Category Added in a WPeMatico Campaign
We are thrilled to introduce Accuracy Evaluation, the newest feature in our Cloud Speech UI, to allow for easy and seamless benchmarking of our Speech-to-Text (STT) API models and configurations. The STT API covers a wide variety of use cases, from dictation and short commands, to captioning and subtitles. Getting the most of STT, however, …
Read more “Announcing Accuracy Evaluation for Cloud Speech-to-Text”
“Please hold” may be the two words that customers hate most — and that contact center agents take pains to avoid saying. Providing fast, accurate, helpful responses based on contextually relevant information is key to effective customer service. It’s even better if answers are personalized and take into account how a customer might be feeling. …
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales and variances. Unlike VAEs, this formulation constrains DMs from changing the latent spaces and …
Read more “f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation”
ChatGPT users can now turn off chat history, allowing you to choose which conversations can be used to train our models.
IT Managers run into scalability challenges on a regular basis. It is difficult to predict growth rates of applications, storage capacity usage and bandwidth. When a workload reaches capacity limits, how is performance maintained while preserving efficiency to scale? The ability to use the cloud to scale quickly and handle unexpected rapid growth or seasonal shifts …
Posted by Shekoofeh Azizi, Senior Research Scientist, and Laura Culp, Senior Research Engineer, Google Research Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical contexts as they …
Read more “Robust and efficient medical imaging with self-supervision”
Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? You’re not alone. Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. This post describes how to implement your first ML use case using Amazon …
Imagine a future where your vehicle’s interior offers personalized experiences and builds trust through human-machine interfaces (HMI) and AI. In this episode of the NVIDIA AI Podcast, Andreas Binner, chief technology officer at Rightware, delves into this fascinating topic with host Katie Burke Washabaugh. Rightware is a Helsinki-based company at the forefront of developing in-vehicle …
Read more “The Future of Intelligent Vehicle Interiors: Building Trust With HMI & AI”
Posted by Yicheng Fan and Dana Alon, Software Engineers, Google Research Every byte and every operation matters when trying to build a faster model, especially if the model is to run on-device. Neural architecture search (NAS) algorithms design sophisticated model architectures by searching through a larger model-space than what is possible manually. Different NAS algorithms, …
Read more “LayerNAS: Neural architecture search in polynomial complexity”
We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done locally. Data scientists typically …