Although it’s rarely publicized in the media, not everything about deploying—and certainly not about training or fine-tuning—advanced machine learning models are readily accessible through an… Read More »Underpinning advanced machine learning models with GPUs
Most enterprise applications of Artificial Intelligence are predicated on understanding, and generating, natural language in the form of text. Deepgram, an AI platform for generating… Read More »Deepgram builds and hosts ‘Voice AI’ models
The synthesis of generative machine learning models and digital agents, which many have dubbed Agentic AI, may well prove the most advantageous development for enterprise-spanning… Read More »Agentic AI in motion: Smart command implementation for document management
Whether rendering sensitive datasets anonymous with synthetic data or simply facilitating ad-hoc question answering on a corpus of domain-specific knowledge, enterprise applications of AI are… Read More »Infrastructural necessities for building and deploying enterprise-scale AI
The defensive security posture underpinned by statistical AI applications is formidable. Organizations can analyze massive quantities of data to determine patterns indicative of cyber attacks,… Read More »Best practices for AI-based security for 2025
The utility derived from the Generative Adversarial Network (GAN) approach to advanced machine learning is less celebrated than that gained from its language model counterparts.… Read More »Deconstructing Generative Adversarial Networks and synthetic data
The Retrieval Augmented Generation (RAG) paradigm has popularized the notion of supplementing prompts for GenAI models with trusted enterprise data. Many organizations utilize this practice… Read More »Revamping prompt augmentation: Multi-agent architecture and LangGraph
