Google developed its Cloud Natural Language API to give customers a language analyzer that could, the internet giant claimed, "reveal the structure and meaning of your text." Part of this gauges sentiment, deeming some words positive and others negative. When Motherboard took a closer look, they found that Google's analyzer interpreted some words like "homosexual" to be negative. Which is evidence enough that the API, which judges based on the information fed to it, now spits out biased analysis. The tool, which you can sample here, is designed to give companies a preview of how their language will be received.

Google is launching new features within its free Cloud Natural Languages API (shared software technology) that will help newsrooms and other businesses sort information so that it's easier to find later. Google is seen as a mixed bag for publishers, offering a lot of traffic, but also sucking up a lot of the industry's ad revenue. Still, the features could be game-changers for newsrooms faced with the daunting task of classifying and taxonomizing hundreds of articles per day and thousands of articles in their archives. It will also make translating text from different languages much easier so that publishers can enter new markets more easily. Publications like Hearst and Vice have already been testing the new features that will be open to all newsrooms and businesses moving forward.

Specifically, you can tell the robot what flavor you like, such as "chewy candy," "sweet chocolate" or "hard mint." The robot then processes your instructions via voice recognition and natural language processing, recommends a particular kind of candy and uses image recognition to recognize and select that recommendation. The entire demo is powered by deep-learning technology running on Cloud Machine Learning Engine (the fully-managed TensorFlow runtime from Google Cloud) and Cloud machine learning APIs. This demo is intended to serve as a microcosm of a real-world machine learning (ML) solution. For example, Kewpie, a major food manufacturer in Japan, used the same Google Cloud technology to build a successful Proof of Concept (PoC) for doing anomaly detection for diced potato in a factory.

This guest post explains how Wootric's platform uses Google Cloud Natural Language API to complement its own machine learning for saving infrastructure and engineering costs. Wootric is a customer feedback management platform that allows businesses to gauge and quantify customer loyalty through proven feedback metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT) and Customer Effort Score (CES). For example, here's an NPS survey that we present in-app (we also support mobile, email and SMS) that usually takes a user less than 30 seconds to complete. As you can see, the question above is very specific and objective. Applying simple arithmetic on this score from your customer base gives you your Net Promoter Score, and allows you to sort your customers into sets of {Promoters, Passives and Detractors}.

There's an arms race among public cloud providers to provide businesses with the best machine learning capabilities. Enterprises are increasingly interested in creating intelligent applications, and companies like Amazon, Microsoft and Google are rushing to help meet their needs. Google fired its latest salvo on Tuesday, announcing a set of enhancements to its existing suite of cloud machine-learning capabilities. The first was a new Jobs API aimed at helping match job applicants with the right openings. In addition, the company is slashing the prices on its Cloud Vision API and launching an enhanced version of its translation API.

There's an arms race among public cloud providers to provide businesses with the best machine learning capabilities. Enterprises are increasingly interested in creating intelligent applications, and companies like Amazon, Microsoft and Google are rushing to help meet their needs. Google fired its latest salvo on Tuesday, announcing a set of enhancements to its existing suite of cloud machine-learning capabilities. The first was a new Jobs API aimed at helping match job applicants with the right openings. In addition, the company is slashing the prices on its Cloud Vision API and launching an enhanced version of its translation API.

The last couple of years have seen a large number of organizations and developers rush towards getting familiar with Machine Learning fundamentals and coming to grips with what it takes to integrate it into their applications. While you can definitely build out your own Machine Learning platform, it is not for everyone and companies like Google are now releasing fully managed API platforms where they expose the Machine Learning platform that they have built over the years. The main value to potential users is that these companies have likely trained their Machine Learning models for years and now the best of these services can be had with a single API call. The latest offering from Google is the Cloud Natural Language API which gives developers insights into unstructured text. A REST API is available to invoke the above functionality and we are going to deep dive into the Sentiment Analysis part of the API to first understand how it works and then build out a Slack Team helper that decodes the sentiment of the text provided to it.

Building on a raft of machine learning-related announcements it made earlier in the year, Google has just launched two new machine learning APIs into beta. The most exciting of the two looks to be the new Google Cloud Natural Language API, which is aimed at helping developers build applications that understand human language. The API works by letting users reveal the structure and meaning of a text, and is available in English, Spanish and Japanese for now, with the promise of support for additional languages to come. In a second blog post focusing on the Cloud Natural Language API, Google demonstrates how it can be used to analyze a report in the New York Times. Per Google's example, you can perform sentiment analysis on various blocks of text using the API, run the results in a BigQuery table, and then use Google Data Studio to visualize them: In a second example, Google showed how digital marketers can use the sentiment analysis capabilities in the Cloud Natural Language API to monitor customer calls to service centers and online reviews.

Google has released open beta versions of two new machine learning APIs to enable its cloud platform customers to perform data analytics on large text and audio files. Building on a raft of machine learning-related announcements it made earlier in the year, Google has just launched two new machine learning APIs into beta. Google's Dart language will get its very own version of the company's Angular 2 framework to better leverage Dart's own capabilities. But the plan has raised concern that it could actually hurt the fledgling language. Today, Google has announced a public beta launch for its new service, Cloud Natural Language API, giving developers the access and ability to use sentiment analysis, entity recognition, and syntax analysis all through the use of Google.

Continuing its evangelism of machine learning, on Wednesday Google said two of its machine learning APIs introduced in March have advanced into open beta status. The first of these is the Cloud Natural Language API, which lets developers parse the meaning and structure of text. With initial support for English, Spanish, and Japanese, the API provides tools to understand the sentiment expressed in text, the relevant entities discussed (e.g. It is, in short, a way to help software understand. For companies, potential applications might include understanding how people feel about a product based on the sentiment expressed in online reviews, or how customers feel about support interaction based on analysis of transcribed calls.