Defining Machine Learning in Today’s Enterprise Ecosystem

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Dec
09
2016
  • Author:
  • Sahana Rajan

Defining Machine Learning in Today’s Enterprise Ecosystem

The streets of engineering and technology world are trending with machine learning since the last decade. The giants of tech-land (Google, Facebook, Apple, Microsoft, IBM, Skype, Salesforce.com and Shell!) have also hugely invested in machine learning and artificial intelligence ventures. Introduced by Arthur Samuel in 1959, the term it is considered the capacity to learn without explicit programming. This implies that the machine should be able to gain insight into its operations, learn from the mistakes and improvise its program to perform better over time.

When the concept of data mining entered the landscape in 1990s, the idea of machine learning picked up weight again. In data mining, algorithms are used to scan for patterns within a series of information. This allows predictions driven by data and actionable insights. Engineers could use predictive analytics and data mining to set up complex algorithms.

 

Machine learning is becoming a tech-essential!

Machine learning has entered our devices in a big way today. Some of the innovative uses in our daily life are:

  • Remember all the times your phone requests you to free up space by analyzing the storage? This is automatically detected.
  • Self-driving cars are gradually becoming completely automatic with the use of machine learning.
  • Does your phone have face detection feature? With this, your phone will be able to click a photo when someone on the other side blinks or shows the palm. This is possible due to machine learning!
  • It also helps with the anti-virus and anti-spam tools that can detect spyware, adware and malicious software on the device.
  • The capacity of a phone to recognize faces is also made possible by machine learning. This is also used on Facebook where you tag acquaintances on photos.
  • Search suggestions and results are also enhanced by using machine learning.

 

What was new yesterday is the norm today!

Earlier, machine learning was a privilege- one of the perks you get when you go for the premium edition of technology. Today, fortunately, it is gradually becoming a staple for companies. They recognize the amount of innovation held within machine learning and are finding ways to put it to practice within their domains.

All the data which has been stored up for years has now found a platform for use. Humungous sum of data can be loaded through machines to reach upon insights and actionable policies.

Here is a gist of the different ways in which it has penetrated the different sectors. While the financial sector has come across analysis and regulation of risks along with customer segmentation through machine learning, healthcare and life sciences is endowed with superior diagnostic abilities from live patient data. Accompanied by proactive health management and disease recognition-risk classification, the healthcare sector is moving towards an era of personalized patient care. Travel and hospitality industry can utilize machine learning for aircraft scheduling, management of traffic patterns and congestion, dynamic pricing and resolution of customer complaints. With machine learning, the manufacturing domain can enhance telematics and improvise the propensity to buy along with better forecasting and predictive maintenance. The field of retail can dip into predictive inventory planning and marketing segmentation-targeting along with building of suggestions’ engines through machine learning.

 

What is in store for us?

Throughout the range of industries, machine learning is taking root in a big way. It is changing the ways in which business processes are carried out. The algorithms are gaining complexity to match the kind of experiences we have as users. This will allow companies to approximate the journeys of consumers on their platforms and personalize their treatment.

Since the algorithms can learn by feeding on continuous data, companies find in them a great potential for development without constant need for human interference. Whenever an error is encountered, the machine learns to explore corrections and employ them. This entire process takes only milliseconds, thus allowing the machine to be an efficient way for optimal decisions and predictions.

It creates the space to set up refined software systems without the need for human-induced work. In a much lesser time, the machine can learn of its operations through data and avoid a copious amount of time/energy spent on continuous code-writing and correcting of the system. Machine learning brings to life not only the age-old science-driven dreams but opens up the possibilities of realizing our 21st century sci-fi stories too!

 

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