The term Machine learning may be extremely high-end but you will be surprised to know that this technology is used for industrial purposes as well as daily activities. Machine learning is the subsidiary of Artificial Intelligence that uses datasets and some data analytics techniques to teach computers what comes naturally to humans. In short, Machine learning allows the computers to learn from the experience, where we provide them with experience in the form of huge datasets.

How Machine Learning fits in the CRM?

A CRM (Customer Relationship Management) software comprises an abundance of customer-related data and Machine learning collects all the data and processes it to derive something valuable. The application that leverages the capabilities of Machine learning not only runs on predictive analytics but also makes adjustments to the new changes. The algorithm that makes the program run gives rise to some new characteristics that were originally not there.

For example, Salesforce AI-powered Digital Assistant aka Einstein is capable of automatically reading and extracting data from emails. All of this is possible thanks to Machine learning. In addition to this, Einstein can also make the prediction by analyzing the customer behavior and patterns from the past.

Big Data empowering Machine Learning

The capabilities of machine learning have seen a great boost after the emergence of big data especially in data-rich fields like CRM, Banking, Pharma, etc. Let’s have a look at some of the fastest-growing applications.

• Finance: Credit scoring and algorithm trading.
• Biometric and image processing: Machine learning has made face recognition, motion & object detection, image recognition, etc a lot more accurate.
• Computational Biology: Technique and procedures like DNA sequencing and drug discovery are relatively easier.
• Natural Language Processing: Here, we are talking about both voice recognition as well as optical character recognition (OCR). Some of the most common examples that we use in daily life are Google Assistant, Amazon’s Alexa, and Apple’s Siri. All three are getting smarter by utilizing the information we feed them regularly.

The outcome of machine learning is directly proportional to the amount of data we feed to its algorithm.

Problems with Machine Learning’s Datasets

Datasets are like fuel to an AI-based platform. Datasets help these platforms in executing their tasks in the most efficient way whether it’s recognizing the coat in a bunch of images or predicting the price of the stock in a volatile market.

But just like other human-made things and as a matter of fact humans themselves, datasets are not flawless. Delve into the next section to get a better understanding of what we are trying to say here.

Recently, the University of California and Google performed combined research on datasets and published it with the name, “Reduced, Reused, and Recycled”. In this article, shared datasets inmachine learningare heavily borrowed, meaning that a dataset made for one task has been used for an entirely different one. This has led to the misalignment as well; the outcomes are also unlikely inaccurate in most of the scenarios.

A member of the Google Brain Team also commented on the same topic, said “SOTA-chasing is bad practice because there are too many confounding variables, SOTA usually doesn’t mean anything, and the goal of science should be to accumulate knowledge as opposed to results in specific toy benchmarks,” he further added, “There have been some initiatives to improve things, but looking for SOTA is a quick and easy way to review and evaluate papers. Things like these are embedded in culture and take time to change.”

What’s Next?

Reusing datasets is not such a huge problem that can’t be dealt with. Apart from that, Machine learning has a great scope from refining our songs playlists to space technology. We just need to steer it in the right direction.