AI

Today on Uncanny Valley, we’re diving into recent reports that the Trump administration is considering an executive order that would establish some sort of federal oversight over new AI models.

Chrome users were caught off guard by a 4-GB Google AI model baked into Chrome, sparking privacy concerns. The good news: You can easily uninstall it. The bad? You might not want to.

OpenAI’s chatbot has some weird linguistic tics in Chinese that are driving users crazy.

As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide supply. This imbalance has made GPUs a scarce resource, creating a challenge for customers who need reliable access to GPU compute resources for their ML workloads. When you encounter

Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences how models learn and make decisions. However, creating robust feedback mechanisms can be complex and error prone. Real-world training scenarios often introduce hidden biases, unintended incentives, and ambiguous success

In a recent interview, Jassy explained the long-term approach to Amazon’s capital expenditures—and why you have to invest in momentous shifts.

We’re in the midst of a fundamental shift in how software gets built and used. AI agents are moving beyond assistants that wait for instructions. They call APIs, access MCP servers, coordinate with other agents, and complete complex multi-step tasks on behalf of users. As agents take on increasingly diverse tasks, the ecosystem around them

Companies like Lovable, Base44, Replit, and Netlify use AI to let anyone build a web app in seconds—and in thousands of cases, spill highly sensitive data onto the public internet.

When Stick Figure’s six-year-old song shot up the charts, the band was thrilled. But its viral moment was spurred by unauthorized AI remixes.

When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980.  Rather than implement automation in pursuit of