The demand for skilled Data Scientists is growing faster than before. There are a lot of requirements in subfields of AI like Machine Learning, Deep Learning, Computer Vision and NLP, etc. Statistics say that the hiring of specialists in AI has grown by 74% over the last four years. Data Science is known as the most “marvelous” job of the 21st century.
But do you know why there is so much demand for AI?
Most of the people who are curious to learn Data Science and/or are inexperienced have this doubt too. To find an answer, let us look at a few glorious and real-life applications of Data Science and AI.
Autocomplete is a feature that predicts the rest of a word while the user is still typing. In smartphones, it is called predictive text.
In the snapshot above, a user starts by typing in “what is the cli..” and receives some predictions as an outcome of Natural Language Processing. The user presses the tab key to accept suggestions or the down arrow key to pick a suitable option. By using Seq2Seq and the attention mechanism, data scientists can achieve high accuracy and low losses for predictions.
For Natural Language Processing, zero-shot and one-shot learning methods also exist. One-shot learning is a perfect option for the implementation and operating with lower training capacities in other applications that use embedded systems. The next word prediction personalized for a specific user, by knowing the user’s habits of messaging, could save a lot of time. This method is used in currently available virtual assistants.
Smart Face Lock
Face recognition is a process of verifying the identity of a person using their face, with face detection as an important step. Face detection distinguishes the human face from the background and other obstacles, which is an easier task.
To perform face detection and precisely detect multiple faces in the frame, DataScientist often uses Haar Cascade Classifier – an XML file used with an open-cv module for reading and detecting the faces. Deep neural networks(DNNs) can also be used for face recognition, and are known to perform well. The Transfer Learning models like VGG-16, RESNET-50 architecture, face net architecture can help build a high-quality face recognition system.
The present-day models are highly accurate and can provide more than 90% accuracy for labeled datasets. The face recognition models are used with security systems, surveillance, and law enforcement, and many more real-world applications.
A virtual assistant is also defined as an AI assistant, an application program that understands voice commands and executes tasks for the user. AI-powered Virtual Assistants are becoming increasingly common and are taking over the world by storm.
Some popular examples of virtual assistants are Google AI, Apple Siri, Microsoft Alexa, and many other similar virtual assistants. With the aid of these assistants, speech commands can be translated and mapped to an automated practical job. For instance, a user can make calls, send messages, or browse the web with a simple voice order. Users can also speak to these virtual assistants, so they can also act as chatbots.
The power of Virtual Assistants is not limited to smartphones or computer devices. They can also be used in IoT devices and embedded systems to perform tasks efficiently and to monitor the entire world around you. An example of this can be home automation using the Raspberry Pi, where one can control the whole house with a voice command.
Progress and advances of Artificial Intelligence and Data Science in the area of finance are also immense. Financial firms have long used Artificial Neural Network systems to identify charges or allegations beyond the norm, flagging them for human investigations. The use of AI in banking can be dated specifically to 1987 when the US Security Pacific National Bank set up a US Fraud Prevention Task Force to counter the fraudulent use of debit cards.
Rapid decision-making and quality results achieved to solve complex real-time financial and economic problems such as stock market predictions by using time series analysis. Deep learning approaches with LSTMs are also applicable in this area to achieve reliable projections of the future of companies.
With AI technology, processes were automated to handle activities such as interpreting new rules and regulations or generating customized financial reports for individuals. For example, IBM’s Watson can grasp specific legislation, such as additional reporting provisions of the Markets in Financial Instruments Directive and the Home Mortgage Disclosure Act.
The application of artificial intelligence and data analysis in the medical sciences is crucial, and advances in this area are improving immensely. With its various applications, AI has plenty of reach in the medical department.
One of the first computer science beginner problems is to solve a prediction machine learning challenge to classify whether or not a patient has a tumor. Evaluation Data generally has a series of input features with different variables and sample output for patients. After preparation, the machine learning algorithm can recognize these input features and output features and attempt to find the right match during training. When done, the model can accurately measure and render projections about other datasets with higher accuracy.
However, this was just a single case, and there are a lot of uses in the medical industry. Deep learning and Neural networks help to achieve successful outcomes in scanning and other medical applications. Advances in computing capacity combined with large volumes of data produced in healthcare systems make particular clinical issues perfect for AI applications.
Below are two recent implementations of reliable and scientifically applicable algorithms that can help both patients and clinicians by making diagnosis easier.
The first of these algorithms is one of several existing examples of an algorithm that outperforms physicians in the tasks of image detection. In the fall of 2018, researchers at the Seoul National University Hospital and the College of Medicine developed an AI algorithm named DLAD to examine chest radiographs and identify irregular 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 developed a learning algorithm, LYNA (Lymph Node Assistant), which analyzed histology slides stained tissue samples to classify metastatic breast cancer tumors from lymph node biopsies. It is not the first application of AI to attempt an examination of histology, but noteworthy that this algorithm could classify suspicious regions unidentifiable to the human eye in the biopsy samples presented.
With many more data-driven intelligent applications made available to us already, the future will continue to witness numerous more explorations in this growing field of Data Science and AI.
In this post, I aimed to cover some of the most common real-life applications of Artificial Intelligence and Data Science in the current generation of the advanced world. There are tons more uses of these technologies in AI, and it would take a long time to list all these various possibilities.
However, this post provides a fair understanding of the modern real-life applications discovered using AI and Data Science. If you are curious to know more complicated and advanced projects, then comment about it below. I will try to cover that in more detail in a future article.
I hope you have found this article useful and have a great day, Thank you.
The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.