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10 Awesome Real-World Applications Of Data Science And AI

Understanding and analyzing the day to day use of AI and Data Science In the real world!

Photo by Aaron Burden on Unsplash
Photo by Aaron Burden on Unsplash

Artificial Intelligence and Data Science are taking over the modern era and are changing the modern era into a revolutionary step. We are surrounded by fast-paced computing devices and a variety of game-changing evolutionary ideas that are making the world a much better bubble to live in and witness the numerous explorations to be made in the future.

There is no shortage of Artificial Intelligence and Data Science usage and implementations in the practical world. They are fabulous fields that are covering a wide array of spectrums and multiple real-life applications.

In fact, Artificial Intelligence is the fastest-growing field in the present-day. According to fortune, the statistics say that the hirings for AI specialists have grown by 74% over the last four years. Artificial Intelligence is often regarded as the "Hottest" job of the present generation.

The demand for skilled AI specialists is growing faster, like never before. Requirements and open positions for experts in the sub-fields of AI like machine learning, deep learning, computer vision, statistics, and natural language processing are surging each day.

But why is there so much demand?

This question is commonly asked by beginners or people inexperienced in the fields of AI and data science. In this article, we aim to overcome this question with ten out of the billion possible applications in the real-world. The aim of the ten use-cases provided in this article is to understand the most commonly used AI and Data Science Technologies in the current generation.

So, without further ado, let us get started and look at some of the wonderful applications of AI and Data Science in the real-world.


1. E-mail Spam Filtering:

Photo by Austin Distel on Unsplash
Photo by Austin Distel on Unsplash

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. Let us understand this concept with a simple example.

Assume that we have a dataset of 30,000 emails, out of which some are classified as spam, and some are classified as not spam. The machine learning model will be trained on the dataset. Once the training process is complete, we can test it with a mail that was not included in our training dataset. The machine learning model can make predictions on the following input and classify it correctly if the input email is spam or not.

The main methodology behind detecting if the provided email is – spam or not is by detecting patterns of fake emails and words that are usually used while promoting or advertising products for customers with over the top discounts or other similar ways.

The various machine learning algorithms like Naive Bayes, support vector machines, K-nearest neighbors, and random forests, among many other algorithms, can be used for filtering spam messages and classifying if the received email is a "spam message" or not. Advanced spam detection can be performed using techniques like neural networks or optical character recognition (OCR), which is also used by companies like Gmail for spam filtering.

2. Autocomplete:

Screenshot By Author
Screenshot By Author

Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing. In Android smartphones, this is called predictive text. In graphical user interfaces, users can typically press the tab key to accept a suggestion or the down arrow key to accept one of several.

As we type in "what is the wea.." we already receive some predictions. These predictive searches also work on AI. These usually work on concepts such as natural language processing, machine learning, and deep learning. A sequence to sequence mechanism with attention can be used to achieve higher accuracy and lower losses for these predictions.

Zero-shot and one-shot learning methods even exist for natural language processing. The same methods can be used for better training of the model to improve the overall performance and avoid repeated training procedures which can be a really big hindrance in some real-life applications and scenarios. Hence, one-shot learning is a great alternative for deployment and working in other embedded systems with lower training capacities.

The next word prediction for a particular user’s texting or typing can be awesome. It would save a lot of time by understanding the user’s patterns of texting. This could be also used by our virtual assistant to complete certain sentences. Overall, the predictive search system and next-word prediction is a very fun concept to implement. You can check out my article below, which covers the deep learning methodology to predict the next words.

Next Word Prediction with NLP and Deep Learning

3. Autocorrect:

Photo by Patrick Tomasso on Unsplash
Photo by Patrick Tomasso on Unsplash

Autocorrection, also known as text replacement, replace-as-you-type or simply autocorrect, is an automatic data validation function commonly found in word processors and text editing interfaces for smartphones and tablet computers.

Autocorrect based on AI methodologies is highly beneficial to achieve the best results while texting or typing to avoid incorrect statements or words. The spellings are automatically checked, and they are immediately corrected to the nearest right values. However, if the quality of your AI trained is not up to the mark leading to certain errors can be caused, and you might end up sending a message you did not want. Jokes aside, For the most part, autocorrect does a tremendous job in correcting misspelled words while texting quickly.

The process of autocorrect involves four main steps, namely, identifying a misspelled word, then finding the strings while computing the minimum edit distance from each of them, filtering the possible candidates for the right word selection. And finally, calculating the word probabilities to forecast the best possible prediction for the particular word.

The method mentioned above is one of the ways to compute the autocorrect problem with the help of machine learning algorithms like logistic regression or Naive Bayes. However, even methods of deep learning can also be used to solve such similar problems. If you guys are interested, then let me in the comments below, and I will make an article covering the following in further detail.

4. Smart Face Lock:

Photo by Dariusz Sankowski on Unsplash
Photo by Dariusz Sankowski on Unsplash

Face recognition is the procedural recognition of a human face along with the authorized name of the user. Face detection is a simpler task and can be considered as a beginner level project. Face detection is one of the steps that is required for face recognition. Face detection is a method of distinguishing the face of a human from the other parts of the body and the background.

