AI

A viral red carpet moment shone light on a group of hunky Instagram influencers—and the followers who are too horny to care that they’re not real.

As people increasingly rely on AI chatbots for guidance, even on financial matters, a healthy dose of skepticism is critical.

In this week’s episode of Uncanny Valley, we talk about Tim Cook’s legacy as CEO at Apple and what his long-rumored departure means for the future of one of the world’s biggest companies.

CS 153 has gone viral on the Palo Alto campus—and on X. Not everyone is happy about it.

Imagine the following scenario: You’re leading marketing campaigns, creating content, or driving demand generation. Your campaigns are scattered and your insights are buried. By the time you’ve pieced together what’s working, the moment to act has already passed. This isn’t a tools problem because you have plenty of those. It’s a connection problem. Your marketing

Healthcare and life sciences decision making increasingly relies on multimodal data to diagnose diseases, prescribe medicine and predict treatment outcomes, develop and optimize innovative therapies accurately. Traditional approaches analyze fragmented data, such as ‘omics for drug discovery, medical images for diagnostics, clinical trial reports for validation, and electronic health records (EHR) for patient treatment. As

“To be clear, we were never approached … nor were we in any discussions regarding a partnership or tour access,” a spokesperson for the artist tells WIRED.

Many organizations are archiving large media libraries, analyzing contact center recordings, preparing training data for AI, or processing on-demand video for subtitles. When data volumes grow significantly, managed automatic speech recognition (ASR) service costs can quickly become the primary constraint on scalability. To address this cost-scalability challenge, we use the NVIDIA Parakeet-TDT-0.6B-v3 model, deployed through

Organizations are racing to deploy generative AI models into production to power intelligent assistants, code generation tools, content engines, and customer-facing applications. But deploying these models to production remains a weeks-long process of navigating GPU configurations, optimization techniques, and manual benchmarking, delaying the value these models are built to deliver. Today, Amazon SageMaker AI  supports

Confidence is persuasive. In artificial intelligence systems, it is often misleading. Today’s most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they’re right or guessing. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence