From voice assistants to self-driving cars, learning is revamping not only the way we interact with machines but also how we interact with the world. It is becoming one of the hottest technologies in the market, making mobile apps and services smarter and better than ever.

According to IBM, about 90% of existing in the world has been in the last 2 years. On average, we generate about 2.5 Quintillion bytes of data every day. This massive amount of data can’t be processed and managed by humans physically. This is where Machine learning comes into the picture.

Machine Learning

Usually, people interchange the terms Artificial Intelligence and Machine Learning. However, they are different. Artificial Intelligence is the concept of making machines capable to performing tasks without human intervention, such as building smart machines; while Machine learning (ML) is a subset of based on the idea of making computer algorithms that automatically upgrade themselves by discovering patterns in existing data without being explicitly programmed.

The whole processing of ML tools depends on data. The more data an algorithm obtains, the more accurate it will become and thus, the more effective the end results will be.

Machine learning uses various techniques for data extraction and data interpretation. However, the two prominent techniques are supervised learning and unsupervised learning.

Supervised machine learning creates a model that could make predictions based on data in the presence of uncertainty. Whereas, unsupervised machine learning determines hidden patterns or inherent data structures, taken into consideration for drawing conclusions from data sets comprising of input data without labelled results.

Applications of Machine Learning in the Present World

Today, machine learning (ML) has impacted an enormous number of industries; including retail, healthcare, robotics, mobile app development and travel. Companies are using machine learning in different ways. Some of the more prominent ways are:

Personalization

Have you ever wondered how Facebook shows ‘People You May Know’, or Amazon suggests product recommendations? It’s all possible because of machine learning. The technology is used to process an extensive amount of user data: personal information, search history, content interactions, etc. to offer personalized data that we see.

Netflix saved nearly $1 billion due to machine learning algorithms that could recommend personalized TV shows and movies to the users/subscriber, instead of hiring a team of movie critics to bunch titles together haphazardly.

Image Recognition

Machine learning algorithms are also used to find and process objects illustrated in images. This concept is widely used by various applications; like dating apps, photo editing apps, user-authentication apps, and more. In fact, Google is relying on this technology to let you perform image searches, and Facebook is working on launching an ML-based feature that describes images to vision-impaired people.

Speech Recognition

Ever wondered how ’s Siri responds to your commands? Or how Amazon’s Echo lets you place an order or get weather reports just by speaking loudly? Or how software can offer speech-to-text translations? This is all possible thanks to Machine Learning’s feature of speech recognition.

Fraud Detection

Machine learning is also significantly used in banking/finance industries to cope with fraud. ML tools scan the transactions you make in real-time and provide a fraud-score. If the fraud-score exceeds a specific threshold, your account is automatically frozen. If this had to be done manually, it would be nearly impossible to review thousands of data points per second and make a decision.

PayPal has various machine learning tools that study billions of transactions and determine which is legitimate and which is fraudulent. This helps in dealing with money laundering cases.

Health Diagnosis

Machine learning is also becoming a buzzword in the healthcare . It is used for different purposes, like drug discovery and robotic surgery.

Recently, Google created a machine learning algorithm that helps detect cancerous tumors on mammograms, while Stanford is using the technology to identify skin cancer.

Predictive Analysis

Machine learning tools, along with Big data analytics is used by the mobile app developers and marketers to understand how the users interact with a mobile app and group the data under different categories for predicting next step to be taken for engaging users and increasing the conversion rate.

Better Gaming Experience

In 1952, a UK graduate created a tic-tac-toe game employing the basic of Machine Learning. And today, here we are with video game engines like Unreal and Unity that use machine learning for analyzing the video feeds from games and fully interpret what it receives. This provides a breathtaking video game experience for all of us.

Future Predictions

Machine learning has already established its importance in our daily , and a lot more has yet to be uncovered. With the booming market for Internet of Things (IoT) solutions – the technology connecting billions of devices and their data streams altogether – it’s a sure that more digital data will be obtained, which gives a hint of increasing demand for machine learning.

As per the present scenario, following are the different places where Machine learning will be enhancing our experience:

Higher level of NLP

At present, ML-based NLP (Natural Language Processing) is still in its infancy. Right now, there’s no such algorithm available that could understand that various words have a different meaning in different situations and act successfully. However, it is expected that such algorithms will come into existence in the future.

Deeper Personalization

We will receive more personalized services with fewer ads in the future.

Neural networks running on mobile devices

In the future, mobile devices will have the potential to conduct ML tasks; presenting new opportunities for speech recognition, face detection, image processing, etc..

Real-time Speech translation

In 2014, Skype launched an application called Skype Translator which translates speech from one language to another in real-time. Since then, it has undergone various updates. If it continues to evolve in the same way, we will soon be able to enjoy high-quality international communication – eradicating language barriers.

Extending Mobile battery life

Machine learning is predicted to be used along with the automation of system resource allocation for mobile apps to cut down the unnecessary battery consumption.

In conclusion, Machine Learning will continue to evolve to make our daily lives easier, and lower costs for business to operate. This will result in a boom in cloud-related jobs, but will definitely impact low-skilled labor markets. I definitely feel that the costs associated with making a more automated, intuitive world are worth it. Do you agree? I’d love to hear from you in the comments section below.



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