Deep Learning Applications

Speech recognition

Speech recognition is a piece of technology that makes it possible for machines to understand human-language and then change it to machine-readable code for the execution of specific instructions. We all know that machines only think in 0’s and 1’s, a fact that has always made it difficult for earlier programmers, demanding that they be explicit in the kind of coding done. But with speech recognition, that requirement is minimized. Even though speech recognition software is still at its infancy stage with, a few phrases and vocabulary words, the rate at which it is advancing is impressive. The rapid rate of development is attributed to new discoveries that are continuously being made in Deep Learning.

Even though we have had speech recognition for decades, it is becoming common today due to the contribution of Deep Learning. The emergence of Deep Learning has helped increase the accuracy of speech recognition. According to Andrew Ng, the technology’s accuracy ranges between 95% and 99% (Rouse, Speech recognition, n.d.). The 4% gap in its accuracy is assigned to factors like incredibly useful and annoying unreliable.

With the aid of neural machine translation process, it has become possible to just input audio into a neural network, which is then taken through training phase to yield text. That is the basics of how Deep Learning has simplified speech recognition. The only problem that this technology has to deal with is the fact that different people have different speech speed. For instance, one person may say “coming!” while another could take a bit longer saying “cooommmmiiing!” This leads to a longer file that has more data making it hard to align audio files of different lengths to affixed length (Geitgey, 2016). The technology also does not factor in most languages except English.

Speech recognition is a technology that has applications in various fields including voice search, call routing, voice dialing, and speech-to-text processing. The emergence of Deep Learning has made speech recognition fairly easy to use. The fact that most computers and mobile devices today come pre-installed with the technology means that access has been simplified in advance (Gales, July).

Vision & Image recognition

Deep Learning has without doubt been taking over computer vision in the past few years, making major milestones with regard to different tasks and key international competitions. ImageNet is one of the most complex and lucrative computer vision competition. This competition requires that researchers come up with a model that would classify certain images in the dataset with utmost accuracy. As Deep Learning progressively improves, accomplishment of tasks in the competition has been outstanding, even going beyond human capability.

Computer vision has led to major applications including image classification, object detection, object tracking, semantic segmentation, and instance segmentation. Each of these applications is possible thanks to Deep Learning. The problem of image classification is stated this way: the dataset has several labeled images with one category. The goal is to predict the categories into which these images fall and determine the accuracy at which that has been done (Weiner, 2016).

Of course, challenges are bound to be felt with this task including image occlusion, image deformation, intra-class variation, and background clutter. Researchers have managed to develop data-driven models that can solve this problem. When it comes to object detection, computer vision is required to output bounding boxes for a specific object and accurately label it (Le, 2018).

As you can see, computer vision is more than just image recognition. The technology is currently being used for all kinds of real-world applications such as indicating where there are medical maladies in X-rays, warning drivers about animals on the road, and making identification of products and where they can be bought. There are companies that scan social media sites with the aid of computer vision to get images which couldn’t be found using the traditional search process. This is a technology which includes significant complexities and needs more than just image recognition. Big data and semantic analysis also have a role to play.

Besides just image recognition, computer vision is also being used for medical imaging. When you go to a doctor, and MRI, ultrasound, X-ray, and any other test can show whatever changes are going on inside our bodies. When image recognition is applied to these images, it becomes possible to process them at a faster rate and determine health abnormalities with increased accuracy (Le, 2018).

Just as human vision, computer vision being designed with the goal of making it ‘see’ things. As more data is fed into the machine and Deep Learning techniques further refined, the performance of these computers is bound to out-do humans (Weiner, 2016).

Natural language processing

Natural language processing, NLP, is a branch of Artificial Intelligence, information engineering, and computer science, whose interest is in how computers and humans could interact from the perspective of a ‘natural’ language. Focus is on the programming of computers such that they are able to process and analyze large natural language datasets. Several challenges have been developed for NLP including natural language generation, natural language understanding, and speech recognition. The solutions to these problems basically pave the way for major leaps to be made with regard to NLP (George, 2018).

Natural language generation entails yielding natural language out of a machine-representation system like logic form or knowledge base. Language production is a common term that is associated with the formal representation. One could look at a natural language generation system as a translator which receives data and changes it into an NL representation.

As already discussed in the previous sub-section, speech recognition entails the machine receiving human language and converting it into machine-readable format for the execution of specific instructions. Its advancement is fostered by research and knowledge from different fields including computer science, linguistics, and electrical engineering. The speech recognition system gets trained in such a manner that isolated vocabulary and texts are read into the system which analyzes the specific voices used by the person.

