In Its Simplest Definition, The Performance Of Human Thought Processes By Devices, Especially Computer Systems, Is Called Artificial Intelligence.
Areas Of Artificial Intelligence, Expert systems, natural language processing, speech recognition, and machine vision are examples of artificial intelligence applications that, if you invest in learning them, will succeed in the future.
In this article on the network site, we show you that if you invest in these important artificial intelligence applications, you will have a bright future with a lot of job security.
Important applications of artificial intelligence that shape the future of this technology
Artificial intelligence is widely used to offer customized offers to customers based on previous searches and purchases and other online activities. In the business world, artificial intelligence plays an important role in optimizing product design, investing in stock exchanges and buying stocks, streamlining the supply chain, etc.
The term artificial intelligence was first widely used in 1956, and businesses decided to use the technology extensively in business processes shortly after introducing theoretical concepts. In this article, I, Hamidreza Taebi, based on the perspectives of this field, employment advertisements published on job sites, and student enrollment rates in the field of artificial intelligence will introduce the most important applications of artificial intelligence are future and help you find good jobs.
1. Artificial intelligence and its applications
Healthcare industry: Artificial intelligence in the healthcare industry can diagnose appropriate drugs or read X-ray images. Smart assistants can be used as life coaches to remind you not to forget medications, exercise regularly and eat healthy meals.
Production: AI can use an amplification network, or more accurately, machine learning, which is a form of deep learning that uses consecutive data to evaluate the performance of production units, forecast supply, and demand rates, and validate the performance of devices especially IoT equipment.
Life sciences: Artificial intelligence technology can use the full potential of data to overcome health challenges. Predict drug interactions or non-interaction and accelerate the process of introducing new drugs.
Retail: The virtual shopping features provided by Retail AI are based on the right offers and help customers buy the right products. In addition, artificial intelligence can help manage budgets for customers and increase sales to retailers.
Banking sector: Artificial intelligence in the banking sector increases the speed of processing requests, accuracy and improving the performance of human activities. Artificial intelligence approaches can help financial institutions identify scams, money laundering, automate transaction management, and score good customers.
Public Sector: Industrial intelligence has the potential to make smart cities smarter. In addition, it helps the military to carry out preventive operations and missions better.
2. Machine learning
Machine learning is a type of data analysis that aims to build analytical models that work automatically. Machine learning is the main application of artificial intelligence in the real world, which tries to help computers learn from data, recognize patterns, and select and perform tasks without any human information or at least human intervention.
Machine learning applications
The importance of machine learning technology is most evident in the areas that deal with the vast amount of data. The most important applications of machine learning are the following:
Financial Services: Machine learning in banks and financial institutions is used for two main purposes: identifying the right and valuable choices and detecting fraud.
Healthcare Services: Machine learning in this industry will experience an improvement due to the development of wearable devices and sensors that can use data to analyze patients’ health in real-time. Medical professionals may use this technology to review data and diagnose the patient’s healing process or condition and prescribe appropriate medications that accelerate the healing process.
Government: Government agencies have access to a vast amount of data that they can use to extract insights. Government agencies can use machine learning to provide better government services.
Retail: Online stores can use machine learning to evaluate customers’ shopping history and offer products to customers. Today, large retailers use machine learning extensively to collect, evaluate, improve the shopping experience, run marketing campaigns, optimize prices, plan for delivering items, and identify customer behavior patterns.
3. Deep learning
Deep learning is a more advanced type of machine learning in which the computer tries to analyze speech, recognize images, and make high-precision predictions. In-depth learning receives and adjusts basic parameters about data and teaches the computer to use multiple layers to identify patterns rather than using pre-defined equations to work with data.
Deep learning applications
Voice recognition: In-depth learning is used for voice recognition in both business and research. Software such as Xbox, Skype, Google Now, and Apple Siri are some letters that use deep learning technology in this field.
Natural language processing: One of the main applications of deep learning is to use deep learning to process and interpret text. The above approach, which is a subset of text analysis, is used to find patterns in various sources such as customer complaints, medical notes, news, etc.
Image recognition: The two main image recognition applications are providing automatic subtitles for images (videos) and evaluating graphic elements within images. Not bad to know that the 360-degree technology used in cars also uses the speech recognition pattern.
Bidding systems: Amazon and Netflix came up with the basic idea of using bidding systems. Systems that can predict users’ interest in a future product based on previous activity. In-depth learning can improve offers on many complex platforms and industries, such as music, clothing, and personal accessories.
4. Natural language processing
Natural language processing is a subset of artificial intelligence that helps computers understand, interpret, and identify human speech patterns. Organizations use natural language processing to reduce the machine-to-speech problem of smart devices, accurately analyze human speech and better understand human conversations by machines. Natural language processing is used in various fields such as computer science and computational linguistics.
Natural language processing is not new, but organizations use natural language processing more seriously due to the growing interest in human-machine communication and the availability of huge amounts of data, powerful processing resources, and improved performance algorithms in information processing.
Natural language processing applications
Text Analysis and Natural Language Processing: Text analysis counts, categorizes, and categorizes words to extract structure and meaning from large volumes of data.
Examples of natural language processing in everyday life: NLP has a wide range of common and practical applications in our daily lives. Bayesian spam filtering is a natural language processing statistical approach that examines whether spam emails are sent to users’ mailboxes.
Ever missed a phone call and then received a voicemail text in your email inbox or smartphone app? This is a clear example of speech-to-text conversion, one of the main uses of natural processing language.
5. Car vision
Machine vision is another important application of artificial intelligence that teaches computers how to analyze images and understand images. Machines can reliably identify and classify objects using digital images produced by cameras and camcorders and take appropriate action after accurately identifying objects based on their perception of the images.
In many ways, machine vision can identify people or analyze live football. Perhaps the most obvious example is YouTube, which does not automatically allow you to do violent or inappropriate videos.
Computer vision applications
Image segmentation: Machine vision can divide an image into several sections or parts, each of which may be studied independently.
Object Recognition: Machine vision can handle the process of identifying a particular object in an image. A soccer field, an attacking player, a defensive player, a ball, etc., can all be identified using advanced detection of objects in an image. Typically, machine vision models identify objects based on X or Y coordinates.
Face recognition: A special image processing that can identify a specific person in an image and match the recognized image with its database.
Edge Detection: A method of determining the outer edge of an object or scene that helps to understand better and identify what is in the image.
The technique of recognizing shapes, colors, and other visual cues in images is known as pattern recognition.
Categorize photos that are used to distinguish images from each other.
Feature matching: A pattern recognition that compares image similarities to make them easier to classify.