#machine-learning

#machine-learning

Wikimedia is releasing a dataset for AI training to curb Wikipedia scraping.

Partnership with Kaggle aims to provide structured data for AI model training.

The dataset includes well-structured information while reducing server load from AI bots.

Yann LeCun argues against the belief that larger AI models always lead to smarter AI, highlighting the need for different approaches.

AI is significantly transforming various fields, including engineering, law, and healthcare, through innovative applications and responsible practices.

Agentic AI transforms from a reactive tool to an independent operator capable of decision-making and action.

Vector embeddings require effective management systems to realize their potential in AI.

Vector Search & Management (VS&M) is crucial for value-driven data utilization.

Efficient conversation data management relies on a comprehensive and adaptive label taxonomy.

AI credits facilitate access to essential tools for data science teams, enhancing innovation and cost-effectiveness.

Onehouse's Open Engines capability breaks the link between compute and storage, enhancing open data utilization.

ODSC East 2025 will highlight key AI trends, innovations, and skills development, showcasing the future direction of AI technology.

A modern recommendation engine can predict customer preferences by integrating user behavior and product data.

Mastering Large Language Models is critical for engineers in today's rapidly evolving AI landscape.

ODSC East provides expert-led sessions for both beginners and advanced users in LLM application.

Real-world development workflows and governance are key to successful LLM integration across teams.

Reliability in text classification is crucial and can be assessed using multiple annotators and LLMs to align with human consensus.

The article introduces a new evaluation suite for taxonomy generation and text classification using a combination of evaluation strategies.

Creating your own large language model can be beneficial for tailored business solutions despite existing models' reliability.

Serverless machine learning on Kubernetes enables scalable and flexible deployment without server management issues.

Serverless ML on Kubernetes offers scalability and flexibility, enabled by frameworks like KServe.

The study presents a new NLP pipeline that efficiently constructs knowledge graphs from unstructured scientific texts by fine-tuning language models with minimal data.

Multi-Token Attention allows transformers to attend to groups of tokens, enhancing model performance in natural language processing.

Multi-Token Attention revolutionizes transformers by enabling simultaneous attention to groups of tokens, enhancing contextual understanding.

Darktrace's new AI models enhance proactive threat investigation and prevention in cybersecurity.

The article discusses the training and evaluation of various LLM models with a focus on NER and internal relation recognition.

Large language models learn language similarly to children: through reading, guidance, and feedback.

Docker Model Runner enables efficient local LLM integration for developers, enhancing privacy and control without disrupting workflows.

Docker Model Runner eases the serving of ML models locally without complex setups.

Docker's Model Runner simplifies serving ML models locally as REST APIs without any complex setup.

Privacy is a fundamental human right and is enforced through various international laws.

Using a fine-tuned language model can improve privacy risk assessment in data anonymization through effective sequence labeling.

OpenAI's new models, o3 and o4-mini, incorporate visual reasoning, enhancing AI understanding of images alongside text.

OpenAI's GPT-4.1 model significantly improves coding capabilities and supports long input prompts for better contextual processing.

OpenAI's GPT-4.1 models outperform previous versions in performance and cost efficiency.

OpenAI's new models o3 and o4-mini enhance reasoning capabilities, offering greater efficiency and performance.

AI agents must evolve to outperform humans in speed and accuracy.

Real-time data extraction is crucial for AI agents to succeed online.

Marketers should critically assess AI tools in martech to distinguish genuine innovation from rebranded automation.

OpenAI's new models o3 and o4-mini show increased tendency to hallucinate, reversing a positive trend in model development.

Wikipedia is providing a structured dataset for AI developers in response to server strain caused by bots.

The new dataset aims to relieve bandwidth consumption and improve human user experience.

Meta's internal legal filings reveal how specific training data can enhance AI models, utilizing a technique called ablation.

Generic machine learning applications are ruled ineligible for patents unless they show model improvements.

The Federal Circuit upheld the invalidation of Recentive's patent claims, deeming them unpatentable due to their generic application of machine learning.

The CAFC ruled that applying established machine learning methods to new environments doesn't qualify for patent eligibility.