Top 10 Applications of Deep Learning - Great Learning

Top 10 Applications of Deep Learning

Think of a world with no road accidents or cases of road rage. Think of a world where every surgery is successful without causing the loss of human life because of surgical errors. Think of a world where no child is underprivileged and even those with mental or physical limitations can enjoy the same quality of life as does the rest of humanity. If these are too hard to fathom, think of a world where you could just segregate your old images (the ones without much metadata) according to your own parameters (events, special days, locations, faces, or group of people). Deep Learning applications may seem disillusioning to a normal human being, but those with the privilege of knowing the machine learning world understand the dent that deep learning is making globally by exploring and resolving human problems in every domain. Here are a bunch of things already done or under process, thanks to the wonders of deep learning:

  1. Self-Driving Cars – Deep Learning is the force that is bringing autonomous driving to life. A million sets of data are fed to a system to build a model, to train the machines to learn, and then test the results in a safe environment. The Uber AI Labs at Pittsburg is not only working on making driverless cars humdrum but also integrating several smart features such as food delivery options with the use of driverless cars. The major concern for autonomous car developers is handling unprecedented scenarios. A regular cycle of testing and implementation typical to deep learning algorithms is ensuring safe driving with more and more exposure to millions of scenarios. Data from cameras, sensors, geo-mapping is helping create succinct and sophisticated models to navigate through traffic, identify paths, signage, pedestrian-only routes, and real-time elements like traffic volume and road blockages. According to Forbes, MIT is developing a new system that will allow autonomous cars to navigate without a map as 3-D mapping is still limited to prime areas in the world and not as effective in avoiding mishaps. CSAIL graduate student Teddy Ort said, “The reason this kind of ‘map-less’ approach hasn’t really been done before is because it is generally much harder to reach the same accuracy and reliability as with detailed maps. A system like this that can navigate just with onboard sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped.”
  2. News Aggregation and Fraud News Detection – There is now a way to filter out all the bad and ugly news from your news feed. Extensive use of deep learning in news aggregation is bolstering efforts to customize news as per readers. While this may not seem new, newer levels of sophistication to define reader personas are being met to filter out news as per geographical, social, economical parameters along with the individual preferences of a reader. Fraud news detection, on the other hand, is an important asset in today’s world where the internet has become the primary source of all genuine and fake information. It becomes extremely hard to distinguish fake news as bots replicate it across channels automatically. The Cambridge Analytica is a classic example of how fake news, personal information, and statistics can influence reader perception (Bhartiya Janta Party vs Indian National Congress), elections (Read Donald Trump Digital Campaigns), and exploit personal data (Facebook data for approximately 87 million people was compromised). Deep Learning helps develop classifiers that can detect fake or biased news and remove it from your feed and warn you of possible privacy breaches. Training and validating a deep learning neural network for news detection is really hard as the data is plagued with opinions and no one party can ever decide if the news is neutral or biased.
  3. Natural Language Processing (NLP) – Understanding the complexities associated with language whether it is syntax, semantics, tonal nuances, expressions, or even sarcasm, is one of the hardest tasks for humans to learn. Constant training since birth and exposure to different social settings help humans develop appropriate responses and a personalized form of expression to every scenario. NLP through Deep Learning is trying to achieve the same thing by training machines to catch linguistic nuances and frame appropriate responses. Document summarization is widely being used and tested in the Legal sphere making paralegals obsolete. Answering questions, language modeling, classifying text, twitter analysis, or sentiment analysis at a broader level are all subsets of natural language processing where deep learning is gaining momentum. Earlier logistic regression or SVM were used to build time-consuming complex models but now distributed representations, convolutional neural networks, recurrent and recursive neural networks, reinforcement learning, and memory augmenting strategies are helping achieve greater maturity in NLP. Distributed representations are particularly effective in producing linear semantic relationships used to build phrases and sentences and capturing local word semantics with word embedding (word embedding entails the meaning of a word being defined in the context of its neighboring words).
  4. Virtual Assistants – The most popular application of deep learning is virtual assistants ranging from Alexa to Siri to Google Assistant. Each interaction with these assistants provides them an opportunity to learn more about your voice and accent, thereby providing you a secondary human interaction experience. Virtual assistants use deep learning to know more about their subjects ranging from your dine-out preferences to your most visited spots or your favorite songs. They learn to understand your commands by evaluating natural human language to execute them. Another capability virtual assistants are endowed with is to translate your speech to text, make notes for you, and book appointments. Virtual assistants are literally at your beck-and-call as they can do everything from running errands to auto-responding to your specific calls to coordinating tasks between you and your team members. With deep learning applications such as text generation and document summarizations, virtual assistants can assist you in creating or sending appropriate email copy as well.
