While Large Language Models (LLMs) like LLama 2 have shown remarkable prowess in understanding and generating text, they have a critical limitation: They can only answer questions based on single ...

The problem: Generative AI Large Language Models (LLMs) can only answer questions or complete tasks based on what they been trained on - unless they’re given access to external knowledge, like your ...

We are in an exciting era where AI advancements are transforming professional practices. Since its release, GPT-3 has “assisted” professionals in the SEM field with their content-related tasks.

Ever since large language models (LLMs) exploded onto the scene, executives have felt the urgency to apply them enterprise-wide. Successful use cases such as expedited insurance claims, enhanced ...

The initial surge of excitement and apprehension surrounding ChatGPT is waning. The problem is, where does that leave the enterprise? Is this a passing trend that can safely be ignored or a powerful ...

Generative AI depends on data to build responses to user queries. Training large language models (LLMs) uses huge volumes of data—for example, OpenAI’s GPT-3 used the CommonCrawl data set, which stood ...

Wikidata has built the semantic web backbone supporting knowledge cards in popular engines. Now, it's extending this foundation using a vector database to enhance its existing knowledge graph and ...

Knowledge graph startup Diffbot Technologies Corp., which maintains one of the largest online knowledge indexes, is looking to tackle the problem of hallucinations in artificial intelligence chatbots ...

The latest trends in software development from the Computer Weekly Application Developer Network. The original title in full for this piece is: From Better Reasoning to Faster QFS, An LLM Just Can’t ...