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Machine Learning in everyday applications

Markos Giannopoulos
Web engineer probing the mysterious space between the human mind and what a machine can understand
Updated on ・3 min read

Machine Learning has been around for decades and is already used in a variety of applications such as spam filters, speech recognition and self-driving cars. It is defined as a subset of artificial intelligence (AI) that specifically deals with algorithms that can learn by themselves. It is estimated that today, 80% of all organizations use Machine Learning in some capacity even if that is not directly evident to the end user.

Understanding the basics of Machine Learning

Machine Learning is an integral part of the future of mobile and web applications. Software that can learn to aid the user intelligently will revolutionize the industry.

Without a doubt, we are on the verge of a learning breakthrough that will make machines intelligent enough to do tasks for us that they can learn to do better than us, just like humans have done for other humans for centuries.

There is a fascinating concept called the "intelligence explosion" that has been gaining traction in the scientific community. It's based on the theory that super intelligent AI will be created, and as it gets smarter, it will create new AIs, which will eventually lead to an event horizon (aka singularity) when the system becomes so intelligent that it's beyond human comprehension.

Why Machine Learning is important for every software app?

Machine learning is an important technology that enhances the performance of software and makes it faster and more efficient for the end-user.

Simply put, ML is nothing but the process of a computer algorithm gaining insight into data by detecting patterns and extracting knowledge without being explicitly programmed. Machine learning techniques can be applied to various domains like classification, optimization, regression and clustering.

Cool cases of Machine Learning in everyday applications

Machine learning is being used in a variety of industries to perform tasks from routine to complex. The following are a few examples of cool cases that demonstrate machine learning in everyday applications.

1) With machine learning, you can optimize your advertising campaigns by understanding consumer behavior. A majority of marketers rely on Google Adwords to drive traffic to their website. However, there's a catch - it's difficult to identify which keywords will bring in the most conversions without spending a fortune on testing different variations. Machine learning can help with this process by "learning" from past advertising campaigns and delivering insights about which words, phrases, and landing pages are the most effective.

2) You can also use it to classify images. In the medical field, machine learning has been used to aid medical diagnosis and treatment for decades. It can take hours for a radiologist to examine the X-rays of patients with brain tumors, but it only takes seconds for an AI algorithm to make the same assessment.

3) The retail industry is also benefiting from machine learning as they are able to predict customer needs before they even make their purchase decision. With this knowledge, retailers can provide tailored deals for customers with specific product preferences or offer personalized recommendations based on previous purchases and browsing history.

4) Memrise, the language learning application, uses ML to identify objects with the user's camera and provide the translation of what the respective word (not always fully successful of course, as shown in the screenshots below :))
Memrise image classification

5) ML has further applications in education, for example in natural language processing or in grading essays. One could also use ML in an educational setting to identify bad habits from essay answers such as missing or incorrect punctuation. It could also be used to identify and correct mistakes made by students when writing essays, like switching between first and third person points of view.

Future of Machine Learning in software applications - What to expect?

As these technologies continue to grow, they will be able to assist humans in ways that they traditionally couldn't. In the future, we will be able to use the help of AI assistants for everything from programming (Github’s Copilot is a first example already) to marketing or business intelligence.

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P.S. Another application of ML is in producing original content. About 70-80% of this post was generated by such an application as a first test. I promise to have a full review in a future post as I try this further.
P.P.S. Feature Image “Recycling” by Brian J. Matis on Flickr – Licenced under creative commons

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