Machine Learning, as a subset of the wider field of Artificial Intelligence, aims to create computer applications that can outperform humans in certain tasks by making them smart and being able to learn and improve by themselves.
It is already being used in a vast variety of businesses across many industries and in the public sector as well. There are many use cases ranging from predicting user behaviour in e-commerce, optimizing delivery routes up to hospitals analyzing patient data, saving time and cost in the process.
91% OF EXECUTIVES SAY AI WILL HELP THEM OUTPACE THEIR RIVALS, ACCORDING TO A FORBES INSIGHTS SURVEY
- 6 out of 10 C-level executives surveyed by Forbes Insights believe AI is a key enabler of their organization’s future success.
- 4 out of 5 of those organizations have AI programs in place or are currently piloting them
- 74% have 10 or more separate initiatives underway.
Where is it used?
Banks and other businesses in the financial industry use machine learning to identify important insights in data and prevent fraud and more. This enables identify valuable investment opportunities and helps investors know when and what to trade. On the other side data mining can reduce risks by identifying clients with high-risk profiles and can detect fraud.
Government agencies such as public safety and utilities have a particular need for machine learning since they work with all kinds of data that can be analyzed and used for predictions or forecasting. Machine learning can also help detect fraud and minimize identity theft.
Machine learning is vastly used in many health research fields and is especially growing rapidly trend in the health care industry, thanks to advancements in wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze such data to identify health risks and prevent them.
By analyzing your purchase and browsing history through machine learning, websites can recommend items you might like and forecast purchase behaviour at a larger scale. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, optimize prices and supply demanding and more.
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.
Oil and Gas
Machine Learning is also being used in the oil in gas industry for finding new energy sources, analyzing minerals in the ground, streamlining oil distribution to make it more efficient and cost-effective and for many other purposes. This field is quickly expanding in research and adaption of machine learning and AI.