Which best describes the different between predictive AI and generative AI?
''The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques to generate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos.''
What are the key components of the data quality standard?
''Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.''
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?
Incomplete data quality negatively impacts the accuracy of product recommendations made by Cloud Kicks. If data is missing or incomplete, the AI models used for product recommendation may not have enough information to accurately predict customer preferences and behavior. This leads to recommendations that may not align well with customer needs, reducing customer satisfaction and potentially affecting sales. Ensuring complete and accurate data is crucial for effective recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and strategies to enhance data integrity in their documentation on AI and data management, which can be referenced at Data Management for AI.
What is Salesforce's Trusted AI Principle of Transparency?
Salesforce's Trusted AI Principle of Transparency emphasizes the importance of providing clear and understandable explanations of AI decisions and actions. This principle ensures that users can understand how AI conclusions are drawn, which is crucial for trust and accountability, especially in business applications where AI decisions can have significant impacts. Transparency helps mitigate the 'black box' nature of AI systems by making them more interpretable and allows for better oversight, compliance, and alignment with ethical guidelines. Salesforce elaborates on these principles in their ethical AI practices, which can be further explored at Salesforce Ethical AI.
Ciara
28 days agoLizette
2 months agoMagda
3 months agoEllsworth
3 months agoTemeka
4 months agoAnnelle
4 months agoCassi
4 months agoBrittani
5 months agoRoyce
5 months agoLarae
5 months agoWai
6 months agoMeghan
6 months agoAja
6 months agoMarleen
7 months agoDalene
7 months agoDannette
7 months agoAnnita
7 months agoCiara
8 months agoCatrice
8 months agoMargarita
9 months agoVinnie
10 months agoGerman
10 months agoHerschel
10 months agoLouann
11 months agoLindsey
11 months agoRoy
11 months agoKimbery
11 months ago