Machine learning, from the perspective of the average person, is the ability of computers to learn apart from a human programming the code. (We really don’t need to know the details, do we?) But as has been demonstrated, sometimes computers can get too smart for our own good. Just ask Facebook programmers.
In this article we will limit the discussion to machine learning that is positive for humans and is beneficial to their existence. From the title of this article you can easily conclude that is what is good for Big Business is good for the average consumer, otherwise how would they make money? You will find that the 5 ways that follow are likely to affect millions, if not hundreds of millions, people around the world. Naturally, much of the forward progress in machine learning is connected to the Internet in one way or another.
1. Since Facebook has already been mentioned, we can start there.
Facebook Messenger, love it or hate it, is using machine language in its chatbots and has improved to the point where the responses from the computer are virtually impossible to separate from normal human communication. (A chatbot is technically known as an Artificial Conversational Entity, but chatbot seems so much friendlier. Think IBM’s Watson.) Broken English (or any other language) generated by a chatbot can be annoying or cause a huge problem. Chatbots are getting better.
Also, Facebook is using machine language learning to filter out low quality content and spam from your view, and that happened before the Russian interference investigation, so we know there is still more work to do.
2. When you talk about Facebook, a discussion about Twitter cannot be far behind.
Twitter is also using machine language to perform an important task that sometimes irritates its users – deciding which Tweet is displayed at the top of the list. The company has created an algorithm that is used to decide that for you, so your tweets do not have to be displayed in chronological order. The future holds the potential for machine language to allow you to specifically customize how you want your Tweets ordered.
There is a question that looms here, which is if Twitter maximizes the potential of its machine learning algorithm, will that drive more people to use the service?
3. The next big business to be on the list is a natural – Google.
Their search engine is the preference for a sizeable chunk of the world’s search engine users, and when you ask about how it came to be so popular you immediately think of machine learning. Those more technically minded think about neural networks, which are a series of interconnected units that are able to learn as they go. Essentially, the Google search engine gets “smarter” based on what its millions of user type in, allowing you to get the list of results you are looking for.
Anyone who seriously uses Google knows that it has some flaws in its result lists, but like the rest of us, it is learning. But the company is expanding their application of machine learning to include natural language processing (its YouTube translator is quite good) and prediction systems that can have economic and political implications.
4. Like Facebook, IBM has been previously mentioned so it naturally will be added to the list.
Most people have seen at least one commercial showing off their Watson technology. What the company has done is to extend that machine learning technology to the field of healthcare. Watson has been proven to be effective in providing recommendations for the treatment of certain types of cancer. Not all mind you, but some.
One thing to note here is that the first 3 big businesses pretty much were expected to be on this list. (Apple, where are you?) But IBM is generally considered to be a retro company when considering technological advancements. But there is a reason it has survived a corporate history that goes back to the 19th century.
5. Last but not least is one of our favorite review companies – Yelp.
If you have used Yelp at all you have either posted a picture or viewed one as part of the review process. When it comes to be a first time customer to a new store or restaurant, a picture definitely speaks 1,000 words. Yelp had to find a way to organize the millions of pictures uploaded to its site that pose beside the text reviews. Yep, it turned to machine learning to solve the problem. The picture classification technology Yelp uses to make all those images easy to find is based on machine learning, so you can continue to upload your best pics of the place you love (or hate) the most.
If you’re thinking, “What’s the big deal?” because it is easy for you to decide with 99% accuracy whether a picture shows the inside or an outside of a building. But computers have not yet become totally human, so applying machine language is essential for categorizing images correctly (100% of the time?)