Artificial Intelligence Use Cases.

What is Artificial Intelligence?

Artificial intelligence (AI) is a branch of computer science that studies and develops intelligent systems. These systems comprise of hardware and/or software components which can perform certain tasks intelligently, without relying on strictly defined algorithmic steps coded by humans. Instead, these systems are powered by data to build a new bread of algorithms in the form of machine learning (ML) and deep learning (DL) simulation models that can mimic human intelligence to perform specific tasks. However, these systems do not yet posses the human-like cognitive abilities to learn, reason, and solve problems from the scratch. This capability is known as as Artificial General Intelligence (AGI) and is considered one of the main research goals of AI.   

Top 10 Artificial Intelligence Technology Domains

AI/ML infrastructure comprises of the computing and storage systems that provide a reliable and scalable environment for data scientists and engineers to build and deploy AI/ML models efficiently in production. It also includes ancillary tools, libraries, and cloud services that aid in overall deployment and management of an AI/ML system.

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AI/ML middleware plays an important role in achieving efficiency in the overall deployment of a AI/ML system.  It acts as an intermediary layer between the AI/ML models, the AI/ML infrastructure and user to provide enhanced features to optimize the typical workflows such as model management, deployment, and monitoring. 

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AutoML (Automatic Machine Learning) refers to the process of automating the end-to-end process of applying machine learning to real-world problems, using pre-built models and tools for tasks such as feature engineering, algorithm selection, and hyperparameter tuning. The goal of AutoML is to make machine learning accessible and effective for a wider range of users, including domain experts with limited data science and machine learning expertise.

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Computer vision is a specialized field under artificial intelligence focused on enabling computers to interpret and understand visual information from the world in the same way that humans do. This involves developing algorithms and models that can analyze images and videos to identify objects, recognize patterns, and extract meaningful information.

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Data analytics is one of the fundalemtal areas in artificial intelligene which deals with examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. The process involves a range of techniques and tools, including statistical analysis, probability, data visualization, and database management, to help organizations turn raw data into actionable insights.

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Deep learning is a field of artificial intelligence that focuses on building artificial neural networks that minic the human brain, having multiple layers. It is based on the idea that a complex problem can be divided into smaller and simpler parts, and these parts can be learned and combined to produce a solution to the original problem. It is based on deep neural networks that are designed to recognize patterns to make predictions and generate similar patterns based on input patterns.

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ETL stands for Extract, Transform, Load, and refers to the process of collecting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse for further analysis and reporting. It is one of the fundamental processes in artificial intelligence which is responsible for data management process, as it allows organizations to integrate data from different sources, identify trends, and make informed decisions. The goal of ETL is to ensure that data is consistent, accurate, and available for analysis, regardless of the original source.

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ML Oerations (ML Ops) is a set of practices and processes that enable organizations to manage and operationalize their models at scale. ML Ops is concerned with the entire lifecycle of ML models, from development to deployment and management, and covers a range of tasks including model training, validation, deployment, monitoring, and maintenance. The goal of ML Ops is to ensure that ML models are consistently delivering accurate results in production, while also providing the agility and speed required to rapidly develop, deploy, and update models in response to changing business needs.

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Natural Language Processing (NLP) is a field of artificial intelligence and computer science that focuses on the interaction between computers and humans in natural language. NLP aims to enable computers to understand, interpret, and generate human language in a way that is meaningful and useful. NLP techniques are used in a variety of applications, including sentiment analysis, text classification, machine translation, text generation, and question answering.

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Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions and observing the consequences in an environment. The goal of the agent is to maximize a reward signal over time by selecting actions that lead to the highest expected reward. RL algorithms are used to solve a wide range of problems, including robotics, game playing, recommendation systems, and autonomous systems. The key components of an RL system are the environment, the agent, the state space, the action space, the reward function, and the policy.




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Use Cases by Industry Verticals

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Use Cases by Horizontal Functions

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Use Cases by User Personas

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Artificial Intelligence Use Case Master Index

AI/ML Infrastructure Use Cases

AI/ML Middleware Use Cases

AutoML Use Cases

Computer Vision Use Cases

Data Analytics Use Cases

Deep Learning Use Cases

ETL Use Cases

ML Operations Use Cases

NLP Use Cases

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This page is updated every week to enlist emerging use cases of artificial intelligence realized with the help of innovating platforms, solution components and tools. 

Which Artificial Intelligence Use Cases are Covered?

Artificial Intelligence encompasses multiple cutting edge technologies, from computer vision, to natural language processing, to deep learning and streaming data processing. The artificial intelligence use case index covers all the prevalent and emerging use cases that are setting the stage for newer innovations enabling a world where humans are assisted by augmented intelligence. You can check out the domains section to get a glimpse of the various technological domain within artificial intelligence.


How can I search for Artificial Intelligence Use Cases?

You can search based on various artificial intelligence domains or the alphabetical list of artificial intelligence use cases. Additionally, you can also look out for artificial intelligence use cases based on industry verticals, horizontal functions, and user personas. Some of the popular industries adopting artificial intelligence are finance, insurance and agritech. In terms of horizontal functions, we find a lot of artificial intelligence use cases in HR and sales , marketing. Similarly, ML engineers and data scientists are the most prevalent personas working on the development of artificial intelligence based applications.


What are the Top Artificial Intelligence Use Cases?

The top artificial intelligence use cases are the ones that augment our basic senses, such as vision and hearing. In this regard, computer vision and text to speech recognition capabilities have made ground breaking progress. Apart from that, natural language processing and deep learning have profoundly improved our cognitive abilities in deciphering massive amount of unstructured data. Additionally, explainable AI is now rising up as the new man machine interface to synergize AI assisted tasks for humans.   


How Can I Contribute to Artificial Intelligence Use Case Index

If your company is building innovative platforms or tools that have artificial intelligence at the core of innovation, you can reach out to us. We are happy to cover your story, as a blog post covering the technical nuances or business insights around adopting your technology for realizing the specific use case.


Have Any Specific Questions?

If you have any specific questions, please feel free to drop us a line and we shall initiate a conversation.