Machine learning and artificial intelligence are said to be shaping the way people will live in work in the future. It’s already grown rapidly over the last decade, with automation software being introduced into workplaces and our daily lives. However, machine learning is still yet to infiltrate the world on a widescale level.
What Is Machine Learning?
Machine learning is closely linked to AI (artificial intelligence). It summarizes a computer’s ability to self-teach and improve its performance of a specific task. Essentially, it’s about analyzing large volumes of data and interpreting it into performable tasks. Machine learning can make predictions and learn from them, whether they were correct or not. It can then use this information to refine its knowledge, making the correct prediction in the future. Companies such as Google, Facebook and Amazon use this algorithm to analyze a user’s search activity. This means that they have access to extraordinary amounts of data.
The Impact on Businesses
Machine learning has the potential to automate many aspects of the workplace. It can effectively change the way businesses operate. When we think of robots in the work environment we think of automated machines, such as the checkout machines in the supermarket. However, machine learning delves much deeper than just pre-programmed automation. Machine learning refers to artificial intelligence that can learn and adapt, for example a customer service robot can transform this part of a business. Automation software could refine customer service answers to queries by analyzing chats with customers and adapting their responses to be more accurate every time.
Industries such as education, banking, retail, law, and healthcare all stand to benefit from machine learning. Machine learning can help human employees with their daily tasks, decreasing costs in the long run yet improving effectiveness. We are also now seeing mobile app development companies starting to link with machine learning to improve customer experiences as well as helping employees.
Education
Machine learning can help the education industry by lessening the responsibilities and pressures placed on teachers. Marking papers is something that can take up a lot of a teacher’s time. AI would be able to come up with an algorithm for grading papers that could eliminate the hours spent marking by teachers. Machine learning could also be programmed to establish study plans and tailor it to each child’s needs. Though no robots will be taking over a teacher’s job anytime soon, they can certainly help ease the daily number of tasks many teachers face.
Retail
We’ve already touched on how automated software has made its way into retailers. This software could be further developed with machine learning to replace tasks of workers. For example, many responsibilities can now be mechanized, such as stock room tasks and customer services. Where machine learning has room to adapt is with self-service checkouts and other store tasks. Machines are still not capable enough at handling problems as efficiently as humans just yet.
Healthcare
Computers are already used as part of healthcare initiatives in surgeries and hospitals, where they can give faster patient diagnosis and also predict potential problems a patient may have in future. Hospitals are currently using AI algorithms to more accurately detect tumors and analyze moles for skin cancer. The hope is that the technology will develop so much that it will help in the search for a cure to cancer.
The Future of Machine Learning
Machine learning has already come such a long way, improving work life in many industries as well as positively impacting customer experience. However, there is still a way to go if we want to see it used in our everyday lives. The ideal scenario is that is can be used to help employees with their jobs, meaning businesses will save money and time.
Written by: Paul Matthews
Paul Matthews is a Manchester based business writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes’ latest pieces.
