Real World Applications of Deep Learning

A subsidiary that stemmed from artificial intelligence, Deep Learning, despite being a newer technological development is quickly proving its capabilities in improving and enhancing various fields. It is more advanced than Machine Learning in the sense that machine learning is aided by algorithms that are man-made while deep learning makes use of its artificial neural network, which essentially refers to an artificial organ that is similar to our human brain. What this means is that the decisions or processes they implement or carry out are considered and analyzed just like a human brain would.

It has the ability to sift through enormous amounts of data that is generated daily by humans with ease and without the requirement of human programmers by making use of it’s artificial neural network to identify patterns in datasets enabling the solving of complex problems or in discovering trend patterns.

With this nearly human intelligence at our perusal, it is understandable that we would find applications for its functioning in various fields as such a technology can truly uplift any sector it touches. Let us therefore check out some real world applications where Deep Learning has made an impact.

1. Computer Vision

Ever wondered how your Google photos seem to know how to group your seemingly random photos together? Be it based on a person or a category? Well, this is where computer vision comes into play. Deep Learning surpasses all the traditional image processing with a greater level of accuracy in visual recognition and is prone to lesser errors when compared to the human error percentage. Deep learning enhanced computer vision is more efficient in object or even face recognition programs. It can even be used to identify artists based on their painting styles, be it Van Gogh or Picasso. It also aids in restoration and even image segmentation. Deep learning based computer vision was also used over machine learning based computer vision to try and recreate regular images in the styles opted by other artists which has recently grown to act as filters or even apps.

2. Text Analysis

Millions of plans, ideas, thoughts, and more are shared via texts, with it being a major mode of communication. Therefore, analyzing texting patterns or contents can help in sentiment analysis and even language translation and recommendations. Ever wonder how your smartphone’s keypad just knows to suggest the right words as you’re typing it out? Well this is yet another application of deep learning as it enables the accurate prediction of words, which at first may appear random, however with frequent usage, the suggestions become increasingly accurate aiding you in texting faster. Automatic language translation can also be aided by deep learning and natural language processing which is applicable for both texts and images. Be it labels on objects in any language to texts or captions that you may not understand, deep learning can resolve it through automatic language translation.

3. Social Media

Another use for deep learning comes in social media where text based sentiment analysis can help in aggregation of data where companies can often rely on tweets, reviews, posts and stories to understand how their products are being received and act based on this feedback. It also helps in filtering out any untowardly negative content from news or social media. Major apps like Facebook and Instagram make use of deep learning technology to make content more personalized and curated according to the users preferences, which enables the presentation of accurate ads, recommended friends lists and objects according to your interests. Deep Learning is also used by Facebook in enabling facial recognition that makes tagging predictions possible.

4. Virtual Assistants

From Siri to Alexa and even Cortana, all these virtual assistants are assisted by deep learning to make them functional. Every interaction you have with your virtual assistant gives it a chance to glean more information about you making it extremely efficient in providing a human to human experience. You can ask Alexa to jot down the groceries and items you need to buy, make your to-do lists and even set reminders for a friend’s birthday or a very important client meeting! Virtual Assistants can assist so efficiently owing to their ability to learn more about the owners preferences, from their favorite genre of music to the places they love to dine out. It also makes online bookings for movies, doctors appointments and more easier and hassle free by making use of speech recognition, speech to text recognition which is also aided by natural language processing.

5. Healthcare

Be it in scientific research for discovering protein structures or in repurposing potential drug candidates for various diseases and even in early diagnosis of life threatening disease, deep learning has the immense ability to augment the services rendered by healthcare professionals to their patients and research. Deep learning can make analyzing patient histories easier even if hospitals vary, it can also make the sector’s workflow more fluid and secure, making relevant data available to professionals who need it. Deep learning can also play a huge role in analyzing and mitigating any possible cases of re-admissions ensuring the welfare of the patients and whilst bringing down additional expenses as well.

6. Speech Recognition

Deep learning is capable of avoiding the cumbersome process of speech extraction by directly dealing with raw data in the form of audio recordings that is generated everyday. It can be used for voice to text and even texts to voice, be it from reading a book aloud to proof reading your mails. While recognizing the subtle variations between similar sounds and words seems like an easy thing to comprehend for humans, it is actually an extremely complex process which relies on language efficiency and context clues and even the demeanor in which one says it. However, in the case of speech recognition systems, the model needs to have a good grasp on the context and language which in traditional speech models weren’t accurate enough when compared to deep learning enhances speech recognition.

7. Autonomous Vehicles

In self driven or autonomous vehicles, deep learning plays a crucial role in assimilating the data obtained by various sensor systems be it to detect objects or other vehicles near the car, identify traffic signals and potentially jammed routes. Deep learning in the case of autonomous vehicles enables the processing of real time data that can provide more information about any possible events or routes to be taken at any given location than the ones that have already been mapped out. Tesla’s autonomous cars are the perfect example for the amalgamation of automobiles and artificial technology or in this case, deep learning.

8. Entertainment

Major online movie platforms like Netflix and Amazon Prime are increasing their deep learning prerogative in order to provide customers with a more personalized user experience and recommendation of movies, series and other features according to their preferences. Content editing and content generation is yet another perk of deep learning since it makes use of an artificial neural network and can generate content based on vast resources they have unlike programmer based intelligences like machine learning. Deep Learning has made a great impact in the field of games as well enabling games to be played with AI coordinating their own moves, for example in the case of traditional Go game which due to its complexity couldn’t be comprehended by machine learning where with Google’s deep learning AlphaGo program, it actually defeated the strongest human Go player in the world.

9. AI Cybersecurity

Cybersecurity is yet another field where deep learning has served a noble purpose by both ramping up the defense systems available to flagging down any potential malware before it can affect the integrity of the system it’s protecting. Traditional mode of cybersecurity whilst being able to protect from major known threats, might still result in data breach or even loss of data and cannot identify minor differences that might make it a potential threat. This is where deep learning is beneficial as it addresses all the flaws put forth by the traditional method of machine learning employed in cybersecurity. Therefore in addition to determining the malicious nature of a file it can also identify the type of malware it is.

These are some real world applications that are offered by deep learning and enhanced by the application of it. As you can see from its application potential, it isn’t unrealistic to say that deep learning still has miles to go and heaps of beneficial changes to bring about!!