  5. Entertainment (VEVO, Netflix, Film Making, Sports Highlights, etc.) – Wimbledon 2018 used IBM Watson to analyse player emotions and expressions through hundreds of hours of footage to auto-generate highlights for telecast. This saved them a ton of effort and cost. Thanks to Deep Learning, they were able to factor in audience response and match or player popularity to come up with a more accurate model (otherwise it would just have highlights of the most expressive or aggressive players). Netflix and Amazon are enhancing their deep learning capabilities to provide a personalized experience to its viewers by creating their personas factoring in show preferences, time of access, history, etc. to recommend shows that are of liking to a particular viewer. VEVO has been using deep learning to create the next generation of data services for not only personalized experiences for its users and subscribers, but also artists, companies, record labels, and internal business groups to generate insights based on performance and popularity. Deep video analysis can save hours of manual effort required for audio/video sync and its testing, transcriptions, and tagging. Content editing and auto-content creation are now a reality thanks to Deep Learning and its contribution to face and pattern recognition. Deep Learning AI is revolutionizing the filmmaking process as cameras learn to study human body language to imbibe in virtual characters.
  6. Visual Recognition – Imagine yourself going through a plethora of old images taking you down the nostalgia lane. You decide to get a few of them framed but first, you would like to sort them out. Putting in manual effort was the only way to accomplish this in the absence of metadata. The maximum you could do was sort them out based on dates but downloaded images lack that metadata sometimes. In comes, Deep Learning and now images can be sorted based on locations detected in photographs, faces, a combination of people, or according to events, dates, etc. Searching for a particular photo from a library (let’s say a dataset as large as Google’s picture library) requires state-of-the-art visual recognition systems consisting several layers from basic to advanced to recognize elements. Large-scale image Visual recognition through deep neural networks is boosting growth in this segment of digital media management by using convolutional neural networks, Tensorflow, and Python extensively.
  7. Fraud Detection – Another domain benefitting from Deep Learning is the banking and financial sector that is plagued with the task of fraud detection with money transactions going digital. Autoencoders in Keras and Tensorflow are being developed to detect credit card frauds saving billions of dollars of cost in recovery and insurance for financial institutions. Fraud prevention and detection are done based on identifying patterns in customer transactions and credit scores, identifying anomalous behavior and outliers. Classification and regression machine learning techniques and neural networks are used for fraud detection. While machine learning is mostly used for highlighting cases of fraud requiring human deliberation, deep learning is trying to minimize these efforts by scaling efforts.
  8. Healthcare – According to NVIDIA, “From medical imaging to analyzing genomes to discovering new drugs, the entire healthcare industry is in a state of transformation and  GPU computing is at the heart. GPU-accelerated applications and systems are delivering new efficiencies and possibilities, empowering physicians, clinicians, and researchers passionate about improving the lives of others to do their best work.” Helping early, accurate and speedy diagnosis of life-threatening diseases, augmented clinicians addressing the shortage of quality physicians and healthcare providers, pathology results and treatment course standardization, and understanding genetics to predict future risk of diseases and negative health episodes are some of the Deep Learning projects picking up speed in the Healthcare domain. Readmissions is a huge problem for the healthcare sector as it costs tens of millions of dollars in cost. But with the use of deep learning and neural networks, healthcare giants are mitigating health risks associated with readmissions while bringing down the costs. AI is also being exceedingly being used in clinical researches by regulatory agencies to find cures to untreatable diseases but physicians skepticism and lack of a humongous dataset are still posing challenges to the use of deep learning in medicine.
  9. Personalizations – Every platform is now trying to use chatbots to provide its visitors with personalized experiences with a human touch. Deep Learning is empowering efforts of e-commerce giants like Amazon, E-Bay, Alibaba, etc. to provide seamless personalized experiences in the form of product recommendations, personalized packages and discounts, and identifying large revenue opportunities around the festive season. Even recce in newer markets is done by launching products, offerings, or schemes that are more likely to please the human psyche and lead to growth in micro markets. Online self-service solutions are on the rise and reliable workflows are making even those services available on the internet today that were only physically available at one time. Robots specialized in specific tasks are personalizing your experiences real-time by offering you the most suited services whether it is insurance schemes or creating custom burgers.
  10. Detecting Developmental Delay in Children – Speech disorders, autism, and developmental disorders can deny a good quality of life to children suffering from any of these problems. An early diagnosis and treatment can have a wonderful effect on the physical, mental, and emotional health of differently-abled children. Hence, one of the noblest applications of deep learning is in the early detection and course-correction of these problems associated with infants and children. This is a major difference between machine learning and deep learning where machine learning is often just used for specific tasks and deep learning on the other hand is helping solve the most potent problems of the human race. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions have developed a computer system that can identify language and speech disorders even before kindergarten when most of these cases traditionally start coming to light. The researchers evaluated the system’s performance using a standard measure called area under the curve, which describes the tradeoff between exhaustively identifying members of a population who have a particular disorder. They use residual analysis that identifies the correlation between age, gender, and acoustic features of their speech to limit false positives. Autism is often detected by combining it with cofactors such as low birth weight, physical activity, body mass index, learning disabilities, etc.

What is Deep Learning, you ask again? It is the stuff of sci-fiction dreams infusing our reality.

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