Machine learning is an inevitable phenomenon nowadays. It is a part of AI and has been influencing our lives in many ways. It is true that this has been prototyped for a long period of time until humans could actually confirm the learning capability of the machines.
We can consider the 21st century as the revolutionary period for technology and science. However, the measure for the development of technology has been in the past decade itself. The idea of Artificial Intelligence was coined back in 1950 but it came into use in the 21st century.
Machines learn via pattern and that had to be incorporated by the humans. This took a long period of time. If the machine could pick up the data, it can easily help humans in catching up the pace.
Challenges of Machine Learning
Apart from the advantages of machine learning, there are several challenges that are faced in getting the ML ready. Here is the list of few challenges faced by ML:
Learning from the bulk data: With the advancement of technology, the amount of data that we are processing is really creating huge traffic and that is the reason we need to take the help of the big data in order to manage the bulk data. On 2017, Google has reportedly processed approximately 25 petabytes data per day and this continuously increasing. The biggest challenge is to process such huge data of information. The distribution of frameworks with parallel computing is one of the best ways to take care of the entire process.
Learning the low-value density data: The core reason for machine learning for analyzing big data is to extract a large amount of data and transform into a small and easy to store formate. It is important to find the value from the voluminous data which is having a low-density. This is a huge challenge. Data mining and knowledge discovery in the database can be of huge help in these circumstances.
Grabbing the knowledge from the various types of data: There are a variety of data being generated from different companies and enterprises. There are three types of data, structured, semi-structured and unstructured data that further leads to heterogeneous, high-dimension and non-linear data. Therefore the Machine learning will have to be aware of all these things in order to grab the knowledge from it. It is indeed a huge challenge to get all the information from the vast variety of data generated by all the enterprises across the world.
Learning and offering results quickly: There are several tasks that need to be solved on time. The velocity of the data is one of the major features of the big data and if the said task is not completed on time the dependants might face a huge problem. For example earthquake and stock market predictions. Therefore, it is really necessary to sew the awareness about an earthquake so that people can be ready to stay protected or even evacuate the area right on time.
Industries That are Leveraging Machine Learning
Machine Learning might not be a very new concept but the usage of the same began really late. However, thanks to the wearable devices that make the ML more actionable than ever. Here is the list of industries that leverage to ML:
- Health Industry: Machine learning in the healthcare industry has been a dominating factor. With the help of actionable devices, it can access the patients’ health in real-time.
- Finance sector: In the finance sector, ML is used mainly for two purposes: a) To identify the valuable insight; b) to prevent the fraudulence.
- Retailers: In this industry, ML serves mainly to personalize the shopping experience in the real-time. It can, therefore, be a reason for the increased lead and conversion of the retail business.
- Government sector: Since this a public sector and encompasses a large number of public privacy, safety and utilities and therefore, it is important to safeguard and maintain the privacy policies. ML can save the information and make sure that it provides the required information on time.
- Transportation: Transportation is made easy with that of the ML as it offers smart operational methods. It identifies the pattern and gets a proper insight into how things work and get the perfect planning done.
Machine Learning in Healthcare
When it is about utilizing the maximum benefit of machine learning, healthcare is a sector where it has left the maximum mark. In fact, in the year 2017, 7.1 million Americans wore a sensor device that accesses their health in real-time. The information was later sent to the Machine Learning analytics center in order to flag the anomalies and provide an alert to the treatment professionals.
We hardly see doctors and nurses with pen and paper any longer. It is a noteworthy point that most of the hospitals in the US are using tabs and laptops to generate the patients’ report and make sure. It is said that there are lesser to no chances of confusion and that accurate treatment is being done. Machine Learning in healthcare has been an influential process. This process is the speed-up secret of the health care sector.
Some diseases require diagnosis then and there and thanks to the machine learning in healthcare that is this is now an easy process. Doctors are now being informed about a patient and their problems within a few minutes. It is interesting to know that ML in the healthcare sector is now being able to detect cancer long before humans can.
Conclusion
Technological advancement is a boon to all the disease that had caused fatal death to humans. Machine learning in healthcare is just taking the next step. Various applications are being developed that promises to offer ahead to time disease detection, prevention of diseases and delivering care at a much cheaper rate.
