You hear the buzzwords “machine learning” (ML) and “artificial intelligence” (AI) in most technical conferences nowadays. Machine learning has been around for some time, and the official term was coined in 1959 by an American artificial intelligence (AI) pioneer named Arthur Samuel. The idea of a computer that could teach itself was far more efficient than coding manually by a programmer.
The internet came to birth in the late 80s and 90s, and this automation of self-programming computers truly came to the fore. Machine learning leads to better decisions based on high-value predictions. As we moved from the Information Technology age, into the age of Data-driven Technology, where actions and system behaviour need to adapt based on the data; we are flooded with data from text, audio, image and video everywhere, right from Social Media to enterprise software ecosystems. ML helps us automate information gathering, filtering, enable workflows and learns to adapt rules based on the data it reads.
Types of Machine Learning
1) Supervised Machine Learning
Supervised machine learning techniques are one of the most popular machine learning techniques in use today. It is called supervised learning because the algorithm learns from a set of base training datasets, much like a teacher imparting knowledge to his class. When the algorithm reaches an acceptable level of consistent performance, the learning automatically stops.
2) Unsupervised Machine Learning
Unsupervised learning is a less common form of ML, wherein one only has input data but no corresponding output variables. It is called unsupervised learning as unlike supervised learning, there are no correct answers provided by it. This kind of machine learning studies the underlying structure or distribution in data to learn more about it. Algorithms are automated to discover present and future structure in data.
Uses of Machine Learning
A simple use of ML that we have been using for long is spam filtering in our emails. Multinomial Naive Bayes Classifier is one such basic form of spam filter. With this machine learning technique, you define a list of words as “spam” and not spam, then you compare the frequencies of the words you have judged to be spam appearing in sentences, and the frequencies of the words judged as not spam, to make judgement about new sentences incoming through email.
Online advertisers also use similar ML techniques to push ads based on an individual user's online behaviour, enabled by ML. ML is particularly useful for automating repetitive tasks, rule-based activities and predictive analysis.
AI-Driven Enterprise Software
Machine learning and artificial intelligence has also made its way into enterprise software, through enterprise applications such as IFS ERP (Enterprise Resource Planning) software, IFS FSM (Field Service Management) software and Enterprise Management software like ServiceNow. These software help automate several work flows and decision-making points where manual intervention is required.
For example, a robust tool like ServiceNow or IFS FSM which deals with large volumes of incoming requests everyday can efficiently use ML. It can be used to automatically tune the routing behaviour depending upon the category and priority of the request, so that the alerts go to the right team for accurate resolution at the right time. Machine learning algorithms in these applications is a useful tool that allows automation of these manual tasks, thereby providing repeatability, standardization and reducing the number of manual errors.
By using these tools ML can become an effective tool in improving overall efficiency of an organization by taking care of mundane repeatable jobs and allow users to spend their effort on other productive activities.
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