Rising of machines !

 

Machine learning allows computers to solve tasks that have, until now, only been performed by individuals. Machine learning is driving an explosion in the capabilities of artificial intelligence, from driving cars to interpreting speech, helping software make sense of the chaotic and unpredictable real world.
But what exactly is machine learning and what is making it possible for the latest machine learning boom?

At a very high level, the process of teaching a computer system how to make precise predictions when fed data is machine learning. These predictions could address if a piece of fruit in a picture is a banana or an apple, seeing pedestrians crossing the road in front of a self-driving vehicle, whether the use of the word book in a sentence relates to a paperback or a hotel booking, whether an email is a spam, or correctly understanding speech enough to produce YouTube video captions.

TYPES OF MACHINE LEARNING
Machine learning is generally split into two main categories: supervised and unsupervised learning.

WHAT IS SUPERVISED LEARNING?
This approach basically teaches machines by example. During training for supervised learning, systems are exposed to large amounts of labeled data, for example, images of handwritten figures annotated to indicate which number they correspond to. Given sufficient examples, a supervised-learning system would learn to recognize the clusters of pixels and shapes associated with each number and eventually be able to recognize handwritten numbers, able to reliably distinguish between the numbers 9 and 4 or 6 and 8.  
However, training these systems typically requires huge amounts of labeled data, with some systems needing to be exposed to millions of examples to master a task. As a result, the datasets used to train these systems can be vast, with Google's Open Images Dataset having about nine million images.

WHAT IS UNSUPERVISED LEARNING?
In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories. An example might be Airbnb clustering together houses available to rent by neighborhood, or Google News grouping together stories on similar topics each day.
Unsupervised learning algorithms aren't designed to single out specific types of data, they simply look for data that can be grouped by similarities, or for anomalies that stand out.

WHAT IS REINFORCEMENT LEARNING?
A way to understand reinforcement learning is to think about how someone might learn to play an old-school computer game for the first time, when they aren't familiar with the rules or how to control the game. While they may be a complete novice, eventually, by looking at the relationship between the buttons they press, what happens on screen, and their in-game score, their performance will get better and better.
An example of reinforcement learning is Google DeepMind's Deep Q-network, which has beaten humans in a wide range of vintage video games. The system is fed pixels from each game and determines various information about the state of the game, such as the distance between objects on the screen. It then considers how the state of the game and the actions it performs in-game relate to the score it achieves.
Over the process of many cycles of playing the game, eventually, the system builds a model of which actions will maximize the score in which circumstance, for instance, in the case of the video game Breakout, where the paddle should be moved to in order to intercept the ball.

WHY IS MACHINE LEARNING SO SUCCESSFUL?

While machine learning is not a new technique, interest in the field has exploded in recent years. This resurgence follows a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. What's made these successes possible are primarily two factors; one is the vast quantities of images, speech, video, and text available to train machine-learning systems.
But even more important has been the advent of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be clustered together to form machine-learning powerhouses.
Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google, and Microsoft.
As the use of machine learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which these models can be trained.
These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. 

The machine has started ruling our present, Future would be certainly in the hand of machines only.

                                                    ****************