, with guidance, to be able to learn and from past behaviors and change them for the future.
Today, I want to show you three of the coolest ways that Machine Learning is being applied in your daily life, without you even knowing.
1. Search Optimization
Google is using Machine Learning to track patterns of user behavior. They are trying to make their search engine “read your mind,” by having whatever web page you are looking for pop up as the first item in the tab.
Google tracks whether it successfully brought you to the right web page is two factors are fulfilled:
a) the user clicks on the first link;
b) the user stays on the website for a certain amount of time.
Machine Learning is used in this situation by taking massive amounts of data from the keywords that the user typed and past information from their previous searches to bring them the result that is most correct. If it is unsuccessful according to the two above factors, Google will make a note of it in their search engine, which will be used to help improve the search accuracy for future searches.
Image Source: YouTube
2. Recommendations
You know those “suggested” items you are starting to get on your social media feeds, your Netflix shows, or your amazon purchases? All of these entities are using Machine Learning to track your behavior on their platform, and based on what you do, they find people/shows/products that are similar. For example, if I watched 5 Russell Peters stand-up comedies in a row on Netflix, the Machine Learning component of the app will collect the data and will think “Hmmm… Shaan really likes Russell Peters… why don’t I show him the newly released Russell Peters stand-up comedy?” So, when I finish watching Russell Peters, I will most likely get a suggestion with his next comedy. Obviously, it isn’t this simple and Machine Learning entities take a variety of factors into consideration to account for “What if I finish all of Russell Peters’ stand-up comedies?” Then, it will still be able to suggest alternate shows that are of the same genre. These systems are very robust!
Image Source: Tech Hive
3. Trading
Machine Learning is even being used in the stock market. While their systems are still based on probability, many prestigious trading firms are using Machine Learning to help them determine the best way to navigate the stock market at any given time, not because they are “smarter” than humans, but simply because they can take in such vast amounts of data in a split-second and all the data they are receiving is within the minute.
Image Source: BBC
Personally, I find it incredible to imagine that machines are getting so smart that they can read, analyze, and interpret data so quickly. However, what I find most important is the commonality between all of these applications of AI – they are all helping to make people’s lives much more efficient. If there is one thing that you take away from this piece, it is that humans will be able to thrive more than ever in a co-existing relationship with our self-taught robots, and we have to do our part by embracing the change that machine learning will bring to our world – not fighting it.
