Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When Snowflake announced its $250 million acquisition of Crunchy Data two weeks ago at its ...

With archives hosting about 180 million works, the world’s largest library is drawing interest from AI startups looking to train their large language models on content that won’t get them sued. Black ...

Central to successful AI implementations is establishing a robust tech stack to support its demands. When it comes to the database layer, the choice between traditional relational databases, vector ...

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now In 2014, a breakthrough at Google ...

When Aquant Inc. was looking to build its platform — an artificial intelligence service that supports field technicians and agents teams with an AI-powered copilot to provide personalized ...

Chang She, previously the VP of engineering at Tubi and a Cloudera veteran, has years of experience building data tooling and infrastructure. But when She began working in the AI space, he quickly ran ...

The graph database market, driven by AI, is growing at a rate of almost 25% annually. Graph databases support knowledge graphs, providing visual guidance for AI development. There are multiple ...

Artificial intelligence (AI) processing rests on the use of vectorised data. In other words, AI turns real-world information into data that can be used to gain insight, searched for and manipulated.

Even though traditional databases now support vector types, vector-native databases have the edge for AI development. Here’s how to choose. AI is turning the idea of a database on its head.

Did you know that over 80% of the data generated today is unstructured? Traditional databases often fall short in managing this type of data efficiently. That’s where vector databases come into play.

Many of the tools below can be used in research to assist with finding, tracking, and summarizing scholarly sources. Many of these are paid tools that offer a free version with limited functionality.