When I started to use Google Assistant multilingualism, I immediately thought, why not have it teach you a language, with the aid of languages you know? This is a start in that direction.
Via Kirk Borne:
Google's new #AI can help you speak another language in your own voice — the project is called Translatotron: https://t.co/P6y7ILWEIH
More technical details at the Google AI blog:
https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html
Showing posts with label Google AI Platform. Show all posts
Showing posts with label Google AI Platform. Show all posts
Thursday, May 16, 2019
Wednesday, April 17, 2019
Faster and Smaller Neural Nets
Fascinating development. Smaller usually means faster with training nets. Smaller can also mean easier implementation at the IOT edge. Now will they be as accurate? It is all about more efficient perception. Closer to human. Technical piece in Google AI. Intro below, more at the link:
MorphNet: Towards Faster and Smaller Neural Networks in Google AI. Wednesday, April 17, 2019
Posted by Andrew Poon, Senior Software Engineer and Dhyanesh Narayanan, Product Manager, Google AI Perception
Deep neural networks (DNNs) have demonstrated remarkable effectiveness in solving hard problems of practical relevance such as image classification, text recognition and speech transcription. However, designing a suitable DNN architecture for a given problem continues to be a challenging task. Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expensive in terms of computational resources and time. Approaches such as Neural Architecture Search and AdaNet use machine learning to search the design space in order to find improved architectures. An alternative is to take an existing architecture for a similar problem and, in one shot, optimize it for the task at hand. .... "
MorphNet: Towards Faster and Smaller Neural Networks in Google AI. Wednesday, April 17, 2019
Posted by Andrew Poon, Senior Software Engineer and Dhyanesh Narayanan, Product Manager, Google AI Perception
Deep neural networks (DNNs) have demonstrated remarkable effectiveness in solving hard problems of practical relevance such as image classification, text recognition and speech transcription. However, designing a suitable DNN architecture for a given problem continues to be a challenging task. Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expensive in terms of computational resources and time. Approaches such as Neural Architecture Search and AdaNet use machine learning to search the design space in order to find improved architectures. An alternative is to take an existing architecture for a similar problem and, in one shot, optimize it for the task at hand. .... "
Tuesday, April 16, 2019
Google has an AI Cloud Platform. Lets link it with BPM
Quite some detail for making AI applications work with the cloud in this new production factory for AI in the Cloud. I like the idea of standardizing such learning projects and installed solutions. I would also like to see this kind of work linked with business process models like BPM.
AI Platform
Create your AI applications once, then run them easily on both GCP and on-premises.
Take your machine learning projects to production
AI Platform makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, AI Platform’s integrated tool chain helps you build and run your own machine learning applications.
AI Platform supports Kubeflow, Google’s open-source platform, which lets you build portable ML pipelines that you can run on-premises or on Google Cloud without significant code changes. And you’ll have access to cutting-edge Google AI technology like TensorFlow, TPUs, and TFX tools as you deploy your AI applications to production. ... "
A testimonial they provide:
" ... In retail, it’s important to provide customers with easy access to alternative products or recommended add-ons. We train our own machine learning models with TensorFlow on Google Cloud ML, and we automate the periodic retraining of these models with Kubeflow Pipelines. Together with AI Hub, useful for sharing models between data scientists, we can now iterate faster on our models, and automatically deploy them to staging and production. ... ' Lucas Ngoo, co-founder, CTO, Carousell
See also: https://techcrunch.com/2019/04/10/google-expands-its-ai-services/
AI Platform
Create your AI applications once, then run them easily on both GCP and on-premises.
Take your machine learning projects to production
AI Platform makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, AI Platform’s integrated tool chain helps you build and run your own machine learning applications.
AI Platform supports Kubeflow, Google’s open-source platform, which lets you build portable ML pipelines that you can run on-premises or on Google Cloud without significant code changes. And you’ll have access to cutting-edge Google AI technology like TensorFlow, TPUs, and TFX tools as you deploy your AI applications to production. ... "
A testimonial they provide:
" ... In retail, it’s important to provide customers with easy access to alternative products or recommended add-ons. We train our own machine learning models with TensorFlow on Google Cloud ML, and we automate the periodic retraining of these models with Kubeflow Pipelines. Together with AI Hub, useful for sharing models between data scientists, we can now iterate faster on our models, and automatically deploy them to staging and production. ... ' Lucas Ngoo, co-founder, CTO, Carousell
See also: https://techcrunch.com/2019/04/10/google-expands-its-ai-services/
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