Measuring Gen AI responses – Challenges Generative AI, with its ability to create novel content, faces unique challenges when it comes to measuring its performance. In this post, we’ll explore key unresolved issues and strategies for evaluating generative AI systems. 1. Lack of Ground Truth Establishing a definitive “ground truth” for evaluating generative AI output can […]
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Unleashing the Power of Large Language Models In recent years, Large Language Models (LLMs) have emerged as game-changers in natural language processing. These models, pretrained on vast amounts of text data, exhibit remarkable capabilities, from text generation to sentiment analysis. In this article, we delve into various LLM architectures and sample some applications they enable.
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Effective Workarounds for Common Gen AI Limitations Introduction Generative AI has transformed various industries with its ability to generate human-like text, create content, and perform complex tasks. However, it comes with its own set of limitations. In this blog post, we’ll explore these limitations and discuss effective workarounds to enhance the performance and reliability of
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This is an intuitive framework for Gen AI use cases. Generative AI (Gen AI) has a wide range of capabilities that can be applied to various artefacts, making it a versatile tool for numerous applications. Here are some of the key capabilities of Gen AI: Gen AI can handle a variety of artefacts, including text
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Gen AI is obviously very popular. Must be due to the benefits possible from usage. Based on reports, these are the top impacts sought. Let’s explore each of these impacts areas very briefly. These show why Gen AI is a big deal everywhere. By enabling such a wide variety of impacts, it is quickly becoming
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Generative AI is not the result of a single breakthrough but rather a journey enabled by a series of significant advancements. From the early development of deep learning and transfer learning to the transformative impact of the transformer architecture and the evolution of large language models like GPT, each milestone has played a crucial role
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Scalability Issues: Scaling generative AI solutions can be challenging due to the need for large datasets and extensive computational resources. For example, deploying AI at scale in real-time applications can be technically and financially demanding. LLM-Specific Limitations: Large Language Models (LLMs) like GPT-3 can produce plausible-sounding but factually incorrect or nonsensical answers, and they may
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Following are some of the limitations of Gen AI Knowledge Cutoff: Generative AI models can’t access information beyond their training data, limiting their ability to provide up-to-date responses. For example, a model trained before 2023 won’t know about events or developments after that year. Training Data Dependency: The quality and bias of outputs are heavily
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Here are some popular types of generative AI architectures:
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Non-Generative AI, or traditional AI, focuses on analyzing and interpreting existing data rather than creating new content. These models excel at recognizing patterns, making predictions, and classifying data. Common applications include image recognition, natural language processing (NLP), recommendation systems, and fraud detection, using models like Decision Trees, SVMs, and CNNs.
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