An inmate at a New York correctional facility, Glenn Rodriguez, was due for parole soon. The man had demonstrated excellent behavior and eagerly anticipated his release to embark on a fresh start. To Glenn’s dismay, the board denied him parole.

TL;DR Many companies rush into generative AI without realizing just how steep—and unpredictable—the associated expenses can be. Meanwhile, the cost of implementing generative AI in business can range from $0.0005 per 1,000 text units (tokens or characters) if you use a popular commercially available Gen AI tool to over $190,000 for a fully custom solution based on a fine-tuned open-source model.

Edge AI brings artificial intelligence to local devices, deploying lightweight models directly on sensors, cameras, and IoT equipment instead of relying on cloud servers.

Artificial intelligence (AI) promises sharper insights, faster decisions, and leaner operations. Every organization wants in. But AI thrives on data. Yet most of that data is sensitive, and global regulations like GDPR, HIPAA, and CCPA are tightening the rules on how it’s handled.

Retrieval-augmented generation (RAG) is an AI architecture that enhances LLM accuracy by dynamically retrieving relevant, up-to-date information from external knowledge sources RAG significantly reduces hallucinations and improves response accuracy in critical domains like healthcare (96% diagnostic accuracy) and legal (38-115% productivity gains)