AI

The AI arms race between China and the US has researchers on both sides worried about a “Chernobyl moment.”

When your document repository contains hundreds of millions of files accumulated over nearly a decade, how do you systematically find and redact sensitive customer data without taking years to complete? This was the challenge facing The Huntington National Bank (Huntington), a top 10 bank in the United States. Redacting sensitive information at scale Since 2015,

If you run a clinic or hospital network, you already know the cost of missed appointments. The average no-show rate across US healthcare sits between 5–30 percent, depending on specialty. Each empty slot represents lost revenue, idle provider time, and delayed patient care. The standard fix, calling patients one by one to confirm or reschedule,

One dashboard shows 42,000 active movie view counts while another shows 38,500. Your chat agent references a third number entirely. Data teams spend hours reconciling numbers instead of answering strategic questions, and trust in analytics erodes. This is a pattern that we see across many organizations. Teams spend more effort reconciling numbers than actually using

Loka transformed customer voice interactions by building a conversational AI agent with Amazon Nova 2 Sonic that keeps customers engaged with natural, responsive experiences. Their AWS-based solution achieves high speech reasoning accuracy on Big Bench Audio while delivering significantly lower costs and faster response times than traditional voice AI pipelines. In this post, we demonstrate

At high-stakes meetings with the White House, Anthropic’s CEO—a “weirdo,” per one official—has been replaced by cofounder Tom Brown.

Modular, one of the most promising chip software startups of the AI era, heads for a multibillion-dollar exit.

In a new interview, Garman explains why enterprises are moving past AI experimentation—and what’s driving real business value.

At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics. The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial

Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find structurally similar candidates is slow, error-prone, and requires deep domain expertise to interpret results. Building a protein research copilot can transform how researchers search for structurally similar peptides across large datasets — enabling natural language queries, automated embedding generation, and