Output evaluation is the process through which the functionality and efficiency of AI-generated responses are rigorously assessed against a set of predefined criteria. It ensures that AI systems are not only technically proficient but also tailored to meet the nuanced demands of specific...
Acronyms allow us to compact a wealth of information into a few letters. The goal of such a linguistic shortcut is obvious – quicker and more efficient communication, saving time and reducing complexity in both spoken and written language. But it comes at a price – due to their condensed nature...
Effective data management is crucial for the optimal performance of Retrieval-Augmented Generation (RAG) models. Duplicate content can significantly impact the accuracy and efficiency of these systems, leading to errors in response to user queries. Understanding the repercussions of duplicate...
Deep learning vs. traditional machine learning: Which model is right for your needs? Each approach has its unique strengths and applications, but there are key differences between deep learning and traditional machine learning. Traditional Machine Learning Explained Traditional machine learning...
In the banking sector, every percentage point in efficiency can translate to billions in revenue. According to McKinsey, GenAI could potentially add $340 billion in revenue to the sector’s annual global revenues. This represents a 4.7% increase in total industry revenues – a surge comparable...
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models (LLMs) by pulling from vast sources of external data, they are not immune to the pitfalls of inaccurate or outdated information.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...
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...
Augmented Shelf | Issue 2 | March 19, 2024 Welcome to Augmented Shelf, a wrap-up of the week’s AI news, trends and research that are forging the future of work. Evil Geniuses Vs. ChatDev To evaluate the vulnerability of LLM-based agents, researchers at Tsinghua University in Beijing, China, have...
Large Language Models (LLMs) rely on extensive memory to store and manipulate vast datasets, a key factor that allows them to learn from past inputs and improve their linguistic abilities over time. But what if, alongside remembering, LLMs could also benefit from adaptive forgetting? The notion of...
Welcome to Augmented Shelf, a wrap-up of the week’s AI news, trends and research that are forging the future of work. Is Claude 3 Opus Self-Aware? In a remarkable display of potential self-awareness, Anthropic’s newly released Claude 3 Opus AI showcased an unexpected response during an...