Three Applications for Deep Learning in Artificial Intelligence

Deep learning is a sector of machine learning used to train computers to learn by example – or receive, process, and filter complex information with all five senses to produce a final output (similar to a human brain). It achieves incredible accuracy rates, ensuring products can be implemented in the real world safely. From the virtual assistants in our phones to disease diagnostics, we can see deep learning everywhere. Here, we will cover the three most popular and progressive applications of deep learning.

Computer Vision

One exemplary application of deep learning is computer vision. Computer vision is a field under deep and machine learning that allows computers to gain a high-level understanding from digital images or videos. It is now the fastest growing sub-field and applied to a wide range of use cases.

For example, computer vision has been used by self-driving cars to evaluate and register their environment. Companies such as Tesla regularly implement computer vision to heighten the perception and control of their self-driving vehicles. These cars can gain the ability to “see” simply through a small computer chip. Image segmentation, a process by which a digital image taken through a camera lens is partitioned, allows cars to view their surroundings. Partitioning breaks images up into pieces they recognize to avoid crashing in real time.

Natural Language Processing (NLP)

Natural language processing, otherwise known as NLP, is another popular segment of deep learning. NLP merges artificial intelligence with human language. Because of the nuances and intricacies of language, NLP is seen as one of the most complex and difficult deep learning algorithms types to create. For example, one word can take on several meanings in some languages, and NLP needs to be designed so that it recognizes the surrounding context of that word and associates it with the correct meaning.

NLP is implemented using three main tactics: Part of Speech (PoS), parse trees, and semantics. In short, PoS defines the functions of individual words. Meanwhile, parse trees are used to determine the syntax of sentences (differentiating between verbs, nouns, adjectives, etc.). With semantics, the computer learns to read through and understand context (previous sentences) to deduce the appropriate meaning for a word.

NLP has been used to create YouTube’s auto-captioning system or Apple’s Siri. Still, it is much less common when compared to other fields such as computer vision. However, NLP remains a valuable application of deep learning.

Audio Signal Processing (ASP)

Audio signal processing has been used to create voice search and voice activated programs. ASP often works hand in hand with NLP. ASP is another rapidly growing field of deep learning.

Audio recognition is a large aspect of ASP, as it uses many of the same programming techniques to create. We can see ASP around us in the form of automatic speech recognition when we get a voice message which gets automatically transcribed into a script by our phones.

What’s next?

ASP, NLP, and Computer Vision are extremely lucrative and fast-growing aspects of deep learning.

  • Learn more about deep learning and what it is
  • Read the ultimate list of use cases for computer vision
  • Find out how to scale computer vision using Viso Suite

 

Share on facebook
Share on twitter
Share on linkedin
Share on whatsapp
Share on email
Related Articles