Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
The adage “Garbage In, Garbage Out” (GIGO) holds a pivotal truth throughout all of computer science, but especially for data analytics and artificial intelligence. This principle underscores the fundamental idea that the quality of the output is linked to the quality of the input. As...
The AI Weekly Breakthrough | Issue 8 | May 1, 2024 Welcome to The AI Weekly Breakthrough, a roundup of the news, technologies, and companies changing the way we work and live Even LLMs Get the Blues Findings from a new study using the LongICLBench benchmark indicate that LLMs may “get the...
Poor data quality is the largest hurdle for companies who embark on generative AI projects. If your LLMs don’t have access to the right information, they can’t possibly provide good responses to your users and customers. In the previous articles in this series, we spoke about data enrichment,...
While Retrieval-Augmented Generation (RAG) significantly enhances the capabilities of large language models (LLMs) by pulling from vast sources of external data, they are not immune to the pitfalls of inaccurate or outdated information. In fact, according to recent industry analyses, one of the...
The AI Weekly Breakthrough | Issue 7 | April 23, 2024 Welcome to The AI Weekly Breakthrough, a roundup of the news, technologies, and companies changing the way we work and live Mona Lisa Rapping: Microsoft’s VASA-1 Animates Art Researchers at Microsoft have developed VASA-1, an AI that...
It’s estimated that $1 trillion in healthcare spending is wasted each year in the U.S. By automating routine tasks and making more use of clinical data, GenAI is a new opportunity to optimize healthcare expenditures and unlock part of the money lost to inefficiencies. It could organize...
Large language models are skilled at generating human-like content, but they’re only as valuable as the data they pull from. If your knowledge source contains duplicate, inaccurate, irrelevant, or biased information, the LLM will never behave optimally. In fact, poor data quality is so inhibiting...
AI hallucinations refer to instances where AI systems, particularly language models, generate outputs that are inconsistent, nonsensical, or even entirely fabricated. This issue is especially prevalent in AI systems that rely on external data sources, such as Retrieval-Augmented Generation (RAG)...
While large language models excel in mimicking human-like content generation, they also pose risks of producing confusing or erroneous responses, often stemming from poor data quality. Poor data quality is the primary hurdle for companies embarking on generative AI projects, according to...
Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be harmful, biased, or even nonsensical. The cause? Poor data quality. According to a poll of IT leaders by...
The AI Weekly Breakthrough | Issue 6 | April 17, 2024 Welcome to The AI Weekly Breakthrough, a roundup of the news, technologies, and companies changing the way we work and live Meta Breaks Silence, Announces Llama 3 Meta, relatively quiet as of late, breaks its silence this week to make two...
Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach in artificial intelligence (AI) that blends human intelligence with machine learning to teach computers how to perform complex tasks. This method is particularly exciting because it represents a shift from traditional...