Machine Learning has been one of the most significant technological advancements in recent times. ML is a subfield of Artificial Intelligence that gives the machines the ability to learn without the need for explicit programming by employing large datasets and training algorithms. The accelerated development of this technology has resulted in many exciting and innovative use cases which will become mainstream in the coming years.

Let’s take a look into some of the exciting use cases of Machine Learning;

Autonomous Vehicles

Self-driving cars have created a lot of curiosity and buzz in recent years. Early reports have also indicated that autonomous vehicles can reduce traffic-related fatalities and lead to a safer future for transportation. However, it will take some time for the wide-scale adoption of these autonomous vehicles.

How do these self-driving cars work?

Self-driving cars are powered by Machine Learning and Deep Learning algorithms. Unstructured data from raw images are converted to structured data by annotating objects in the images. These labeled images are then used to train the ML and DL algorithms. 

Computer Vision models are developed based on the trained ML & DL algorithms which process data points from different sensors such as radars, lidar, camera to detect the obstacles and operate the vehicles autonomously.

Moderating Content on Digital Platforms

Most of the industries have become digital leading to an exponential rise in the number of digital platforms. This has led to a situation where a heterogeneous mix of content is added to the digital platforms daily and there is a need to identify unacceptable content quickly thereby adding complexity to the review process.

Machine Learning tools such as image recognition is already being put to use by companies like Facebook, Twitter, etc., to recognize objects within images. These tools also take into account other factors such as user experience and risk involved to classify them as content to be reviewed by humans. This eliminates the large volumes of content from reviewer’s queue allowing them to see the flagged-content and take a decision on whether to publish or remove the content.

Influence Customers’ Experience in Retail

The retail companies have been gathering customer behavior data such as their age, gender, spending habits, and their preferences. But the main challenge for these companies is to extract valuable insights from the data available both offline and online to improve their business.

Machine Learning helps retailers to discover patterns in the data on which they can act upon to improve the customer buying experience and enhance their brand. Retailers can use Machine Learning models to predict which products to offer and when to give discounts from previously acquired data and provide a more personalized experience to their customers.

Efficient Healthcare

Machine Learning algorithms can be used to analyze and understand diagnoses and risk factors and give recommendations for treatment of diseases. IBM’s Watson has been deployed in various hospitals where it has proven its ability to make highly accurate recommendations for the treatment of certain cancer types. Google has also ventured into this space with a Machine Learning algorithm that helps in identifying cancerous tumors in mammograms.

Cybersecurity

Cyber-attacks are skyrocketing globally and companies are investing in huge numbers to overcome this looming threat. It is estimated that companies will spend more than $1 trillion from 2017 to 2021 in countering cyber-attacks.

Researchers worldwide are of the idea that ML can be leveraged to prevent and defend against cyber-attacks. There already exists an overabundance of big data which can be used to train the ML algorithms to improve the state of cybersecurity. Defense systems are being developed which can run for 24 hours a day and easily spot any suspicious activity and act against it based on the past data.

Machine Learning has numerous use cases in various fields. Businesses have huge amounts of data with them. By understanding core business challenges, companies can use this data to train the ML models and transform their business with intelligent systems and gain a competitive edge.

At Data Labeler, we combine technology with human care to provide annotations and labeling of images and videos with pixel accuracy. Our data labelers maintain quality while processing & labeling the image data which can be used efficiently for various AI and ML initiatives.