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July 30th, 2018

Beast in Making: Machine Learning


“A breakthrough in machine learning would be worth ten Microsofts.”- Bill Gates

Walking-n-Talking machines, what once seemed to be a part of science-fiction movies only, has turned into a reality now. From voice assistants to self-driving cars to effective web search, and a vastly improved understanding of the human genome, machine learning has demonstrated unprecedented growth since it’s inception and is still revamping the way humans interact with machines. It is not only making digital services better but also becoming an integral part of human life nowadays. However, as the forecasts from some of the legends from the IT industry suggest, it’s still in making to achieve a breakthrough or maybe the highest notch in this revolution but going by the bounds of progress in machine learning it’s clear that it will permeate every sphere of human life.


Being one of the pervasive and the hottest technologies in the market today, machine learning is a method of data analysis that automates analytical model building. It is a subset of Artificial Intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It was in 1959 when Prof. Arthur Lee Samuel first coined the term ‘Machine Learning’. It was born out as an ambitious subdivision of AI, resulting from the combination of computer sciences and neurosciences, and requires the development of an algorithm to allow an application to learn from past experiences and predict outcomes in near future. Many researchers also think it is the best way to make progress towards human-level AI. Almost every IT organization is working to develop their own algorithm to better/improve the quality of service or product.

The infancy of Machine Learning:

“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” – Dave Waters.

In spite of the fact that ML is seen to be dominating the IT industry, a lot more is yet to uncover. Although the technologies are continuously obtaining new acumens and experience and spot trends faster, yet it’s difficult to predict just how clever will be the machines of the future. Despite the learning potential of ML, it is still in its infancy and cannot be left unattended. From over-optimized GPS sending unwitting travelers into scarcely existent filthy paths inside Death Valley to Microsoft’s AI Twitter bot performing miscreant after Twitter pranksters filled its brain with hate speech to the Facebook AI program which later shut down; the mere reason behind these flaws is that they haven’t mastered situation-based reasoning yet, which might be accomplished in further stages of ML’s lifecycle.

Parenting through algorithms

A prevalent analogy for the way machines learn is the same as how a child learns: Both are not told any rules for how to recognize an image or walk, but by drawing information from previous patterns and mapping connections, that follow necessarily articulation. But both need supervision. Unlike humans who use common sense skills, algorithms don’t reflect upon past experiences to course correct. They rely entirely on the data presented to them using human feedback. Thus, organizations should ‘parent’ algorithms that operate in the most significant segments of their business.

So, keeping up with the same line, Think Future Technologies Pvt. Ltd. is working on improving the quality of machine learning algorithms for various types of applications. The agenda is to let applications learn automatically and respond accordingly without having a human’s interference. Following are the four methods developed to categorize the process:

Supervised Machine Learning Algorithms

The method is to frame an algorithm that will help an application learn from past data to new data and predict future events in advance. At the beginning of an analysis, we use the known training dataset that helps to make predictions about the output value that will be produced by learning algorithms. These algorithms compare the output with the correct one and find an error to modify the system for better prediction in the coming future.

Unsupervised Machine Learning Algorithms

These algorithms are used for the non-classified and unlabeled information. The Unsupervised learning algorithms study the systems to assume a function to find the hidden structure in the unlabeled data.

Semi-Supervised/Hybrid Machine Learning Algorithms

Falls somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

Reinforcement Machine Learning Algorithms

It is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Types of machine learning algorithms

Think future Technologies is practicing Machine learning in many types of application and have developed a decent amount of machine learning algorithms. As there are limitless uses of machine learning, Think future technologies keep researching for new optimized and effective algorithms. Here are some of the most common models used are:

Correlation Model

It is a class of machine learning algorithms, which involves identifying a correlation, generally between two variables, and then using it to make predictions about future data points.

Decision trees

These imitations examine certain actions and try to reach the desired conclusion through the best pathway.

K-means clustering

This model gang up a specific number of data into a set of groups having similar characteristics.

Neural network

These machine learning miniature uses a gigantic amount of instructed data to analyze correlations between all possible fluctuations to determine to process the incoming variables in the upcoming future.

Reinforcement learning

It is an area of deep learning, involving models iterating over many attempts to complete a process. It discovers the algorithm through trial and error, i.e. favorable outcomes are rewarded and undesired outcomes are penalized until the algorithm learns the optimal process.

The phrase of this decade, “computers are creating their own language” strikes fear into the hearts of technophobes around the globe. Science-fiction movies have always made citizens wary that the future is a bleak and terrifying dystopia ruled by murderous sentient robots, but the present scenario affirms a different narrative altogether. It’s there to help and work in tandem with the human race not to overpower.

“Men and machines will never be able to overpower their creators.”– Anonymous

Human oversight will always be important in machine learning because computers are not skillful enough to determine the context of a situation. Seldom, undeniably it produces some of the most trash-riddled jibber-jabber resulting in egregious malfunctioning, keeping the predictions related to machine learning dominating the world, at a halt. Thus, in order to evolve and advance, we need to deliberately incorporate human experts from every academic field to train the machines and refine the processes.

At a time when new technologies emerge in the blink of an eye, it’s easy to get lost in the gigantic maze of information and rising concepts. It will be interesting to watch machine learning expanding the horizons.

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