AI

VCs are betting that artificial intelligence will disrupt nearly every industry in the world. Are they prepared for it to disrupt their own?

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use

Deveillance’s Spectre I, developed by a recent Harvard grad, wants to give people control over the always-on wearables surrounding their lives. The problem? Physics.

I stuck Amazon’s Echo Show 15 and its Alexa+ AI assistant in my kitchen for a month. Things have not gone well.

In an exclusive interview with WIRED, Block’s cofounder and CEO says he axed 40 percent of his workforce so that he can rebuild the company “as an intelligence.”

In this episode, our hosts unpack the ongoing conflict in the Middle East, particularly as the AI industry has been entrenching itself with the Department of Defense.

Sources allege the Defense Department experimented with Microsoft’s version of OpenAI technology before the ChatGPT-maker lifted its prohibition on military applications.

ByteDance’s new Seedance 2.0 AI video model seemed unstoppable—until heavy demand strained the company’s compute capacity and copyright complaints began piling up.

As your conversational AI initiatives evolve, developing Amazon Lex assistants becomes increasingly complex. Multiple developers working on the same shared Lex instance leads to configuration conflicts, overwritten changes, and slower iteration cycles. Scaling Amazon Lex development requires isolated environments, version control, and automated deployment pipelines. By adopting well-structured continuous integration and continuous delivery (CI/CD) practices,

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain greater control over their deployments, optimize costs, and align with compliance requirements. However, this flexibility introduces a critical technical challenge: response format incompatibility with Strands agents. While these custom