Artificial Intelligence (AI) is everywhere and seems to be rapidly growing before we can even fully comprehend the full scope and power of the tools that AI makes possible. There are several different types of AI that have emerged, and these are the top 10 you are most likely to run into.
1. Machine Learning: Machine learning is a type of AI that utilizes algorithms to learn from data and continuously improve the performance of the task being monitored. This ismachinelearning a popular type of AI because it does not need to be explicitly programmed to follow a set of rules, it observes and makes its own rules to mimic the process it is completing. Here are some examples of how businesses may use machine learning to streamline their processes:
- Risk Assessment: Specifically in the insurance industry, but applicable to all industries that deal with risk, machine learning leverages algorithms to analyze historical data and predict risks associated with insuring individuals or properties. This helps in determining premiums, underwriting policies, and identifying fraudulent claims.
- Customer Segmentation: Machine learning algorithms can analyze customer data to identify patterns and segment customers based on their behavior, demographics, and preferences. This enables insurance companies to personalize their offerings, target specific customer segments, and improve customer satisfaction.
- Claims Processing: Machine learning algorithms can automate the claims processing workflow by automatically analyzing claim documents, identifying fraudulent claims, and predicting claim outcomes. This helps in streamlining the claims process, reducing costs, and improving efficiency.
2. Deep Learning: Deep learning is a subcategory of machine learning. It uses a more in-depth process with neural networks and multiple layers to extract information and achieve high levels of accuracy for tasks like speech and image recognition. Here are a couple of examples of how businesses use this form of AI:
- Image Analysis: Companies can use deep learning algorithms for image analysis tasks, such as various assessments such as insurance claims or property valuation based on satellite imagery. Deep learning models can accurately analyze and interpret complex visual data, improving the speed and accuracy of these processes.
- Natural Language Processing: Deep learning models can be employed for tasks like sentiment analysis of customer feedback or policy documents, automated document classification and information extraction. These techniques help companies gain insights from unstructured textual data and automate manual tasks.
3. Natural Language Processing (NLP): NLP interprets and generates human language. Tasks it is most commonly used for include machine translation, sentiment analysis and language understanding. Here are a couple of ways NLP can be implemented into business processes:
- Customer Support: NLP-powered chatbots and virtual assistants can handle customer queries and provide instant responses. These systems can understand and generate human-like language, assisting customers with policy inquiries, claims status updates, or general information.
- Policy Analysis: NLP algorithms can analyze policy documents, extracting relevant information and identifying policy terms, conditions, and exclusions. This assists underwriters in assessing risks, comparing policies, and generating accurate quotes.
4. Computer Vision: Computer vision is an interpretation and understanding of images. This can be object detection, image recognition and image segmentation. Here are a few examples of computer vision usage:
- Property Inspection: Computer vision algorithms can analyze images or videos of properties to assess their condition, identify potential risks or damages, and determine insurance coverage or premiums.
- Vehicle Damage Assessment: By analyzing images or videos of damaged vehicles, computer vision can estimate the extent of damage, automate claims processing, and accelerate the settlement process in auto insurance.
5. Robotics: Robotics is not solely AI because it combines with mechanical engineering to develop machines that can physically perform tasks in their environment. An example of robotics businesses could be:
- Claims Investigation: Robots equipped with cameras and sensors can be deployed to assess damages in hazardous or hard-to-reach areas. They can collect data and images, assisting claims adjusters in determining coverage and processing claims.
6. Expert Systems: Expert systems are systems that have encoded information that allows for them to make decisions on different domains, mimicking what the decision-making human would decide. A couple of examples of how expert systems could be used for businesses include:
- Underwriting: Expert systems can capture the knowledge and decision-making rules of experienced underwriters. They assist in evaluating risks, determining coverage, and generating quotes for various insurance products.
- Document Assessment: Expert systems can help businesses by providing guidance and recommendations based on predefined rules and knowledge. This improves the consistency and efficiency of various assessment processes.
7. Reinforcement Learning: Reinforcement learning takes place by allowing the system to learn optimal actions through trial and error. The system receives feedback with penalties or rewards to reinforce their decision-making to improve.
- Pricing Optimization: Companies can use reinforcement learning algorithms to determine optimal pricing strategies by continuously learning from the market dynamics, customer behavior and competitor actions.
8. Generative Adversarial Networks (GANs): GANs are a type of deep learning that have two neural networks that work together to generate synthetic data, such as text and images. An example of how businesses can use GANs is:
- Synthetic Data Generation: GANs can generate synthetic data that mimics real-world scenarios. This can be useful for augmenting training datasets, simulating risk events or testing AI models.
9. Knowledge Representation and Reasoning: Knowledge representation and reasoning focus on organizing information into a structured format to enable reasoning and problem-solving.
10. Explainable AI: Explainable AI is a system that is made to create clear explanations for other AI systems' decisions or predictions. The purpose is to provide transparency and hold AI accountable for its responses.
After reading these most common forms of AI, maybe there's a new idea you've come up with on how to best utilize AI as it evolves. Leave us a comment below on what idea you thought of while reading this article! Many types of AI are related, work together and solve complex problems. There will be more AI uses and types in the future as it is rapidly developing with research processes.
EOX Vantage leverages many different forms of AI to help out clients streamline their workflow and provide accurate, consistent completion of tasks. Contact us today if your company is looking to integrate AI or create faster processes!
