Tag: faang

Recent large language models (LLMs) have enabled tremendous progress in natural language understanding. However, they are prone to generating confident but nonsensical explanations, which poses a significant obstacle to establishing trust with users. In this post, we show how to incorporate human feedback on the incorrect reasoning chains for multi-hop reasoning to improve performance on …

Read more “Improve multi-hop reasoning in LLMs by learning from rich human feedback”

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. …

Read more “What Is Agent Assist?”

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 …

Read more “Deliver your first ML use case in 8–12 weeks”