Generative AIGenerative AI hallucinations: Why they occur and how to prevent them
Hallucinations are an obstacle to building user trust in generative AI applications. Learn about the phenomenon, including best practices for prevention.
Generative AIGenerative AI survey results: Customers expect brands to be transparent
A survey conducted by TELUS Digital unveiled consumer concerns about misinformation and bias in generative AI. Learn more.
Generative AIThree ways RLHF is advancing large language models
Learn how reinforcement learning from human feedback helps AI models respond to complex human preferences, reduce bias and decrease the occurrence of hallucinations.
Data CollectionData governance with AI data collection
Robust data governance not only improves data quality, it offers a standard to ensure that data is consistent, complete and accurate. Find out more.
Generative AIArtificial intelligence, real benefits: Applying generative AI in CX
Generative AI has seemingly limitless potential for brands who lead with their digital CX. Investigate the benefits of AI customer service.
Generative AIGenerative AI 101
Generative AI is a type of AI that can be used to generate new content such as text, images, audio, video, code or synthetic data. Discover exactly how generative AI works and what you can do with it.
Responsible AIThe critical role of impact sourcing on AI model expansion
Addressing bias in AI models is a key principle of responsible AI. Discover how diversifying your team of AI contributors through impact sourcing can help.
Linguistic AnnotationAre we headed for an AI data shortage?
Discover how a potential AI training data shortage for language models could affect your business, and what you can do to offset its impact.
AI Use CasesFinancial Services & FintechSix AI technologies set to define the future of banking
Artificial intelligence is set to define the future of banking. Learn about how banks are using machine learning, RPA and other AI-powered tech.
Data AnnotationWhat is data classification?
AI data demands classification to be useful at scale. Learn the benefits, challenges and best practices of data classification.
Data AnnotationFive common data annotation challenges and how to solve them
Data annotation can create bottlenecks in AI and machine learning projects. Learn how to improve data quality and overcome data annotation challenges.
AI Best PracticesQuality assurance best practices for AI training data
Training AI systems demands observing data quality best practices. Learn more about AI data quality control in this primer.
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