Category: FAANG

We are launching a new set of resources for general-purpose robotics learning across different robot types, or embodiments. Together with partners from 34 academic labs we have pooled data from 22 different robot types to create the Open X-Embodiment dataset. We also release RT-1-X, a robotics transformer (RT) model derived from RT-1 and trained on …

Read more “Scaling up learning across many different robot types”

IT is under enormous pressure. The expectation is 24/7/365 performance while also delivering increasingly better customer experiences at the lowest possible cost. The reality is that it’s difficult to keep apps performing as designed, especially in modern, cloud-native environments with microservices and Kubernetes. Cloud costs are out of control, and teams spend too much time …

Read more “Operationalize automation for faster, more efficient incident resolution at a lower cost”

Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated. However, typical machine learning models are trained to approximately minimize loss over restricted families of predictors, that are unlikely to contain …

Read more “When Does Optimizing a Proper Loss Yield Calibration?”

The recent rise of generative artificial intelligence (AI) including large language models (LLMs) has inspired organizations in every industry to consider how AI can drive innovation. Leaders are increasingly recognizing the power of AI as well as its potential limitations and risks. It’s critical that leaders think carefully about how AI is created and applied and …

Read more “The U.S. Chamber of Commerce Foundation and IBM explore generative AI’s applications for skills-based hiring”