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Empowering businesses and developers to do more with AI


AI has evolved dramatically in the last two decades. Technologies like image recognition and machine translation are now a part of everyday life for millions. AI has transformed industries all over the world, and created entirely new ones. And in the process, it promises an increase in quality of life and work never before imagined. But there's still much more we can do--after all, AI is still a nascent field of many opportunities and challenges.

Google's Grand Plan To Make AI Accessible To Developers And Businesses

Forbes - Tech

Artificial intelligence took center stage at Google's annual user conference, Cloud Next 2018. The company made several announcements that make machine learning and artificial intelligence accessible to both developers and businesses. One of the first announcements came in the form of Cloud AutoML, a managed service that lets developers build machine learning models without requiring any specialized knowledge in machine learning or coding. AutoML Vision, along with other automated ML services became publicly available. According to Google, it is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google's state-of-the-art transfer learning, and Neural Architecture Search technology.

Spotlight on AI at Google Cloud Next '18 – SyncedReview – Medium


Artificial intelligence has become a sort of secret weapon in the battle to build the best cloud service platform. Google Cloud Platform is currently the underdog, trailing both Amazon Web Services and Microsoft Azure. But Google is betting robust AI will give it the edge it needs to catch up. At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products. Earlier this week, Google parent company Alphabet reported its Q2 earnings, which were ahead of Wall Street's expectations.

Google is adding new automated machine learning tools and bringing its AI software to call centers


Google has a slew of artificial intelligence announcements it's making this week at its Cloud Next conference, which kicks off in San Francisco today, and many are focused on the company's democratization of machine learning tools. Starting today, Google's AutoML Vision tool will now be available in public beta after an alpha period that started back in January with the launch of its Cloud AutoML initiative, the company announced during its keynote. Cloud AutoML is basically a way to allow non-experts -- those without machine learning expertise or even coding fluency -- to train their own self-learning models, all using tools that exist as part of Google's cloud computing offering. The first of these tools was AutoML Vision, which lets you create a machine learning model for image and object recognition. Google makes these tools legible to those outside the software engineering and AI fields by using a simple graphical interface and universally understood UI touches like drag and drop.

Google launches an end-to-end AI platform


As expected, Google used the second day of its annual Cloud Next conference to shine a spotlight on its AI tools. The company made a dizzying number of announcements today, but at the core of all of these new tools and services is the company's plan to democratize AI and machine learning with pre-built models and easier to use services, while also giving more advanced developers the tools to build their own custom models. The highlight of today's announcements is the beta launch of the company's AI Platform. The idea here is to offer developers and data scientists an end-to-end service for building, testing and deploying their own models. To do this, the service brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labeling service) and then either use existing classification, object recognition or entity extraction models, or use existing tools like AutoML or the Cloud Machine Learning engine to train and deploy custom models.