Teaching Machines to Learn Language and Think More Like a Human

Learning language is the task of syntactic and semantic parsers in computing. These systems are trained on sentences annotated by humans that define the structure and meaning behind words. But gleaning the annotation data can be time-consuming and complex for less common languages. For decades, people have been making machines to the human level to harness the power of computers.

However, to get information into and out of voiceless machines, human beings have relied upon a small set of expert computer programmers, adapted themselves to emerging user interfaces, learned how to consider intricate visuals and planted themselves at workstations.

It is a fact that computers outperform humans in many functions. They are faster, cannot be distracted, without taking rest, and are perfect at crunching down the series of numbers. But do they know how a cat looks like? how to drive a car? Or to play a strategy game? Machine Learning technique here comes to rescue by teaching them to use or follow algorithms guided by data.

With machine learning systems, computers can learn to distinguish speech, objects, and faces. Unlike programs that follow manually created guides for specific tasks, machine learning provides the program or system the opportunity to recognize templates and make predictions. The companies that deliver services like voice, face, object recognition, text-to-speech or speech-to-text, translation, and other tasks leverage various pre-trained application programming interfaces (APIs) that add intelligence to the applications and services.

Since machine learning technologies have been evolving for quite a time, most of the systems covering a more or less standard set of functions are already built. There is no need to create new models and train systems. Several tech giants like Amazon, Google, Azure offer such APIs and services.

A set of Amazon vision services, for instance, assists businesses in easily add the visual search and image classification to the applications. With them, solutions can detect and evaluate objects, scenes, actives, and faces. Additionally, the company offers libraries with tones of images and pre-trained models that already know how to work. So, developers only need to know how to integrate it. Thus, here they require training that Amazon provides through running educational workshops, guiding step-by-step through the process of developing machine learning-based solutions.

Today, lots of tasks are getting delivered to intelligent machines and most of those machines are created to act one specific automated task. Even though machines are faster, more accurate at doing work, there are still things that are out-of-the-way for machines such as creativity, feeling and emotions, and common sense to solve problems.