Machine Learning an Emerging Trend
The modern era is heading towards implementing the artificial intelligence (AI) technologies in the business decision makings and leveraging their strength and computing power for the maximum benefits. Technologies such as advanced machine learning, deep learning, natural language processing, and business rules help software developers and testers understand the business requirements better and faster. These techniques have enabled businesses to think smarter and reap the benefits of first-mover advantage.
Machine learning was not autonomous or smart in past as it largely depended upon the patterns, formulas and algorithms fed by the developers. But modern machine learning relies on big data or cloud platforms, and hence it’s able to analyze the data and provide intelligent decisions.
For example, self-driving cars analyze the situations in their surroundings in the same way how pedestrians do while walking on the road. Based upon the situations, they take the decisions as where to slow down, where to speed up, and when to take turns – right or left. Websites like Amazon and Facebook use machine learning applications to study the trends of their visitors, such as what they are looking at, what type of content they like, what they are interested in, and how frequently they get connected with the community.
In the software industry, AI has brought a revolutionary effect for the developers and testers.
Usually developers debug the program with the help of a debugger, but AI helps them to interrogate the intelligence. i.e., probing questions where the developer’s intent was ambiguous and inviting developers for clarifying the queries.
Secondly, AI embeds state-of-the art into their software development which needs the shift in focus from algorithm development to data development. For instance, while developing an image transformation, the focus should shift from defining the algorithm to creating a good training set.
Machine learning works fantastically in optimizing a large array of parameters, for instance a neural architecture can have billions of tunable parameters. To work system properly, all these parameters must be well set. This task can be a nightmare for humans.
Machine learning has changed the classical way of coding. Developers now train the system and provide tips and advices to carry out the task to achieve various actions. The computer then filters the chunk of available data, analyze, and carry out the data processing.
Though machine learning is good at data analysis and other related tasks, but it fails to understand the complexity and context of the code in the same way as humans can do.
In some cases, machine learning algorithm needs a lot of training data and gathering such huge data becomes cumbersome. Furthermore, it’s not guaranteed that machine learning algorithms will work in all the situations. A clear understanding of the problem is required to implement the right machine language.
As nothing is perfect, even machine learning technologies face considerable challenges. The machine should be tested first to check for the correctness of its outputs before taking the output forward for further processing. Secondly, the data should be checked thoroughly that has been fed to the computer based upon the query asked from it to execute. Another challenge could be to convert the data into more useable form. The data scientists and analysts could be employed to sort these trends and patterns but there is a shortage of such highly knowledgeable professionals in the market.
Machine learning has enabled strong computing power that was not feasible for many companies before. It has leveraged Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure assist the developers build their optimized infrastructure at the least possible cost.
Cybersecurity is the greatest threats companies are facing these days. Machine learning techniques are the only techniques that can help companies to adapt to the security countermeasures. For example, IBM has trained its AI-based Watson in security protocols presented to customers. Another example could be of Amazon that acquired AI-based cyber-security company Harvest.ai to identify the important documents and intellectual property of a business in a way to prevent the data theft.
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