5 Must Follow Trends in Machine Learning

5 Must Follow Trends in Machine Learning

5 Must Follow Trends in Machine Learning

Artificial intelligence (AI) and machine learning (ML) are the new buzzwords in the tech industry.

With considerable funds already being spent in AI and ML research and development, they have also become the new interest of start-ups and conglomerates alike; and with a significant rise in the number of AI-based start-ups, the number is only going to go up.
Machine learning is being used in almost every sphere to bring users personalized services that were previously unheard of. As a result, every IT business is now striving to keep up with the latest machine learning trends. While there are machine learning courses that could get you into the race, we provide you with the following list of the five must follow trends in machine learning that can help you stand a class apart:

Machines Teaching Themselves

Machine learning algorithms that are capable of teaching themselves and other connected systems are gaining popularity among IT companies. As a result, intelligent systems powered by AI software are getting faster and more efficient than ever. Where it previously took manual data mining and feeding to the bots, now thanks to ML algorithms, AI itself is capable of foraging the data, thus taking machine learning and artificial intelligence to another level. However, in some cases, owing to cost and security reasons, some data is hard to gather, even for machines. In such scenarios, machines can share experiences with each other to build up the missing data. In other words, machine learning can self-amplify and grow at exponential rates. This new generation of ML algorithms would require even fewer explicit programming, thus reducing the need for human intervention. Examples of machines teaching themselves can already be seen in the Facebook AI that is designed to build a software that makes up its language, and in Google’s AI that is capable of building another AI without any explicit programming.

The Growth of Edge Computing

For those not acquainted with the concept of Edge Computing, it is used in IT systems to optimize cloud computing systems by the reduction of distance of data processing unit from the source of data. This is achieved by efficiently mimicking a cloud through the provision of compatible services and endpoints that can be used by cloud-based applications. However, if this still does not answer the question of why Edge Computing is the trend you ought to follow, then it would become clear by the fact that machine learning software is heavily dependent on the agility of a program to execute complex operations quickly. Failure of which results in low-latency applications which often struggle and lag. Edge Computing, on the other hand, provides developers with an easier way of deploying code and hence offers a viable solution. Owing to this ease of deploy ability, Edge Computing is being extensively used in autonomous vehicles to host artificial intelligence applications. It provides these vehicles the real-time data which is necessary to make these vehicles run effortlessly.

Machine Learning for IT Operations Management (ITOM)

Every organization faces the struggle of sorting huge volumes of operational data log files, status reports, error files. Sorting through this data and making sense of it becomes a complicated and time-intensive process if done manually. The solution to this problem can be achieved by intelligent automation engines leveraged by extensive machine learning algorithms. Machine learning coupled with artificial intelligence provides increased visibility and predictive analyses that can greatly reduce service outage risks. This helps transform organizations from a reactive state into a proactive state.
Tech giant ServiceNow has invested into developing their intelligent automation system and has come up with a product that can streamline processes, increase accountability, and reduce costs. Gartner has also named ServiceNow a leader in the Magic Quadrant for its high productivity application. This ServiceNow application successfully bridges the gap between technology and manual processes by connecting the dots using ML algorithms.

Machine Learning in Hedge Funds:

Machine learning algorithms have always been used in the finance industry for consumer services like fraud investigation and credit checking. However, with the access to more computing power and sophisticated tools, the financial sector is using machine learning in all kinds of applications, in asset management, loan approval, and risk assessments. The sector greatly relies on sentiment analysis which calculates the impact of social media and news trends on commodities prices. Through sentiment analysis, machine learning algorithms are used in decision making in hedge fund trading by replicating the human response to current affairs. This component in AI is not dependent on human interaction and instead uses logic based on probability to interpret and analyses the daily market data and social media. It makes a decision based on this data and then decides on the best course of action that can be taken. Machine learning algorithms are hence adding value to hedge fund market sectors and are transforming the way the economy trades.

Machine Learning for Precision Medicine Method:

Machine learning is bringing about a new wave of change in the healthcare and medicine sector through its approach towards precision medicine methods. Precision medicine involves wearables which constantly stream biometric data, which are then worked upon by algorithms and molecular tools. This approach enables quick identification and interpretation of symptoms, thus changing the way modern medicine works. The precision medicine system works on the development of precise and efficient machine learning tools that study the collected data to decide on the best treatment option available for the patient. This system will also prove efficient because of its ability to overcome language and cultural barriers, thus making it extremely beneficial for the medical profession. Since the system is based on ML algorithms devoid of human interactions, the system will also mitigate the possibility of incorrect diagnoses and treatment plans.
The ability of ML algorithms to capture and process data in real-time with minimum time lag offers tremendous benefits to both companies and consumers. Machine learning tools, therefore, are not only set to bring about a change in the tech industry, but they are also set to improve the way we live our lives.

Posted by Vivek Kumar

Vivek Kumar

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