Published on March 5th, 2020 | by Manish Gehlot


How is Machine Learning Used in Different Industries?

I am sure most of you describe machine learning as artificial intelligence. It is not the same thing. In fact, machine learning is subset of AI. It would not be wrong to call it current generation and most practical form of AI. AI is much broader concept that aims to deliver a job in the most efficient and smart manner possible.

We humans had to devise machine learning owing to the sophisticated and complex design of generalized AI systems or solutions. As of now, with machine learning a set of data from a given time frame is used to swiftly deliver results. Machine learning is more extensively used than we can think of. For instance, your favorite music streaming application uses it when it starts to recommend songs you like. It is not magic. It is based on your uses. This example is a very basic understanding of machine learning. The machine learns from the data it has access to. Moreover the feedback when you do not like the music helps a machine in ‘learning’. Both the potential and amount of work done on machine learning is tremendous, thanks to the treasure trove of data available today. Big multi-national corporations like Amazon, Google, Facebook etc are de facto custodian of a huge variety of data on most of us. In today’s world, data is the new gold and machines has to be coded to analyze this data like us, humans. The idea is to code machine to think like us because a machine will be more quick, accurate or reliable and not forgetting unbiased.

I would say AI acts like super intelligent, but its programmed. It works like, ‘if this happens, do that.’ Machine learning is learning from the answers or responses. It is like a software writing a software. You can fake or mislead what the Machine learning is learning and teach it to make errors if you provide wrong answers. AI is not liberal and constantly does what the programmer told him to do. Hence, there is no one perfect approach to it.

Machine learning services have been approached in multiple ways with a combination of different learning algorithms types and models. It is not just the tech giants but a lot of industries are rampantly deploying one of the machine learning models.

Let us discuss a few industries where machine learning is being actively used.

1. Healthcare

However scary it might sound to you, we are steadily reckoning on machine learning for healthcare. It is one industry that needs to test anything thoroughly before approving it as production ready. Basically when you code a system to think like a doctor, imagine how quick diagnostics can go, followed by prescriptions and all this with technically zero chances of error. It will certainly improve health facilities in remote locations. The total period of recovery would also be greatly reduced. With machine learning the valuable skills of a medical practitioners can be better utilized to benefit a lot more patients. Recently Google blogged about machine learning model that detects cancer more accurately than a doctor. Further with portable sensors in wearable devices, machine learning can detect growing health issues in individuals. At times, it could save lives of many individuals who need correct treatment in right time. It could be a bridge between death and life. There is also a room for advance machine based follow-up post treatment and possible detection of further health issues or side effects, if any. The significance of adequate follow-up for complete recovery post treatment is a must. We can really use all the data available on the Internet from different medical facilities to predict a situation better in order to help a patient get well soon. It can also expedite a few prolix manual processes in medicine. Even if machine learning cannot fully replace a medical practitioner, it can assist one to help a patient robustly.

2. Education

A physical tutor or teacher cannot be replaced. But what if a panel of good teacher creates a training dataset. A model for education industry can be trained on the training dataset using a supervised learning method. It offers improved availability of better teachers through powerful web apps. This development aims for improved individual learning via machine learning. It can be really hard otherwise as specific style of learning of a specific person are found in the process. Machine learning will know the learning disability of an individual and help a person overcome it in a personalized manner without added cost or additional expenses. The unprecedented level of attention without losing the core qualities of teaching is applaudable. The relevance machine learning models in online education or learning has been recognized by the industry. A lot of smart, intelligent tutor systems have been developed to help a lot of students who deserve quality education from anywhere in the world. Business opportunity in healthcare industry is endless when we combine data and medicine using modern machine learning.

3. Financial and Banking services

Machine learning has entered into financial services industry too. It is mutually beneficial. The tech-savvy customers of a bank or any financial institution crave for all the services in a more personalized manner on their favorite smartphones. A banking company sees this as a cost effective opportunity to offer all the services through a virtual agent backed by a machine learning framework that it deploys. The number of queries these machine learning based systems can handle with the stratum of accuracy and precision are fruitful for any bank in long-term. It is also working out for a lot of banks currently. Nuance’s Nina is one such solution that is serving Swedbank’s growing customers with a better first-contact resolution rate than ever. Machine learning is also helping people make better purchase or investment choices based on the availability of data with advance prediction algorithms. It is also used in fraud detection based on the availability of the transaction dataset and various other entries.

4. Social media management for businesses

With wide-spread availability of 4G enabled smartphones even in developing countries. Social media and network companies have received more traffic than ever before. Family of Facebook apps are being used by 2.26 billion people on daily basis in Dec 2019 as per the fourth quarter report of 2019 by Facebook. So, this obviously attracted attention of all the major brands and companies. They wanted to stay connected with their customers over social media and network platforms. The mileage is the unprecedented access to variety of metadata and related information about their customers. Social network and media is not longer a tool that assist individuals stay connected to their loved ones. Its role in the society has outgrown. Today, it is hard for any brand or business to avoid social media. Customers are using this as a tool to contact companies for queries and it would not be wrong to call it an extension to the customer service via official channels. Tech-savvy customers are willing to get resolutions over social media platforms. Machine learning can be used here to deliver personalized and immediate resolution to customers right from their favorite social media platform. The magnitude of doing business and making numerous clients’ happy is not something you want to miss a thriving business. After all reputation and positive online presence matters as it counts as data. Data is important, is not it? Businesses can also use the same platform to push offers, notifications to their customers in their mother tongue. It is hard to define a limit of outreach with the advent of ML based social media management.

Generally, nothing is virgin when we talk about machine learning. All those recommendations and suggestions are using some form of machine learning in back-end. It is about data which is being studied to deliver proficient or favorable predictions. Various industries and business are making use of it mostly using web-based power apps. Developing these apps with correct combination of model and algorithms type can be tricky. It requires dedicated team of developers that extensively understands your requirement to deliver a solution for your business.

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About the Author

I am a privacy, security, encryption and software freedom enthusiast. I am into VPNs, TLS security. Recently I also got into technical writings. I am working as a VPN support and consultant at some nordic VPN providers.

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