Analysis and prediction: rely on easier-to-understand AI
Posted: Tue Jan 21, 2025 4:56 am
PR_Why businesses are turning to AI as we head into 2023_Analysis n Prediction_v02
The use of AI technology for data analysis has become very common, but how AI generates insights and the reasons behind its predictions are still a mystery. Although more and more business executives are more willing to adopt AI solutions, some still have questions about how and why AI models make specific conclusions, suggestions, and recommendations.
In order to make the "black box" of AI visible, many AI technology companies have developed explainable AI systems and solutions. Explainable AI (Explainable AI), as the name suggests, aims to explain the reasoning austria phone number data process by which AI makes specific decisions, recommendations or predictions . This information can help humans further understand AI and its "thinking process", making people involved in its development or obtaining results more willing to trust AI's choices.
Appier currently has multiple explainable AI solutions. Appier is committed to using AI to improve marketing accuracy and effectiveness for customers. In order to enable our customers to better understand the reasons behind the specific results produced by AI, we are also actively strengthening the explainability of AI models. For example, Appier’s many solutions include: how the AIQUA AI Personalized Marketing Cloud decides to recommend specific content to a certain user, or how the AiDeal Conversion Optimization Marketing Cloud classifies a customer as a hesitant customer and sends a coupon to the customer. The purpose is to help customers further understand the "thinking process" of AI-generated results.
These are all questions that an increasing number of users of AI solutions want answers to. The reasoning is that if companies want to save money and therefore only consider solutions that ensure the desired results, then explainable AI is the decision Provide concrete evidence for researchers to evaluate whether AI solutions are effective.
The use of AI technology for data analysis has become very common, but how AI generates insights and the reasons behind its predictions are still a mystery. Although more and more business executives are more willing to adopt AI solutions, some still have questions about how and why AI models make specific conclusions, suggestions, and recommendations.
In order to make the "black box" of AI visible, many AI technology companies have developed explainable AI systems and solutions. Explainable AI (Explainable AI), as the name suggests, aims to explain the reasoning austria phone number data process by which AI makes specific decisions, recommendations or predictions . This information can help humans further understand AI and its "thinking process", making people involved in its development or obtaining results more willing to trust AI's choices.
Appier currently has multiple explainable AI solutions. Appier is committed to using AI to improve marketing accuracy and effectiveness for customers. In order to enable our customers to better understand the reasons behind the specific results produced by AI, we are also actively strengthening the explainability of AI models. For example, Appier’s many solutions include: how the AIQUA AI Personalized Marketing Cloud decides to recommend specific content to a certain user, or how the AiDeal Conversion Optimization Marketing Cloud classifies a customer as a hesitant customer and sends a coupon to the customer. The purpose is to help customers further understand the "thinking process" of AI-generated results.
These are all questions that an increasing number of users of AI solutions want answers to. The reasoning is that if companies want to save money and therefore only consider solutions that ensure the desired results, then explainable AI is the decision Provide concrete evidence for researchers to evaluate whether AI solutions are effective.