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Top innovative fields in the world of machine learning and artificial intelligence

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26 Dec 2016     Burgess Hayley

Machine learning and artificial intelligence have been claimed to be the domains of technology which the future of the world depends on. Technology giants like Google and Apple consider both the domains to be the future and are doing immense research in both fields. There have been some immense developments in the fields of artificial intelligence and machine learning, but still they remains highly unexplored with huge amount of potential for growth and innovations. We bring to you the most attractive and researched-in fields of machine learning and artificial intelligence which organizations are focusing on.


Cross environment abstraction
It has been a long standing aim of researchers to achieve true artificial intelligence which is comparable to human intelligence. Cross environment abstraction is essential to achieve that goal and allow application of information and data learned from one domain to another domain. As an example, can the knowledge of cooking pasta be interpolated and used to develop the knowledge of creating lasagna. Inherently, a single learning program or algorithm that can be used to learn and consequently act in varying domains which may or may not be related to each other. The major development in this field is being done be DeepMind organization where they have build a seminal DQN which learns to play different Atari games


Intuitive concept Understanding
Use of subsymbolic knowledge and reasoning in order to understand a system as compared to explicitly program or represent it , comes under the umbrella of intuitive concept understanding. Trying to understand symbols, words and other representations having meanings the humans use a cognitive process which is relational to the existing knowledge. Trying to implement this concept in machine learning to solve the symbol-grounding problem is a task important enough to be researched on. This process of machine learning is widely known as deep learning techniques and requires a large amount of training data to create good results.


Abstract thoughts creation
This may be a little tough to understand but abstract thoughts creation is one step ahead of understanding simple concepts and grasping information. The ability to amalgamate a number of ideas and concepts in order to create a new model of make a decision in a transient environment is the beginning of abstract thought creation. The fundamental ideas of abstract thoughts have been used in DeepMind’s neural turing machine and Facebook’s memory networks and the ideas used show immense promise for the future of artificial intelligence.

Vision’s dream
If we wish to achieve artificial cognitive processes comparable to human intelligence, we must not stop at only pattern recognition, understanding, and concept creation. AI and machine learning also strives to achieve the creation of visualization patterns and images which stem from the understood concepts by the machine. Basically, let your machine cook up imaginary visions and images for the future; which may or may not happen; just like we all do. Creating realistic images requires the use of a pyramid of adversarial networks which improves the quality of image creation. MIT and Microsoft Research have developed a deep convolution inverse graphic network in order to create meaningful transformations of an image.

Agile and dexterous motor capabilities
AI and machine learning is now to move into hardware mode just like the human’s cognitive capabilities govern their motor skills. In order to teach the machines with fine motor skills a number of machine learning concepts and techniques can be applied. A research team at UC Berkley has developed a deep reinforcement learning-based guided policy search technique which is being used in robots to screw caps to the bottle, remove nails from wood with a hammer and other every day actions. It undergoes an iterative learning procedure where it refines its technique after a few attempts.


Artificial intelligence and Machine learning are the tomorrow of tomorrow and even further! The path that the technological world is treading now is extremely clear and involves the use of immense cognitive capabilities and machines and artificial digital networks.


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