AI

This post is cowritten with Ilija Subanovic and Michael Rice from Workhuman. Workhuman’s customer service and analytics team were drowning in one-time reporting requests from seven million users worldwide—a common challenge with legacy reporting tools at scale. Business intelligence (BI) admins faced mounting pressure as their teams became overwhelmed with these requests. By rebuilding their […]

Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations often struggle with fragmented feature pipelines, inconsistent data definitions, and redundant engineering efforts across teams. Without a centralized system for storing and reusing features, models risk being trained on outdated or

Dozens of Telegram channels reviewed by WIRED include job listings for “AI face models.” The (mostly) women who land these gigs are likely being used to dupe victims out of their money.

EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that you speculate, the more sequential forward passes the drafter needs. Eventually those overhead eats into your gains. P-EAGLE removes this ceiling by generating all K draft tokens in a

Deployed in AWS data centers and accessed through Amazon Bedrock, AWS Trainium + Cerebras CS-3 solution will accelerate inference speed

The company’s generative AI search tools increasingly cite its own services, like Google Search and YouTube, over third-party publishers.

Hype around the open source agent is driving people to rent cloud servers and buy AI subscriptions just to try it, creating a windfall for tech companies.

From the global RAM shortage driving up console prices to job loss in the industry, gaming is shaping up to be one of the AI boom’s biggest casualties.

Software demos and Pentagon records detail how chatbots like Anthropic’s Claude could help the Pentagon analyze intelligence and suggest next steps.

<!– –> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze