Machine learning and artificial intelligence are a fun concept to think about. However sometimes when the conversation gets technical it takes the fun out of it and people lose interest. The fact is machine learning is being applied in the real world every day. In this article, we are going to discuss some Real World Machine Learning applications and how you can spot them as you go about your day.
Social Media Sites: Chatbots
The company leading the machine learning powered chatbot revolution is Facebook. Facebook has chatbots embedded with its messaging feature - Facebook Messenger. These chatbots are powered by machine learning and seek to provide services to individuals such as answering their questions, queries or commands in a very easy and free-flowing way that people don’t even know they are chatting with a bot. There are a plethora of chatbots now as anybody can create one and submit it to Facebook Developer. Some chatbots are so advanced that they can be indistinguishable from humans. These advanced chatbots have learned (via machine learning) to mimic the way humans chat including little quirk and connotations that a robot will normally not make.
Review Sites: Yelp
We all love reading reviews and using them to make decisions about what restaurant to eat at, what movie to watch, which shop to shop in. There is a whole industry of reviews that fuel and feed the consumer product and consumer service industry. One of the most popular review websites Yelp is using machine learning to improve the user experience. This is a perfect example of a Real World Machine Learning application. Yelp uses machine learning for their image categorization which makes it easy for people to navigate their website and review platform. When images are categorized and labeled more efficiently, it makes the user experience better and thus increases the usage of their review site and service.
Social Media Sites: Pinterest
Another example of Real World Machine Learning applications is with Pinterest. Pinterest is a visual site that users use to search for and save images. Users use Pinterest to curate boards such as vision boards, inspiration boards, and mood board. Pinterest uses machine learning to further improve the way users navigate their site thus improving user experience and improving the overall layout of the website. Pinterest can be a good source of image inspiration and it’s also a good referral site for great content. Machine learning makes content discovery easier with their algorithms that help users navigate the millions of images on the website. Just typing in a search word like “interior design” or “fashion ideas” will give users images that fit their query specifically.
Social Media Sites: Twitter
Twitter is the place to go when you want short content curated to not waste time and to get to the point immediately. The character limit ensures that users can read a twitter post and in seconds know what it’s all about. Twitter uses machine learning to try to curate timelines that meet the specific interests of its users. Twitter ensures that content is always current and relevant so that users stay on the site for longer periods of time since they will be more engaged with content relevant to them. The Artificial Intelligence technology used by twitter evaluates tweets and user profiles and scores and ranks them in ways that affect their visibility. So if a tweet gets lots of engagement, twitter bots will rank it higher and show it to more people. The twitter machine learning algorithms also take note of and track the activities that users show the most interest in and then they show those users more of the same kind of content since they have either deliberately or unknowingly chosen that that is what they are interested in and therefore more likely to engage with.
Search Engines: Google
Google is perhaps one of the leading companies when it comes to machine learning and artificial intelligence technology. They are working on so many aspects and applications of machine learning that it’s difficult to mention an area that they are not involved. Form self-driving cars, to advanced medical devices and healthcare technology, neural networks etc, Google and its parent company Alphabet is involved in it. One of the most prominent of this has been the Google Deepmind Network. Google is also working on other machine learning applications like natural language processing, speech translation, and search ranking and prediction systems and Wireless communication.
According to Google:
“The trick was employing large, highly optimized generative adversarial networks (GAN), or two-part neural networks consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples. The teams’ system, which they dubbed “BigGANs,” benefited from architectural tweaks, an increased batch size (2,048 images), and four times as many parameters (158 million) — the algorithmic levers used to control certain properties of the model — compared to prior art.”
Wireless communication has been mainly effective in the healthcare industry as it has improved the way doctors and nurses communicate with each other within the organization. Messaging platforms within the hospital can now be sued to send detailed messages such as lab results within the organization and also with other external organizations. In some healthcare organizations, these advanced devices have replaced devices like beepers and overhead pagers thus increasing the effectiveness of conversations.