24/02/2017
Machine Learning: What it is and why it matters?
A recent news item went as follows: ‘Apple buys machine learning firm Perceptio Inc., a startup, in an attempt to bring advanced image-classifying artificial intelligence to smartphones by reducing data overhead which is typically required of conventional methods’. Another recent development was that MIT researchers were working on object recognition through flexible machine learning. Yet another tech enthusiast, David Auerbach, claims that, ‘Machine learning is starting to reshape how we live and it’s time we understood what it was and why it matters. So, what IS Machine learning and why has it got everybody talking? Read on to learn all you need to know!
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Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.
SAS, the North Carolina-based, American developer of analytics software comes with a definition on it: ‘Machine learning is a method of data analysis that automates analytical model building’. In other words, it allows computers to find insightful information without being programmed into where to look for a particular piece of information. This, it does by using algorithms that iteratively learn from data.
While the concept of machine learning has been around for a long time, (one might be reminded of the notable example here – Alan Turing’s famous Enigma Machine) the ability to automatically apply complex mathematical calculations to big data – iteratively and quickly – is gaining momentum only in recent times.
This emphasizes the iterative aspect of machine learning – the ability to independently adapt to new data.
While the concept of machine learning has been around for a long time, (one might be reminded of the notable example here – Alan Turing’s famous Enigma Machine) the ability to automatically apply complex mathematical calculations to big data – iteratively and quickly – is gaining momentum only in recent times.
This emphasizes the iterative aspect of machine learning – the ability to independently adapt to new data.
This is made possible as they learn from previous computations and make “pattern recognitions” in order to produce reliable results.
To understand better about the uses of machine learning, we might want to consider some of the instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines - like friend recommendations on Facebook, movie recommendations on Netflix and offers recommendations from Amazon – are all examples of applied machine learning.
All of this echoes the vitality of the role machine learning can play in today’s data-rich world. A recent report from Mckinsey Global has asserted this fact by claiming that machine learning will be the driving factor behind the big wave of innovation in the coming times. Obviously, if machines can aid in filtering useful pieces of information that help in major advancements, and if machines can learn through programmed algorithms, all by themselves, then the technology is bound to find implementation in a wide variety of industries.
Why Machine Learning?
With the constant evolution of the field, there has been a subsequent raise in the uses, demands, and importance of machine learning. The answer to the question as to why one has to adopt machine learning would be: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’ (Source: SAS).
Thus, if big data is gaining all the importance for the contributions it does, machine learning as a technology that helps analyze these large chunks of big data, easing the task of data scientists, in an automated process is equally gaining prominence and recognition. Machine learning has also changed the way data extraction, and interpretation is done by involving automatic sets of generic methods that have replaced traditional statistical techniques.
Uses Of Machine Learning
Some instances of machine learning applicability were mentioned previously. To understand the concept of machine learning better, let’s consider some more examples: web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these, are by-products of applying machine learning in the analysis of huge volumes of data.
So, how drastically is machine learning revolutionizing the data analysis avenue?
Traditionally, data analysis has always been characterized by trial-and-error, an approach that becomes impossible when data sets are large and heterogeneous. It is for the very same reason, that big data was criticized as being an overhyped technology. Availability of more data is directly proportional to the difficulty of coming up with predictive models that work accurately. Also, traditional statistical solutions are focused on static analysis that is limited to the analysis of samples that are frozen in time. This could obviously result in inaccurate and unreliable conclusions.
Machine learning comes as the solution to all this chaos. It proposes clever alternatives to analyzing huge volumes of data. It is a step forward from all of statistics, computer science and all other emerging applications in the industry. By developing fast and efficient algorithms and data-driven models for real-time processing of data, machine learning is able to produce accurate results and analysis.
