This is the first time in history when the president of the United States of America, or probably for any head of state around the world,...
Friday, February 24, 2017
Technology Trend Series: Machine Learning
In the simplistic term, Machine Learning (ML) is all about making machine that can learn from surrounding and apply that learning in action. The more formal definition is from Tom Mitchell: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Machine Learning is now has reached to the point where we actually don’t even realize its presence unless explicitly called out. Google, Amazon, IBM, Apple, Microsoft, Netflix, Uber, Tesla, etc. have adopted Machine Learning successfully. Google uses ML for spam filtering, image recognition, translation, self-driving car. Amazon uses ML for Recommendation system, Drone delivery, etc. Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa and Google’s Google Home use ML to provide Virtual Private Assistance (VPA). IBM’s Watson provides assistance to doctors to diagnose patients. Uber and Tesla created Machine Learning based autonomous vehicles. Machine Learning is used in the stock market for High Frequency Trading and in the postal service for handwriting recognition.
The Machine that Learn is essentially a computer algorithm to best model the input to its output. Carefully prepared large training dataset is fed into the algorithm to find out the optimal model. Machine Learning can be Supervised Learning, Unsupervised Learning, Reinforcement learning, Artificial Neural Network, Deep Learning, and so on. Let’s take an example of how a self-driving car uses machine learning: car is driven by a human driver and through various sensors: steering wheel, accelerator, brake, camera, GPS, radar, Laser, etc. it captures the data and the behavior of the human driver and used as training data set and find the optimal model to map the observed data from all the sensors to driver’s action.Machine Learning is progressing faster and superseding its own legacy. The drivers for continued massive growth and adoption of Machine Learning are the growing surge in data volume and complexities that conventional engineering approaches are increasingly unable to handle. For example, the amount of data created in the last 2 years exceeds the data created in the entire human history. So the traditional brute force computation is no longer a viable option to process this ocean of data. Meanwhile, as predicted in the “Moore’s Law”, the exponential growth of computing power is unleashing the power of Deep Learning. So, the businesses have to be fully prepared to utilize the most out of it to stay competitive in this disruptive and emerging technology landscape.
Machine Learning is ubiquitous in commercial usage and has reached to “Adolescent”, according to Gartner’s Maturity Level. Enterprises that have not yet started adopting the technology should start training and hiring talents in Machine Learning and Data Science and building infrastructure. The precursor of successful Machine Learning adaptation is to be able to manage “Big Data” for training the machines with the right training dataset. Enterprises need to invest more in dedicated Center of Excellence (CoE) for the “Emerging and disruptive Technology” like Machine Learning and embrace the transformation into their core business. For individual computer professionals, this is the time when the focus on learning the concept of Machine Learning and Artificial Intelligence, be familiarize with development tools and technologies (Matlab, R, Apache Mahout, Python ML libraries, etc.), specially, around data and algorithms, and finally dust off the the math and statistics skills long sitting unused. Those days aren't far away when the Java and .NET programmers would be considered as legacy programmers like the way the COBOL programmers were being considered.