Hierarchical learning or deep structured learning is a subfield of machine learning. It can be in a supervised, semi-supervised or unsupervised manner. Deep learning has gained momentum in the last few years and was used to solve numerous problems which ranged from natural language processing to computer vision. Currently, deep learning is used heavily to build intelligent systems with the purpose of assisting humans in several activities. Problems which seemed unsolvable can now be easily solved with the help of deep learning. Deep learning is so named because of the numerous layers which the data has to go through for processing.
Over the last few years, several major breakthroughs in data science was a result of deep learning. Moreover, deep planning applications are slated to see a rise in demand in the future because of their ability to increase the cognitive skills of Artificial Intelligence. Several companies are trying to develop deep learning applications to be used in various feels such as in the military, cybersecurity, customer service, data analysis, robotics, and many others.
Areas of Deep Learning
Deep learning is a rapidly developing concept which helps the Artificial Intelligence to increase its accuracy while performing tasks that require cognitive abilities. Some of the areas where Deep Learning is used are:
- Pattern Recognition: Deep learning is being used to learn the correlation between sound and lip movement to give sound to silent movies. It is also used to restore the colors into black and white pictures. Apart from these, deep learning also helps in pixel restoration in pictures as well as estimating the human posture in real time. Moreover, it can describe photos to create a caption as well as analyze human behavior in real time and enables real-time translation as well.
- Gaming: Deep learning can not only play games, but it can also outperform even the professional players. Interestingly, it was not explicitly designed to play games. Rather, it was simply given the full control of the keyboard while playing. It started by performing poorly with no apparent game plan. But it taught itself the rules of the games within a few hours of playing and continued to master it, even finding shortcuts or loopholes in the games within only a few hours.
- Robotic Learning: Robotic learning is a combination of robotics and machine learning, and thus it uses deep learning, being a subset of machine learning is used to adapt cognitive abilities to the robots. Several robots use this to react to certain situations as well as carry out tasks with minimal to no human assistance. Deep learning is also being used in developing self-driving cars in order to analyze the surroundings and the traffic and to predict its movements.
- Sound Engineering: Deep learning has also learned the ability to generate human-like voices. Though it is not perfect, it has done so completely without any human input. It is a step towards computers generating authentic human voices as it keeps improving the quality of the voice. Moreover, deep learning is also creating original musical pieces by studying the musical works, learning patterns and statistics. It can also be used to restore sound to silent videos by analyzing the visible clues in the video, such as the actions or lip movements of the people speaking, the quality of materials used to produce the sound, such as wood on wood, metal or metal or other such clues.
- Art: Deep learning has also learned how to create art as well as transfer the painting styles from famous paintings into other pictures or pieces of art. It can even create literary pieces similar to classical authors such as Shakespeare or write articles or papers on subjects such as English, Math, recent events, including even computer codes. Not only this, deep learning can mimic human handwritings and use it to write in a human-like style.
- Predictions and Deep Dreaming: Deep learning can recognize patterns and predict the outcomes. It is used while predicting the traffic, as mentioned earlier, or even in predicting the election results. Deep learning is also evolving to predict earthquakes. Another important and interesting aspect of deep learning is Deep Dreaming. It is when the computer is allowed to enhance an image and it sort of hallucinates on top of it, thus giving the image a dreamlike quality. Which is where it got the name from. Deep learning can also write Python code as well as other deep learning codes and it enables the AI to create anti-fraudulent codes as well as codes to try to get past the anti-fraudulent codes. While the former has seen tremendous success, the latter has rarely been successful.
In Conclusion
The ability of deep learning to recognize patterns and learn from them is what makes it more adaptable to various situations. It is even helping people to solve problems which were not thought to be possible without human input. Through deep learning, a new generation of Artificial Intelligence is coming into being that has the ability to make decisions on its own.
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