Generative AI | News, how-tos, features, reviews, and videos
Mistral Large is a large language model optimized to handle high-complexity tasks that require advanced reasoning and multilingual capabilities.
Microsoft’s toolkit for building generative AI applications goes beyond function calling to generate its own plans, using LLMs and templates to fulfill user requests. And it’s reasonably easy to learn and use.
Until CIOs are ready to confront data that is siloed, redundant, or can’t be traced through the business process, generative AI will not pay off.
Free open-source framework gives Go developers a unified generation API, native vector database support, and composable abstractions that simplify the development of AI workflows.
The tools tackle the thorny issue of code translation across both written languages and programming languages.
Rival software providers, Zendesk and ServiceNow, have already introduced similar capabilities.
The new large language model has been made available under the Apache 2.0 license, the French AI startup said.
Data analytics and machine learning can deliver real business value, but too many projects miss their mark. Here are seven mistakes to watch out for, and what to do instead.
For those who have done the real work of data modernization and preparation, AI is worth its high price tag.
You’ve probably heard how generative AI will solve all cloud migration problems. It’s not that simple. Generative AI could actually make it harder and more costly.