AI

This post is co-written with Vikram Gundeti and Nate Folkert from Foursquare. Personalization is key to creating memorable experiences. Whether it’s recommending the perfect movie or suggesting a new restaurant, tailoring suggestions to individual preferences can make all the difference. But when it comes to food and activities, there’s more to consider than just personal […]

Large language models (LLMs) have become integral to numerous applications across industries, ranging from enhanced customer interactions to automated business processes. Deploying these models in real-world scenarios presents significant challenges, particularly in ensuring accuracy, fairness, relevance, and mitigating hallucinations. Thorough evaluation of the performance and outputs of these models is therefore critical to maintaining trust

Only four of the 21 robots in the race crossed the finish line, highlighting just how far humanoids are from keeping up with their real human counterparts.

When an AI model for code-editing company Cursor hallucinated a new rule, users revolted.

AI agents are revolutionizing how businesses enhance their operational capabilities and enterprise applications. By enabling natural language interactions, these agents provide customers with a streamlined, personalized experience. Amazon Bedrock Agents uses the capabilities of foundation models (FMs), combining them with APIs and data to process user requests, gather information, and execute specific tasks effectively. The

Retrieval Augmented Generation (RAG) enhances AI responses by combining the generative AI model’s capabilities with information from external data sources, rather than relying solely on the model’s built-in knowledge. In this post, we showcase the custom data connector capability in Amazon Bedrock Knowledge Bases that makes it straightforward to build RAG workflows with custom input

In a document published Thursday, ICE explained the functions that it expects Palantir to include in a prototype of a new program to give the agency “near real-time” data about people self-deporting.

In 2000, Patrick J. McGovern ’59 and Lore Harp McGovern made an extraordinary gift to establish the McGovern Institute for Brain Research at MIT, driven by their deep curiosity about the human mind and their belief in the power of science to change lives. Their $350 million pledge began with a simple yet audacious vision:

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash. Some methods exist for ensuring LLMs conform to the rules of whatever language they are generating text