But do you know why there is such a demand for AI?
Most people who are curious about learning data science and / or they do not have experience, they also have this doubt. To find an answer, let's look at some glorious and real applications of data science and artificial intelligence.
Autocomplete is a function that predicts the remainder of a word while the user is still typing. On smartphones, it's called predictive text.
In the previous snapshot, a user starts typing “what is the cli …” and receives some predictions as a consequence of Natural language processing. User presses the tab key to accept suggestions or the down arrow key to select an appropriate option. Through use Seq2Seq and the attention mechanism, data scientists can achieve high precision and low loss for predictions.
For natural language processing, zero shot Y a drink there are also learning techniques. One-shot learning is a perfect alternative for implementation and operation with lower training capabilities in other applications that use embedded systems.. The forecast of the next custom word for a specific user, by knowing the user's messaging habits, could save a lot of time. This method is used in the virtual assistants available at the moment.
Smart face lock
Facial accreditation is a procedure for 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 obstructions, what a simple task.
To perform face detection and accurately detect multiple faces in the frame, DataScientist often uses Haar cascade classifier – an XML file used with an open-cv module to read and detect faces. Deep neural networks (DNN) they can also be used for facial accreditation and are known to work well. the Transfer learning models like VGG-16, RESNET-50 architecture, red facial Architecture can help build a high-quality facial accreditation system.
Current models are very accurate and can provide more than 90% precision for labeled data sets. Facial accreditation models are used with security systems, surveillance and law enforcement, and many more real world applications.
A virtual assistant also establishes itself as an artificial intelligence assistant, an application program that understands voice commands and executes tasks for the user. Virtual assistants powered by artificial intelligence technology are becoming more and more common and are taking over the world by storm.
Some popular examples of virtual assistants are Google AI, Apple siri, Microsoft Alexaand many other similar virtual assistants. With the help of these assistants, voice commands can be translated and assigned to automated hands-on work. As an example, a user can make calls, send messages or browse the web with a simple voice command. Users can also talk to these virtual assistants, so they can also act as chatbots.
The power of virtual assistants is not limited to smartphones or computing devices. They can also be used in IoT devices and embedded systems to efficiently perform tasks and monitor the entire world around you.. An example of this can be home automation with the Raspberry pi, where you can control the whole house with a voice command.
The advances 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 accusations beyond the rule., marking them for human research. The use of artificial intelligence in banking specifically dates back to 1987, when the US National Security Bank of the Pacific. UU. Established an EE Fraud Prevention Task Force. UU. To counter the fraudulent use of debit cards.
Quick decision making and quality results achieved to solve complex financial and economic problems in real time, like stock market predictions, through the use of time series analysis. Deep learning approaches with LSTM They are also applicable in this area to achieve reliable projections for the future of companies..
With artificial intelligence technology, processes were automated to handle activities such as interpreting new rules and regulations or generating personalized financial reports for people. As an example, IBM Watson can understand specific legislation, such as the 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 greatly improving.. With its various applications, AI has a wide reach in the medical department.
One of the first problems for beginners in computing is to solve a prediction machine learning challenge to categorize whether a patient has a tumor or not.. The evaluation data generally have a series of input characteristics with different variables and sample output for the patients.. After preparation, the machine learning algorithm can recognize these input and output characteristics and try to find the right combination throughout the training. When I finish, the model can accurately measure and represent projections on other data sets with greater precision.
Despite this, this was just one case and there are many uses in the medical industry. Deep learning and neural networks help achieve successful results in scanning and other medical applications. Advances in computing power combined with the large volumes of data produced in healthcare systems make particular clinical problems perfect for AI applications..
Below are two recent implementations of scientifically applicable and reliable algorithms that can help both patients and clinicians by facilitating diagnosis..
The first of these algorithms is one of several existing examples of an algorithm that outperforms clinicians at image detection tasks.. In the fall of 2018, Researchers at Seoul National University Hospital and College of Medicine developed an AI algorithm called DLAD to examine chest x-rays and identify irregular cell growth, as possible cancers.
The second of these algorithms comes from Google AI Healthcare researchers, also in the fall of 2018, who developed a learning algorithm, LYNA (Lymph node assistant), which analyzed tissue samples stained with histology slides to categorize metastatic breast cancer tumors from lymph node biopsies. Not the first AI application to attempt a histological examination, but it should be noted that this algorithm could categorize suspicious regions not identifiable to the human eye in the presented biopsy samples.
With many more data-driven smart applications that are already available to us, the future will continue to see many more explorations in this growing field of data science and artificial intelligence.
In this post, my goal was 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 many more uses of these technologies in AI, and it would take a long time to list all these various possibilities.
Despite this, this post provides a fair understanding of modern real life applications discovered through artificial intelligence and data science. If you are curious to know more complicated and advanced projects, comment below. I will try to cover that in more detail in a future post..
Hope you found this post useful and have a great day, Thank you.
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