Four most important trends, technologies and innovations related to machine learning

Introduction

Machine learning has been at the centre of transformation of the industrial sector in the age of digital convergence. The advancement in machine learning can be attributed to the fact that this technology is very dynamic and keeps on adapting according to the changing circumstances. In fact, the dynamic nature of the discipline is acting as a catalyst for driving transformation in different sectors. With the help of machine learning, it is possible to spin innovation in sectors that were otherwise reluctant to change.

Academic adaptation

Beyond doubt, the academic sector was as reluctant to change as the industrial sector itself. The influence of machine learning was first felt in the industrial sector. With the help of technologies like the internet of things, automated machine learning, improved cyber security and natural language processing, machine learning established itself as the most supreme technology that businesses can benefit from. Machine learning online courses are now steadily gaining prominence at par with offline courses. This juxtaposition between the academic and industrial sector provided a new lease of life to different machine learning technologies. Thus, it becomes important to shed light on the most important trends, technologies and innovations related to machine learning.

Connectivity by IoT

Iot or internet of things has been the most important development of machine learning that has significantly transformed workplaces into a smart ecosystem. This breakthrough has been intricately linked to cloud technology as well as 5G Technology so that great connectivity can be maintained between devices. This would not only allow devices to communicate with each other but also allow us to process data from different iot sensors in real time. The data collected from the sensors would serve as an important resource for training of different machine learning models and improving the performance of systems that run within the iot environments.

Automated version of machine learning

The automated version of machine learning can be defined as a codeless or programming free interface that allows even generic users to make full use of machine learning technology. With the help of automated machine learning, it is possible to have a high degree of efficiency as well as automation in different types of sectors without the need of programming professionals. The most important reason due to which automated machine learning has become popular is due to its generic use to such sectors which were otherwise functioning on traditional technology. For instance, auto ml is becoming one of the most important technologies that is transforming logistics and supply chain management.

Best of cybersecurity

With the help of machine learning, it is possible to take cyber security to the next level so that systems become immune to cyber attacks. One of the unique ways is to develop an intrusion detection system that is highly proficient in detecting and mitigating cyber attacks. In the recent past, we have used individual methods like supervised learning, unsupervised learning, support vector machines and other machine learning methods to improve our cyber security systems. In the present time, we are now looking at a consolidated model that makes use of techniques like bagging, bootstrapping, random forest models and decision trees to conceive an intrusion detection system that has high accuracy and efficiency in detecting cyber attacks.

Ethical dimensions

With the advent of machine learning and artificial intelligence technology, the ethical dimensions are also becoming the centre of debate. It is important to understand dimensions associated with machine learning and artificial intelligence with the help of an example.

Let us consider that a self-driving vehicle is trained with the help of machine learning technology. It happens that the self-driving vehicle is forced into such circumstances where it has to make a contradictory choice. The choice involves hitting a motorcycle rider on one side of the road who is wearing a proper helmet. On the other hand, it can choose to hit a car that is ferrying children. Both these circumstances have ethical dilemmas involved with them. This is where the amount of training done with the help of machine learning proves inadequate for the self driving vehicle. One of the most important ethical questions is the shouldering of responsibility of the instantaneous decision making done by the self driving vehicle.

Conclusion

Apart from the above mentioned technologies, we are also seeing the development of generative adversarial networks as well as natural language processing systems that are amplifying the applications associated with machine learning. These applications would continue to further proliferate in the time to come.