Studies show that Machine learning reduces Global Trade’s Language Barrier

In the recent times, the idea of machine learning and Artificial Intelligence has skyrocketed in various domains all around the world. This has greatly offered the hope of a much greater efficacy in businesses. Simultaneously, different researchers from around the world have not succeeded in finding any empirical evidence which supports the promised strides in economic activities and productivity in labor.

Well, it seems the tables have turned. A research study by some erudite scholars at the Washington University in St. Louis claims that there is a direct link artificial intelligence’s use in language translation and the increment in Global Trade taking place all around the world. This particular case study has collected and analyzed the data and information from site such as eBay, which are considered to be online e-commerce outlets. Many around the world consider this as a sign that machine learning and Artificial intelligence are working brilliantly in the contemporary times.

According to Meng liu, who is a visiting professor at the Washington University, “There is plenty of anecdotal evidence that AI has exceeded humans in many areas, but there was not much causal-empirical evidence,” She say that there is a correlation between artificial intelligence and the economic growth in the world.

For instance, since the 2000s, the overall productivity growth rates have faced stagnation. Another research by the University of Chicago and MIT states that until the news technologies in the world such as Artificial Intelligence and Machine learning came on the scenes, the overall economic growth was very much stagnant and this introduction of modern technologies has helped increase and fuel economic growth.

In the end, we can say that the introduction of such technologies, especially technologies such as machine translation of eBay, are known for the provision of a clean experiment where the impacts can be measured. However, the effects of Artificial Intelligence are to be witnessed in almost all the sectors of the economy. Hence,  according to the authors “when new systems start working, they will provide new opportunities to assess the economic impact of AI via natural experiments such as the one examined in this paper.”

The Reasons Behind Machine Learning’s Success

In the recent times, it is being observed that Artificial Intelligence is playing a major part in our lives, either personally or professionally. People all around the world are already implementing the ideas of Artificial intelligence in the various algorithms used in businesses and corporations. One of the major subsets of Artificial Intelligence is the promising field of machine learning.

Fundamentally, the Machine learning is a thorough study of different statistical models and algorithms which are used by computer systems to carry out different tasks without any explicit instructions being given. This system of Artificial intelligence uses the paradigm of pattern inference and recognition to learn over time how can efficacy and efficiency be achieved in carrying out various tasks. The various reasons for its success are given below:

Iterative process of learning

The platform of machine is successful because it is promising and powerful as it greatly allows the computer programs to use their own errors to learn from them, and then develop and improve slowly and gradually with little human intervention. The iterative process is considered to be the backbone which is installed in a system. This enables the system to correct its errors, speed up the result search, and correct the mistakes as well.

Speedy, Better and personalized

The machine learning subset of Artificial Intelligence is also successful because of its amazing traits. It is more personalized, better, and faster than anything else. A large number of corporations from all around the world have installed it and have gone live by using a current software that has a machine learning base. As a consequence, these corporations have experienced positive results. Artificial Intelligence, especially the Machine Learning, has assisted a plethora of corporations to make their programs work better, faster, and also to deliver more and more personalized results. This is also because AI reacts to different users and the preferences of those users are learned and noted down. Although its still early, but the ultimate goal is to develop customized programs for all individual corporations according to their needs.

Models of Machine learning

In order to automate the standards of machine learning workflow, the various IT professionals around the world are endeavoring to develop new techniques. If we are to move towards a normal developmental process then it can be a really time-consuming process which would also require a plethora of programming knowledge, which is of a technical nature in order to reach for cloud users and the normal computers.

Soon, the individuals or corporations wishing to create models of machine learning would be able to use simple yet useful tools for the purpose of designing their own algorithms of machine learning, which will enable them to mine the relevant information which they require.


The Must-Have Skills to Excel in Machine Learning

Machine learning is an intricate field in the industry and working on it requires a strong skill set which incorporates knowledge, stats, and practical skills in engineering. For instance, if a user faces a problem in finding products and services, then it is your job to understand the problem on the website and apply statistical modeling in order to solve the particular problem.

Hence, we can say that the success in machine learning depends upon the acquisition of a strong skill set. If you have a major in Computer Sciences, the overall skills required would be to fathom the statistical fundamentals, gain a know how of the algorithms of machine learning, and lastly to establish proficiency in working with data. This includes the data querying from a plethora of sources, its manipulation, and reasoning.

If you have a background of a statistical or quantitative nature, your priority must be your programming skills. It is important for you to become proficient in at least one DS language such as R or Python as it will greatly assist in the execution on any modeling tasks in Machine learning. It is extremely beneficial for you to adopt and understand the effective software engineering practices such as handling of data flows through data systems and automating the workflows which are commonly used such as libraries.

And lastly, the most important and crucial skill for machine learning in the market is to apply the skills in order to solve the business problems by casting the business problem as a machine learning problem. in this way, the machine learning will be effective and productive. For example, machine learning can be utilized to improve the overall delivery-time prediction which have a massive impact on the overall client experience.

Top 3 Data Mining Techniques

Different Data Mining techniques are used for different problems in corporations and hence, we can say that the overall knowledge of the business problem helps in determining the technique for data mining that is likely to generate the best results for the corporation.

In the contemporary world of globalization and digitization, we are largely surrounded by mammoth sets of Big Data which is expected to increase 40% every year for the next decade. The irony of the matter is that despite of such a large magnitude of data availability, the world still starves for beneficial knowledge. Why is this the case? It is important to understand that the data is buried inside large chunks of big data and can only be extracted through significant techniques.

In this small piece of literature, we shall provide you with the 3 major techniques that are crucial for data mining.


In order to retrieve relevant, productive, and beneficial information about data or Meta data, this analysis comes very handy. The classification analysis is used for the classification of different data into classes or segments. This process is similar to clustering as the process of segmentation is carried out in both the techniques, but the uniqueness of Classification analysis is that, unlike clustering, the analysts would be having the overall knowledge and understanding of the different classes or segments. Hence, we can say that in classification analysis different algorithms are applied in order to decide how the classification of new data should be done. For example, the Microsoft Outlook uses a particular algorithm to segment an email as Spam or Legitimate email.


The Regression Analysis, in the statistical terms, refers to the process of analyzing as well as identifying the connection among different variables. This analysis is important because it helps you to gain a sound understanding of the characteristic value of the changes in dependent variable, if the independent variable changes at any time.


The clustering analysis is another important technique that uses clusters comprising of objects which are similar in nature. This means that with in the same groups the present objects are similar to each other and different from the ones in the other groups or the so-called clusters. Therefore, we can say that the clustering analysis is the systemic process through which we can explore clusters in the Big Data, in a manner that the overall degree of association between the two sets of data is the highest if they are from the same group and lowest otherwise. For instance, this Clustering Analysis is used in Customer Profiling.