Small Language Models (SLMs) are gaining traction as a cost-effective alternative to large models. They offer improved accuracy, reduced costs, and greater control over data, making them a compelling option for businesses looking to optimize performance.
Understanding loss functions is crucial for training neural networks. Cross-entropy helps quantify differences in probability distributions, aiding in model selection.
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New president of the Royal Society for Blind Children urges improved design of AI tech to include visually impaired individuals, highlighting discrimination concerns. Tom Pey emphasizes the need for better accessibility in video games and AI agents for blind children to prevent exclusion from technological advancements.
Guardian tests show OpenAI's ChatGPT search tool can return false/malicious results with hidden text, raising security concerns. Users warned of potential risks with new AI-powered search product.
Businesses are reducing costs by fine-tuning LLMs with PEFT techniques like LoRA. AWS's SageMaker HyperPod simplifies distributed training for efficient AI development.
RLHF improves LLM training by incorporating human feedback to reduce bias and toxicity in model outputs. OpenAI's InstructGPT and ChatGPT show promising results with RLHF, enhancing truthfulness and reducing toxic output generation.
Lettria, an AWS Partner, shows integrating graphs into RAG workflows can boost answer precision by up to 35%. GraphRAG improves accuracy by capturing complex human queries and avoiding loss of context in data representation.
Machine learning models can provide prediction intervals to account for uncertainty in outcomes, aiding in making well-informed decisions. Conformal prediction offers insightful prediction intervals with weak theoretical guarantees, enhancing the accuracy of forecasts.
AI images of Pope Francis embracing Madonna have gone viral, sparking controversy over the use of deepfake technology in creating AI art. The debate highlights ethical concerns surrounding the pontiff's unwitting involvement in symbolic digital creations.
PydanticAI introduces an evaluation-driven approach to developing agentic applications, addressing challenges like non-determinism and LLM limitations. The framework allows for mock dependencies, enabling developers to build evaluation-driven applications efficiently.
Nature image datasets, with millions of photos, aid ecologists in studying behaviors and responses to climate change. Multimodal vision language models can improve image retrieval for researchers, but need more domain-specific training data for complex queries.
New approach LEC effectively classifies content safety violations and prompt injection attacks using hidden states from intermediate Transformer layers. LEC outperforms special-purpose models and GPT-4o, offering a lightweight and efficient solution for businesses to protect against model manipulation.
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Large language models (LLMs) require well-curated datasets for optimal performance. Data preprocessing involves extracting text from diverse sources and filtering for quality, using tools like OCR and regex filters.