6 Common Applications of Machine Learning That Are Hiding in Plain Sight

Top Machine Learning Applications in 2020

  1. Dynamic Pricing

Machine Learning, a sub-branch of Artificial Intelligence, has established itself as the new go-to technology for businesses worldwide. Whether it is e-commerce or healthcare, almost all the industries are using Machine Learning extensively to make futuristic solutions and products. Machine Learning depends heavily on programs and algorithms that help machines self-learn without having to be instructed explicitly. Machine Learning is pretty much dictating our daily lives- how, you wonder? Let’s look at the top applications of Machine Learning to understand how it is shaping the digital economy.

1. Dynamic Pricing

As crucial as it is, pricing strategy is one of the oldest puzzles of the modern economy. Whether it is the entertainment industry or the consumables industry, efficient product pricing is essential for profit margins and affordability. Depending on the objective, there are several pricing strategies that businesses can choose for sales and marketing. However, choosing the right pricing strategy is easier said than done. Several decisive factors like cost of production, consumer demographics, demand curve, market control, value and more need to be adequately aligned for any product to be priced properly. Thankfully, Artificial Intelligence has effectively resolved this issue in recent times. AI-powered pricing solutions have helped businesses understand consumer purchasing behaviour and price their products accordingly.

Machine learning tools use insights from data to create logic. This process improves with the amount of data that is fed to the machine learning system — the more the data, the better the results. Without using direct programs, these machine learning softwares use humongous amounts of data to improvise and deliver accurate pricing strategies. Machine learning algorithms use extensive data analysis to find optimised solution functionality. These softwares use various ML pricing models like granular customer segmentation with cluster analysis and competitor and attribute-based pricing and KPI-based pricing to reach an optimised pricing range.

2. Transportation and Commuting

All the taxi-booking, vacation planning apps that you use run on machine learning. Whether it is customer experience or demand-supply gap, machine learning systems use data to manage and optimise the booking process. While using a ride-booking app, you must have come across recommended destinations. Machine learning algorithms use historical data to understand the most frequently travelled routes and provide suggestions accordingly. Apps like Uber and Ola use extensive data analysis to predict time and areas of demand. Once the app calculates the demand, drivers are alerted so that they can offer rides for that particular area. This is how ride-hailing companies manage the demand-supply gap. Machine learning algorithms also reduce ETA by recommending the fastest routes in real-time. For peak hours, this demand-supply predictions work by suggesting higher prices to make these services profitable.

Vacation planning apps use the same system to recommend the cheapest flight fares, hotel bookings, and more.

3. Fraud Detection

While the vast amount of data available on the internet makes for a great case of data studies and analysis, it also increases the chances of fraudulent activities. Machine learning is emerging as an effective technology to secure our cyberspace. Supervised and unsupervised ML models are being used to detect different kinds of online frauds, ranging from spotting anomalous behaviour to preventing money laundering. Even the entertainment and media industry is facing undeniable problems with online frauds. Fake news is a big issue today that can disturb the economic and political situation of any nation. ML semantic analysis studies structured, unstructured and table-type data to detect fake claims and news. ML algorithms also look through existing repositories of news to find similar claims and validate the authenticity of any news piece.

Same holds true for online scams and identity threats. Fraud analysts across industries rely heavily on machine learning tools to investigate claims, news and more.

4. Virtual Personal Assistant

Virtual personal assistants have surfaced as one of the most significant finds of the 21st century. Machine learning algorithms have done phenomenal work in the field of speech recognition, natural language processing, text to speech and speech to text conversion. Once you ask them a question, they scan through the internet to find you relevant answers. In addition to that, they also keep track of your schedule, goals, and preferences to recommend relevant information. These virtual personal assistants feed on all your queries and inputs ( asking about the weather or the traffic) to continually improve and self-learn.

ML algorithms collect and refine information on the basis of any user’s past behaviour. This process helps in customising results according to the user profile.

5. Social Media

With more than 2.5 billion active users every month, social media platforms like Facebook and more are some of the biggest communities today. Social media has become an indispensable part of our lives. Targeted ads, friend suggestions, and personalised news feed are a few of the ways in which machine learning algorithms are improving our experience. Machine learning algorithms go through your profile to understand the friend requests you send, friends you connect with, groups you join, your interests, and based on that provide suggestions on who you can become friends with. Similarly, for Pinterest, ML algorithms recommend similar pins based on the objects (pins) you have pinned in the past. Computer vision, a subset of machine learning, scans through images to identify objects and patterns and uses this data to create recommendations.

Computer vision is also used for the face recognition feature in Facebook and Google. Every time Facebook asks you to tag yourself in a photo, it is because computer vision has scanned through your facial features to recognise the features unique to you. Once the ML systems have collected sufficient data on your facial features, it can accurately suggest the tag.

6. Instant Translation

Google Translate and other such apps are making language barriers a thing of the past. Apps like Google Translate and iTranslate use machine learning algorithms to make translation as accurate and semantic as possible. The ML programs too have evolved from rudimentary levels to include complex sentence structures and broader contexts.

Google Neural Machine Translation uses Natural Language Processing to self learn from numerous languages and exhaustive dictionaries to translate languages correctly. It also uses techniques like NER (Named Entity Recognition), Chunking, POS tagging and more to understand language intonation and deliver the most relevant translation. Translation techniques include:

Dual learning: Texts are translated back and forth from one language to another repeatedly until a natural, accurate translation is delivered.

Deliberation Networks: Similar to dual learning, this method involves translating the same text over and over again to improve the final results.

Agreement regulation: ML algorithms read text from left to right and then from right to left again to create a match. The end result is a consensus from both directions to eradicate errors.

Machine Learning has clearly made an entry into our lives and is here to stay. Its applications are no longer limited to enterprise use. ML programs and algorithms have evolved over time and taken over most industries to improve consumer experiences.

If you are interested in the domain of AI and machine learning and want to learn more about the subject, check out Great Learning’s PG program in Artificial Intelligence and Machine Learning.

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