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Boost client interaction with the adaptation of LLM through no-code using Amazon’s SageMaker Canvas and SageMaker JumpStart.

Amazon SageMaker Canvas and Amazon SageMaker JumpStart have brought a new level of accessibility to fine-tuning large language models (LLMs), allowing businesses to tailor customer experiences precisely to their unique brand voice. No coding is needed for this process, as SageMaker Canvas provides a user-friendly, point-and-click interface. This not only allows faster operation but also…

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AWS DeepRacer permits developers of all expertise levels to improve their skills and begin with machine learning.

The growing accessibility of artificial intelligence (AI) and machine learning (ML) technology is prompting the need for both technical and non-technical teams to understand their application. Practical, hands-on training is vital to this learning process, and AWS DeepRacer presents an innovative and engaging way to teach ML fundamentals. Launched in 2019, AWS DeepRacer is a…

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Item#38 – Virtual Reality, Casual Conversations, and Irremediable Deceptions of ChatGPT

This week's AI news roundup includes stories about advancements in AI and VR leading to increased user isolation, OpenAI's struggle with keeping its models truthful, key businesses poaching AI talent from rival companies, and the potential risks AI poses to scientific integrity. Increasingly, users are leveraging advanced AI and VR technologies to craft their personalized virtual…

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Does a Library Exist for Data Cleaning Prior to Tokenization? Introducing the Unstructured Library for Effortless Pre-Tokenization Purification.

The process of data cleaning is a crucial step in Natural Language Processing (NLP) tasks, particularly before tokenization and when dealing with text data that contains unusual word separations like underscores, slashes, or other symbols in place of spaces. The reason for its importance is that tokenizers often depend on spaces to split text into…

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AnchorGT: An Innovative Attention Mechanism for Graph Transformers Providing a Versatile Component to Enhance Scalability Across Various Graph Transformer Models

The standard Transformer models in machine learning have encountered significant challenges when applied to graph data due to their quadratic computational complexity, which scales with the number of nodes in the graph. Past efforts to navigate these obstacles have tended to diminish the key advantage of self-attention, which is a global receptive field, or have…

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