The Future of Machine Learning

Future of Machine Learning

Machine Learning:

Machine learning is one of the types of Artificial Intelligence (AI) that provides computers with the ability to learn without being programmed. It focuses on the development of computer programs that can change when exposed to new data.

The Future of Machine Learning:

The basic assumption in Machine Learning is that analytical solutions can be constructed with the reference of past Data Models. Machine learning supports that kind of data analysis that learnt from previous data models, patterns, trends and builds automated, algorithmic systems based on that reference.

As   Machine Learning depends up on pre-built algorithms for making data-driven predictions and analysis. It declares to replace data analytics and prediction tasks carried out by Human .In Machine Learning, algorithms have the ability to study and learn from the past data and then encourage the Human decision-making process by using predictive analysis and decision trees.

The Two Drivers of Machine Learning Solutions: Raw Data and Data Models.

Machine Learning has two sides. One side of it is the Raw Data and on the other side Data Models. Machine Learning authorizes data decision systems to learn from new data and adapt itself  to deliver “repeatable and reliable” results. The latest Technologies like Big Data and Internet of Things have given a new lead to the Traditional Machine Learning practice.

Some familiar applications of Machine Learning are as Follows:

  • The Self driving Google Car
  • Fraud Detection Systems
  • Customer feedback for businesses on twitter

Emerging Trends and Forecasts for Machine Learning:

Machine Learning supports Search Engines, Image recognition, Speech analysis, filtering Tools and Robotics. The Author of the article “Where Machine Learning Is Headed”, predicts that in future or the coming year, The Global Community will witness an Immense growth of digital assistants, smart apps and series use of Artificial Intelligence. Machine Learning escalates the mobile market and enter the territories of drones and self driving cars.  According to Mark Koh, Democratization of Artificial Intelligence/Machine Learning will continue.

Gartner’s Hype Cycle for Emerging Technologies:

As per Garntner, a number of new “Embryonic Technologies” will continue to increase in terms of their own market maturity.

PLTs or People Literate Technology:

They can convert text or voice message into retainable Intelligence that dominates the personal communication. By 2020, about 40 percent people will use PLTs as the first mode of technological Interaction.

The Brain-Computer Interface:

The Brain-Computer Interface claims to provide certain Brain patterns to the Computer for controlling a program or a device.

Bioacoustics:

These type of Technologies are front-runners in the world of Digital Humanism that connects Human with Digital workplaces and Businesses. Other than connected homes, self-driving cars and smart robots, Bioacoustics may also become important.

Some important observations about Machine Learning:

The following observations in Machine Learning define many of its current uses.

Machine Learning is a new tool for best Forecasting, In Businesses forecasting demand is becoming an insurmountable challenge, leading to incorrect results and the trends in the demand data fluctuate very much. The Inherent causes behind those fluctuations are complex that understanding demand variability is beyond the scope of most Business Leaders and Managers. With the Inherent ability to learn from current data, Machine Learning can help to overcome challenges facing businesses in their demand variations.

According to Gartner’s Hype Cycle Report of Emerging Technologies, Machine learning recently surfaced on Gartner’s chart but has managed to excel the expectations of its followers. In fact, Machine Learning has assumed the importance of Big Data.

The Demand-Supply gap in Machine Learning and Data Science skills will continue to rise till academic programs.

Most Businesses will tap into algorithmic models for their customer-facing and operational functions.

Machine Learning algorithms will act as major differentiation in Business.

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