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The success of any machine learning (ML) pipeline depends not just on the quality of model used, but also the ability to train and iterate upon this model. One of the key ways to improve an ML model is by choosing better tunable parameters, known as hyperparameters. This is known as hyperparameter optimization (HPO). However, …
Editor’s note: This post is part of an ongoing series on IT predictions from Google Cloud experts. Check out the full list of our predictions on how IT will change in the coming years. Prediction: AI will be the primary driver for moving to a 4-day work week Enterprise use of artificial intelligence (AI) has …
Read more “IT prediction: AI could help realize the dream of the four-day work week”
Training, testing and validating autonomous vehicles requires a continuous pipeline — or data factory — to introduce new scenarios and refine deep neural networks. A key component of this process is simulation. AV developers can test a virtually limitless number of scenarios, repeatably and at scale, with high-fidelity, physically based simulation. And like much of …
Read more “Hittin’ the Sim: NVIDIA’s Matt Cragun on Conditioning Autonomous Vehicles in Simulation”
This paper was accepted at the workshop “Learning from Time Series for Health” at NeurIPS 2022. Heart rate (HR) dynamics in response to workout intensity and duration measure key aspects of an individual’s fitness and cardiorespiratory health. Models of exercise physiology have been used to characterize cardiorespiratory fitness in well-controlled laboratory settings, but face additional …
Read more “Modeling Heart Rate Response to Exercise with Wearable Data”
Today, we are excited to release optimizations to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, along with code to get started with deploying to Apple Silicon devices. Figure 1: Images generated with the prompts, “a high quality photo of an astronaut riding a (horse/dragon) in space” using Stable Diffusion and Core …
The post A conversation with Kevin Scott: What’s next in AI appeared first on The AI Blog.
Posted by Katja Filippova, Research Scientist, and Sebastian Ebert, Software Engineer, Google Research, Brain team Modern machine learning models that learn to solve a task by going through many examples can achieve stellar performance when evaluated on a test set, but sometimes they are right for the “wrong” reasons: they make correct predictions but use …
With the growth and popularity of online social platforms, people can stay more connected than ever through tools like instant messaging. However, this raises an additional concern about toxic speech, as well as cyber bullying, verbal harassment, or humiliation. Content moderation is crucial for promoting healthy online discussions and creating healthy online environments. To detect …
Out of necessity, the life sciences industry has accelerated the innovation and experimentation of drug and device development. The sector, which has traditionally been slow-moving when it comes to clinical trials—for reasons ranging from regulatory, to trial recruitment, to quality control—is now looking towards cloud technology to speed up the process and find new innovative …
Read more “Data: the Rx to faster, patient-centric clinical trials”
Operational Security: Enabling transparency, collaboration, and privacy (Palantir RFx Blog Series, #5) Organizations should look beyond traditional functional security requirements to identify security features that enable transparency and collaboration, rather than hamper them. Editor’s note: This is the fifth post in the Palantir RFx Blog Series, which explores how organizations can better craft RFIs and RFPs to …