The haar cascade classifier can be used for the purpose of face detection and accurately detect multiple faces in the frame. The haar cascade classifier for frontal face is usually an XML file that can be used with the open-cv module for reading the faces and then detecting the faces. A machine learning model such as the histogram of oriented gradients (H.O.G) which can be used with labeled data along with support vector machines (SVM’s) to perform this task as well.

The best approach for face recognition is to make use of the DNN’s (deep neural networks). After the detection of faces, we can use the approach of deep learning to solve face recognition tasks. There is a huge variety of transfer learning models like VGG-16 architecture, RESNET-50 architecture, face net architecture, etc. which can simplify the procedure to construct a deep learning model and allow users to build high-quality face recognition systems.

You can also build a custom deep learning model for solving the face recognition task. The modern models built for face recognition are highly accurate and provide an accuracy of almost over 99% for labeled datasets. The applications for the face recognition models can be used in security systems, surveillance, attendance systems, and a lot more.

Below is an example of a face recognition model built by me using the methods of VGG-16 transfer learning for face recognition after the face detection is performed by the haar cascade classifier. Check it out to learn a more detailed explanation of how exactly you can build your very own face recognition model.

Smart Face Lock System

5. Virtual Assistant:

Photo by BENCE BOROS on Unsplash
Photo by BENCE BOROS on Unsplash

A virtual assistant, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes tasks for the user. Virtual Assistants powered with AI technologies are becoming extremely popular and are taking over the world by a storm.

We have virtual assistants like Google AI, Siri, Alexa, Cortana, and many other similar virtual assistants. With the help of these assistants, we can pass commands, and using speech recognition, it tries to interpret what we are saying and automates/performs a realistic task. Using these virtual assistants, we can make calls, send messages or emails, or browse the web with just a simple voice command. We can also converse with these virtual assistants, and hence they can also act as chatbots.

The power of Virtual Assistants powered by Artificial Intelligence is not limited to smartphones or computer devices. They can also be used in IoT devices and embedded systems to perform tasks effectively and control the entire surrounding around you. An example of this can be home automation using a Raspberry Pi, where you are able to control your entire house with a simple voice command.

The combination of AI and IoT is a big deal as they produce amazing results. With the integration of artificial intelligence in embedded IoT devices like raspberry pi and Nvidia Jetson Nano (Among many others) are capable of developing some masterpieces, which will be highly profitable and beneficial to society as a whole. Some popular examples of virtual assistants like Alexa, Siri, or Google AI show the high-level intellect and future possibilities.

6. Chatbots:

Photo by Austin Distel on Unsplash
Photo by Austin Distel on Unsplash

Chatbots are used universally today on many websites to interact with the human users that arrive on the specific sites. They try to provide them effective communication and explain to the users how the company or industry works while providing detailed instructions and guides with spontaneous replies.

The popularity of chatbots has been on the rise for the past decade. Chatbots are usually used for quick responses to most commonly asked questions on a particular website. Chatbots save time as well as reduce human labor and expenditure. There are so many types of chatbots, and each of them specializes, in particular, in one or a few fields. The best approach for knowing what kind of chatbot you want to build is as follows – If you want to build chatbots, the best approach is to look for what are your target audience, companies, or businesses. Making specific chatbots is ideal as you can greatly improve the performance of the distinct task.

If you are interested in building your very own chatbot from scratch with the help of deep learning and neural networks, specifically using 1-Dimensional Convolutional Layers, then feel free to check out the article below, where I have covered the following procedure in complete detail.

Innovative Chatbot using 1-Dimensional Convolutional Layers

More methods of pre-processing and natural language processing can be used to achieve a higher accuracy and reduced loss on the training of the model. This can also improve the overall predictions of the model. Advanced training algorithms like the GPT-3 model trained on almost 175 billion parameters works fantastic even for conversational chatbots and it is a great alternative method to train high-quality chatbots. Other methods like transfer learning based classification, sequence to sequence models with attention, or even certain one-shot learning methods for training can be used.

7. Optical Character Recognition:

Photo by Arnel Hasanovic on Unsplash
Photo by Arnel Hasanovic on Unsplash

Optical character recognition is the conversion of 2-Dimensional text data into a form of machine-encoded text by the use of an electronic or mechanical device. You use computer vision to read the image or text files. After reading the images, use the pytesseract module of python to read the text data in the image or the PDF and then convert them into a string of data that can be displayed in python.

OCR engines have been developed into many kinds of domain-specific OCR applications, such as receipt OCR, invoice OCR, check OCR, legal billing document OCR. The various applications and utilizations of OCR technology in real-life scenarios are Data entry for business documents, e.g., Cheque, passport, invoice, bank statement, and receipt, Automatic number plate recognition, In airports, for passport recognition and information extraction, and so much more.

The installation of the pytesseract module might be slightly complicated. So, refer to a decent guide to get started with the installation procedure. You can also look at the resource link provided below to make the overall installation process easier. It also guides you through an intuitive understanding of optical character recognition. Once you have an in-depth understanding of how OCR works and the tools required, you can proceed to compute more complex problems. This can be using sequence to sequence attention models to convert the data read by OCR from one language into another.

