Introduction to Artificial Intelligence Applications
Artificial Intelligence Application has been a subject widely prevalent in the IT world for more than 4 decades, and it has been in the laboratory all through.
In today’s digital world, the abundant availability of
- Data generated from multiple sources (Big data)
- Compute and storage resources (on-premises and Cloud)
- Internet Bandwidth
- Complex Algorithms to derive insights from big data facilitated the development of AI applications using various new technologies. AI platforms are being built for old business cases but not implemented due to technology limitations and new use cases that evolved along with AI technology.
What are Artificial Intelligence Applications?
AI is about building intelligence into machines to enable them to perform human tasks precisely for known operations. It also packs machines with many data points for the machines to learn and handle unknown situations. AI simulates machines to work, learn, think, and react like humans.
In this article, let’s see the applications developed using AI technologies.
Most Adopted AI Technologies
1. Natural Language Processing
NLP is a method of making machines understand the natural language humans speak or interact with. It also enables a machine to respond to the human after processing his request as a human understands. Syntax and semantics are the key parameters in NLP.
2. Machine Learning
ML enables the machine to learn automatically using the available data points without depending on external instructions. ML algorithm helps the device handle unknown situations and manage them. Deep learning is a subset of ML, and it works with unstructured big data with a structured layer approach, like the human brain, to derive deep insights.
3. Robotic Process Automation
It automates the mundane manual transaction process with the software robot. This robot navigates ERP/CRM/FSM software steps and completes the transaction as a human does. It improves the efficiency of business operations and releases manual resources for value-added work.
4. Facial Recognition
Face Recognition helps to identify human faces using technology. Using Biometric features, the human face is mapped from a video or photo and matched with the database to find a match. The privacy issue is a major challenge in using this technology.
List of Top Artificial Intelligence Applications
Applications are being built using AI technologies for Industries, Agriculture, Education, and E-commerce. Apps like Digital Assistant developed on voice recognition technology, are widely used in the public domain. AI Apps are on different levels of maturity.
A list of top AI apps across various segments are –
1. Transportation
- Drivers are facilitated with AI features like self-parking and advanced cruise controls to assist them.
- AI techniques are used to improve traffic management systems, reducing wait times, fuel consumption, and emissions by 25%.
- Automatic transmission system.
- A driverless (Autonomous) car is in the pilot stage.
2. Manufacturing
- Robot in manufacturing in non-ergonomic conditions.
- Predictive smart maintenance to avoid production loss.
- Early alert on probable quality issues in the manufacturing line due to machine behavior, raw material quality, etc.
3. Healthcare
- Faster diagnosis using patient’s health and related data (IBM’s Watson).
- Medical image scanning for the detection of diseases.
- Clinical Decision support system using data mining.
- Robots do repetitive jobs in surgery and patient care.
4. Finance and Banking
- Measuring the creditworthiness of clients and risk-free loan disbursement using data from social media and other sources.
- AI engines assist the investment decision of financial institutions.
- Algorithmic trading, complex AI systems, automate trading decision-making.
5. Human resources
- AI-assisted recruitment.
- Predicting Attrition of Employees.
6. Agriculture
- AI techniques to increase the yield of crops and suggest methods to increase efficiency in farming.
- Monitoring crop and soil conditions to track the health of crops.
- Data inputs to farmers on changes in weather conditions and market environment to plant their crops.
7. Education
- AI can help streamline administrative tasks for teachers, such as creating presentations using an AI presentation maker.
- AI Tutor: Individual care for students in the areas wherever they need extra input.
- AI offers an Adaptive learning program that matches the preference of the students.
8. E-Commerce
- Visual Search to identify items to shop.
- Chatbot to provide information content.
- Automatic display of products based on browsing history.
- Utilize an AI Video Generator for dynamic product presentations.
9. Digital Assistant
- Voice recognition applications are popular in the public domain, and many digital assistant platforms in the market interact with people and provide information as per their needs on anything on earth.
- Siri (Apple), Alexa (Amazon), Google Now, Cortana (Microsoft), Facebook Messenger, Blackberry Assistant, Teneo, Speaktoit Assistant, Hound, and Braina are the most popular digital assistant software platforms.
- This software is built into end-user devices like phones and tablets or marketed as separate gadgets like Amazon Echo, Google Home, etc.
- Additionally, AI audio visualizers are becoming increasingly integrated into these platforms, enhancing user experiences by visually representing voice commands and interactions in real time, making it easier to understand and engage with digital assistants.
10. AI Embedded in Devices
- AI applications are embedded in connected devices like machines, fridges, A/c units, and electrical fittings, making them smarter.
- People can interact with these devices and activate and deactivate them remotely, and these devices can talk to other systems and perform certain functions.
11. ERP
- AI functionalities are built-in standard ERP. SAP, Oracle, and other ERP vendors are building add-on AI modules into their ERPs, making them smart and Intelligent ERPs.
Features of Artificial Intelligence Applications Functionalities
It can be classified into the below functions.
1. Narrow Artificial Intelligence Applications
In Narrow AI, systems are designed to perform specified tasks reactively. Many successful AI implementations like Voice recognition systems, RPA, facial recognition, and data intelligence belong to this category, and these Apps undergo continuous improvements.
2. Strong Artificial Intelligence Applications
In Strong AI, Systems are designed to match human cognitive abilities to think and decide like humans when presented with unfamiliar scenarios. Machine learning, Deep learning, and neural networks are in this category. Historical data and data from surrounding systems are essential in building intelligence into these systems. Autonomous/driverless vehicles and decision-making systems are designed in this way.
Other Features of Artificial Intelligence Applications
Skills like Knowledge, Reasoning, Problem-solving, Perception, Learning, Planning and Manipulation, and physical movement will have to be featured in AI programming. Data access from several sources and intuitive algorithms built into the program helps AI to acquire these skills.
Conclusion – Artificial Intelligence Applications
More than 50% of major Industries globally have implemented at least one use case in AI technology, and the adoption of AI is on the fast lane. There are successful AI implementations in automating mundane tasks (RPA, Chatbot), Voice recognition, Service calls management, and Data Intelligence areas. A current trend in AI is to migrate from Decision support AI to Decision making AI.
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