Natural language processing has to do with the machine being able to comprehend the natural language. This is a problem that is classified under AI-hard problems. The application of NLU comes after NLP algorithms have been run. As researchers make progress in the three concepts, that is, natural language generation, natural language understanding, and speech recognition, natural language processing benefits immensely (SAS, n.d.).

The main goal of NLP is to make sure that computers get closer to humans. This proximity, researchers believe, is only possible once the machines are able to comfortably and effectively process human language. Advances in Deep Learning have made it possible for computers to accomplish tasks with the aid of human language. Programmers can code the machine to do things such as language translation, summerizing texts, and semantic understanding. These are capabilities which are important in the real-world, making it possible to perform computations on large text with little effort. Some applications that have resulted due to the progress made here include: voice-activation, text categorization, question answering, and automated reasoning (George, 2018).

Customer relationship management

Customer relationship management (CRM) entails technologies, strategies, and practices devised by companies for the management and analysis of customer interactions and data. The main goal of this analysis is to enhance customer service. The most basic component in the CRM is software which can consolidate customer information into a warehouse for easy access.

As Deep Learning technology advances, CRM system has been improved with other functionalities. Some of the additions include taking records of customer interactions via social media, phone, email, and a host of other channels where customer information can be found. With CRM, it has become possible to automate different activities like alerts, calendars, and monitoring of productivity.

Deep neural networks have the ability to extract features which would be input into other Machine Learning algorithms to perform clustering and classification. A lot of unstructured data tends to be sent to the CRM. This data takes different forms, mostly being time series as a result of activity logs and text when customer feedback is received. The good thing is that Deep Learning is effective at handling such kind of data. The essence of CRM is to check up sales and prevent churn. With the aid of Deep Learning, the company could develop instances in which up-selling has been achieved. This could then be associated with online activity and communications.

Customer relationship management systems feature a couple of capabilities whose functionalities can be further refined with the aid of Deep Learning algorithms. Marketing automation and geo-location technology are a classic example here. With marketing automation, repetitive tasks are automated so that marketing is done with minimal effort. For instance, if a potential customer visits your system, marketing materials could be sent to this prospective client automatically through email or social media. Deep Learning technology could be used to get detailed information about this prospect so that relevant marketing materials, which have the potential of converting the lead to a fully-fledged customer, are sent.

Modern innovations in computational linguistics, data analytics, statistics, and Deep Learning provide the foundations for the technologies used in CRM. Businesses have become some of the biggest consumers of Machine Learning technologies and providers are rushing to out-do each other by developing the most helpful applications. Some of the best machine-learning CRMs which are in the market include: Marketo, SugarCRM, and Base. Each of these programs has been fitted with various capabilities like capturing and analyzing large amounts of datasets to recommend operations which can increase sales.

Financial fraud detection

Deep Learning and Artificial Intelligence work in collaboration in order to develop applications that can mitigate the risks of financial fraud. This is an issue that financial institutions have always battled with, and as the scope of global financial transactions increases, the threat intensifies. But this is a threat which could be handled with smarter fraud detection tools that are powered both by Artificial Intelligence and powerful Machine Learning tools.

McAfee released a report in which it estimated that cyber-crime impacts global economies significantly, leading to the loss of about $600 billion annually (McAfee, 2018). The report explains that online transacting is one of the most common types if cyber-crime and it is preventable. The need for intelligent fraud detection has been motivated by the faster rate at which financial fraud happens.

Deep Learning handles the issue of fraud detection from the perspective of a classification problem. Here, a problem such as spam detection is a classic example. Concerning fraud detection in credit card payments, Deep Learning makes it possible to create models which can classify a transaction as being legit or a fraud. This is done taking into consideration factors like time, location, merchant, and amount (Pierre, 2018).

Financial fraud continues to take away significant amounts of individuals’ money as hackers devise new ways of hacking in accounts. The current state of our society does not permit financial institutions to use conventionally programmed systems that are rule-based in the detection of financial fraud. As a result, Machine Learning has been relied upon as the most convenient approach for doing so. The only challenge that results from taking the classification approach in tackling fraud is that most of the transactions in real world data are not fraudulent.

But the accuracy of fraud detection systems is increased when there is a combination of AI and Machine Learning. The role of Artificial Intelligence here is to identify irregular patterns related to the use of the credit card. Deep Learning on the other hand would be implemented with the aim of creating user and transaction fingerprints such that relationships are established between available data points. The good thing about a sophisticated model is that it can make use of numerous data points so that it can match different customers and given transactions.

The traditional systems used to detect fraud sometimes issue false alerts, locking genuine users from their own accounts. For example, if you have never made an airline ticket purchase and you do so for the first time using your credit card, a fraud warning may be triggered. This is an issue that financial fraud detection systems powered by AI and Deep Learning seek to overcome.

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