Getting Started with Optical Character Recognition using Python

8. Finance:

Photo by Markus Spiske on Unsplash
Photo by Markus Spiske on Unsplash

The progress and advancements of Artificial Intelligence and Data Science in the field of finance is also tremendous. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in the US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

With the help of time-series analysis and forecasting, fast-paced decisions and quality results can be obtained to solve complex real-time financial and economic problems like stock market predictions. Deep learning methods with LSTMs are also applicable in this field for obtaining accurate predictions on the future of businesses.

The many applications of AI in finance include Algorithmic Trading that involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention, predictive analytics, transaction data enrichment, and for financial statements audit, AI makes continuous audit possible.

With AI technology, it’s possible to automate processes to manage tasks like understanding new rules and regulations or creating personalized financial reports for individuals. For example, IBM’s Watson can understand complex regulations, such as additional reporting requirements of the Markets in Financial Instruments Directive and the Home Mortgage Disclosure Act.

Refer to the following reference for further reading on this topic.

9. Medical:

Photo by Ousa Chea on Unsplash
Photo by Ousa Chea on Unsplash

The Utilization of Artificial Intelligence and Data Science in medical sciences is critical, and the advancements in this field are improving tremendously. AI has a humungous scope in the medical department with its numerous applications.

One of the first programs beginners in data science work on, is to solve a classification Machine Learning task to classify if the patient has a tumor or not. You are given a set of input features with various factors and a sample outputs for the same. The machine learning algorithm will understand this input features and output features after training, while trying to find the best fit. Once completed, the model can effectively compute and make predictions of other datasets with higher accuracy.

However, this was just a simple example and there are way more applications in the medical field. Deep learning and neural networks are being used for producing effective results in scanning and other medical applications. Advances in computational power paired with massive amounts of data generated in healthcare systems make many clinical problems ripe for AI applications. Below are two recent applications of accurate and clinically relevant algorithms that can benefit both patients and doctors through making diagnosis more straightforward.

The first of these algorithms is one of the multiple existing examples of an algorithm that outperforms doctors in image classification tasks. In the fall of 2018, researchers at Seoul National University Hospital and College of Medicine developed an AI algorithm called DLAD (Deep Learning based Automatic Detection) to analyze chest radiographs and detect abnormal cell growth, such as potential cancers.

The second of these algorithms comes from researchers at Google AI Healthcare, also in the fall of 2018, who created a learning algorithm, LYNA (Lymph Node Assistant), that analyzed histology slides stained tissue samples) to identify metastatic breast cancer tumors from lymph node biopsies. This isn’t the first application of AI to attempt histology analysis, but interestingly this algorithm could identify suspicious regions undistinguishable to the human eye in the biopsy samples given. LYNA was tested on two datasets and was shown to accurately classify a sample as cancerous or noncancerous correctly 99% of the time. Furthermore, when given to doctors to use in conjunction with their typical analysis of stained tissue samples, LYNA halved the average slide review time.

Refer to the following reference for further reading on this topic.

10. Robotics:

Photo by Possessed Photography on Unsplash
Photo by Possessed Photography on Unsplash

I am super hyped for the future of robots based on artificial intelligence technologies. There will be a wide range of opportunities in the future for both AI and robotics. We can now have robots that visually perceive and understand human emotions and actions as well as interact with us.

Robotics and artificial intelligence have a humungous scope in the future. The integration of data science projects along with robots has tremendous potential for enforcing top-notch product manufacturing in industries with very little human effort.

Apart from this, robotics and data science can be exclusively used to achieve human-level performance on many pre-programmed tasks. The advancements in IoT and the community are also highly beneficial for the integration of AI in robotics to develop smart and effective devices.

A handful of robotic systems are now being sold as open-source systems with AI capability. This way, users can teach their robots to do custom tasks based on their specific applications, such as small-scale agriculture in fields. The convergence of open source robotics and AI could be a huge trend in the future of AI robots.


Photo by Benjamin Davies on Unsplash
Photo by Benjamin Davies on Unsplash

Conclusion:

In this article, we aimed to cover some of the most common real-life applications of Artificial Intelligence and Data Science in the current generation of the modern world. There are tons more applications of these technologies in AI, and it would take a subsequently long time to list all these numerous possibilities.

However, this article provides a decent understanding of the modern real-life applications that can be implemented and performed with the help of AI and Data Science. If you are curious to know more complex and advanced projects, then make sure you comment about it below. I will cover that in more detail in a future article.

If you have any clarifications with the choices mentioned in this article or feel like something you like should have also been included, then make sure you leave a reply below, and I will make sure I get back to you as soon as possible.

Feel free to check out some of my other articles from the following links provided below.

5 Essential Steps For Every Deep Learning Model!

Top 5 AI Trends That Will Change The Landscape Of The Future!

Top 5 Qualities of Successful Data Scientists!

Beginners Roadmap To Master Data Science

Solutions To Interview Questions On Pattern Programming!

Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!


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