Machine learning is a modern innovation that has enhanced many industrial and professional processes as well as our daily lives. It’s a subset of artificial intelligence (AI), which focuses on using statistical techniques to build intelligent computer systems to learn from available databases. 

With machine learning, computer systems can take all the customer data and build on it. It operates on what’s been programmed while also adjusting to new conditions or changes. Algorithms adapt to data, developing behaviours that were not programmed in advance. Learning to read and recognise context means a digital assistant could scan emails and extract the essential information. Inherent in this learning is the ability to make predictions about future customer behaviours. It helps you understand your customers more intimately and not just be responsive, but proactive.

Machine learning is relevant in many fields and industries, with its capabilities growing over time. Here are six real-life examples of how machine learning is being used.

 

1. Image recognition

Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images. This is useful for labelling an x-ray as cancerous or not, assigning a name to a photographed face, aka “tagging” on social media.

Machine learning is used for facial recognition within an image. Using a database of people, the system can identify commonalities and match them to faces. This is often used in law enforcement. Another common use is handwriting recognition, which can segment a single letter into smaller images to discern the handwriting.

 

2. Speech recognition

Machine learning can translate speech into text. Certain software applications can convert live voice and recorded speech into a text file. The speech can be segmented by intensities on time-frequency bands as well. 

In daily life, we utilise speech recognition software, like Google Home or Amazon Alexa. These applications enable us to use handy features such as voice search, voice dialling, and appliance control. 

 

3. Medical diagnosis

Machine learning can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. It assists them in formulating a diagnosis or recommends a treatment option. Oncology and pathology also use machine learning to recognise cancerous tissue (at a level comparable to physicians) and analyse bodily fluids.

In the case of rare diseases, the joint use of facial recognition software and machine learning helps scan patient photos and identify phenotypes that correlate with rare genetic diseases.

 

4. Statistical arbitrage

Arbitrage is an automated trading strategy that’s used in finance to manage a large volume of securities. The strategy uses a trading algorithm to analyse a set of securities using economic variables and correlations. Machine learning optimises to the arbitrage strategy to enhance the results.

 

5. Prediction

Machine learning improves prediction systems to calculate the possibility of fault. It's also useful for predicting  whether a transaction is fraudulent or legitimate. Machine learning can classify available data into groups, which are then defined by rules set by analysts. When the classification is complete, the analysts can calculate the probability of a fault. 

Predictive analytics is one of the most promising examples of machine learning. It's applicable for everything; from product development to real estate pricing. 

 

6. Extraction

Machine learning can extract structured information from unstructured data. Organisations amass huge volumes of data from customers. A machine learning algorithm automates the process of annotating datasets for predictive analytics tools. 

A real-life example of this is a model that’s used to predict vocal cord disorders and develop methods to prevent, diagnose, and treat the disorders. Typically, the process is tedious. But machine learning can track and extract information to obtain billions of data samples. This helps physicians diagnose and treat the problem quickly.

 

Machine learning in the future

Machine learning is a remarkable technology in the field of artificial intelligence. Even in its earliest uses, machine learning has already improved our daily lives and the future.

Interested in artificial intelligence and how it's affecting the global business landscape? Discover the benefits of AI for businesses in every industry. If you're ready to apply machine learning to your business strategy and create customised experiences, check out the Personalisation Builder. Use the power of predictive analytics and modelling to understand each customer’s preferences!