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10 Google Artificial Intelligence Tools Available to Everyoneby@nuaig
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10 Google Artificial Intelligence Tools Available to Everyone

by Ruchi JainMarch 11th, 2022
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An overview of ten Google AI tools that developers, businesses and analysts can use. Google has become frighteningly entrenched in almost everything digital, whether it's electronics (smartphones, tablets, laptops), basic software (Android, Chrome OS), or intelligent software powered by Google AI. For organizations, by monitoring the market closely, Google is identifying how its services can transform a potentially successful idea into an idea. Google Cloud Datasets are Google curated datasets that are periodically updated through analysis from multiple studies. Google Crowdsource works with handwriting recognition, handwriting recognition and image tags.

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An overview of ten Google AI tools that developers, businesses and analysts can use.

It would be unfair to call Google just a search giant - from a system for finding relevant sites, it has quickly become a driving force for innovation in key IT sectors.

In recent years, Google has become frighteningly entrenched in almost everything digital, whether it's electronics (smartphones, tablets, laptops), basic software (Android, Chrome OS), or intelligent software powered by Google AI. In this article, we will discuss what tools are provided by Google AI for developers, researchers, and organizations.

I. For developers

1. TensorFlow

Tensor tools are used to create highly accurate and well-defined machine learning models by data analysts. It consists of deep learning software libraries which are open source.

2. ML Kit

ML Kit is a mobile SDK currently available on Android and iOS. It leverages Google's machine learning capabilities in mobile apps to solve real-life problems. ML Kit will help you succeed in many tasks.

More than a hundred languages ​​are supported, including Hindi, Arabic, Chinese, not to mention European.

Human faces can be recognized by ML KIT as well as text, faces, QR codes and other objects in images. In addition, there is an API to add your own machine learning models to TensorFlow Lite and integrate them into applications.

3. Google Open Source

Google continues to open source code. And with the active participation of the connecting community, Google's work is getting even better. Some of the large projects by google open source are mentioned below-

ClusterFuzz: In February 2019, the company opened the source code for the ClusterFuzz platform to test code using a server cluster. ClusterFuzz automates tasks such as notifying developers, creating an issue, tracking a bug, and closing reports after a fix.

AutoFlip: In February of this year, Google introduced AutoFlip , an intelligent video reshaping system that detects the most important objects in the frame and cuts the video accordingly. Due to this, from landscape-oriented videos, you can get vertical, portrait ones, which are convenient to watch on mobile phones without rotating the screen.

4. Colab

In short, Colaboratory or Colab is an online Python code editor and compiler. Colab is similar to Jupyter Notebook, with an extra feature of online sharing. The idea of ​​working at Colab is summarized in the following video. At the same time, it is allowed to use one NVidia Tesla K80 GPU for free for 12 hours (after 12 hours, the calculation is thrown off, but you can have time to calculate a small model).

II. For researchers

When diving into a new field, research on the topic under study is especially necessary. Google has made it easier to find a dataset by offering the following tools.

5. Moderated Google Cloud Datasets

The fundamental problem with any ML model is to train it on "correct" data. Google Cloud Public Datasets are Google curated datasets that are periodically updated through analysis from multiple studies. The datasets are available in different formats such as image, audio, video or text. It’s intended to help a wide range of researchers with different requirements and use cases. Depending on the need, anyone can search the dataset using the tool.

6. Google search on datasets

In September 2018, Google launched search for datasets (Dataset Search). The issue contains information about the resource where the set is published, the license, the date of the update, the description and the formats available for download. 

Despite the fact that the name of the datasets does not contain the name of the city, the datasets matches the request and contains information on weather changes in St. Petersburg and the Leningrad region. But note that it was updated quite a long time ago.

7. Crowdsource

Another goal of Google is to improve the accuracy of datasets by offering users interesting tasks, such as recognizing different categories of images such as pictures, letters, newspapers, illustrations, and more. In this case, Google exploits users, offering a game from where Yandex gives - albeit small - but money.

Improve your products with Google Crowdsource.

On the other hand, you can learn how Google Crowdsource works with images, handwriting recognition, facial expressions, translation validation and image tags. Crowdsource analysis results can be transferred to your own ideas for data markup as part of a game approach.

III. For organizations

By closely monitoring the market, Google is identifying how its services can transform a potentially successful idea into an accomplished goal and is creating custom tools for organizations.

8. Cloud TPU

Machine learning, despite all the advances, requires high-performance hardware. To this end, the company has created a tensor processor (Tensor Processing Unit). Cloud TPU enables enterprises to offer their customers the best possible service by reducing hardware costs. The tensor processor belongs to the class of neural processors with an application specific integrated circuit. The architecture is tailored for linear algebra tasks, and, for example, allows Google Photos to process up to 100 million photos per day with one processor.

9. Cloud AI

Cloud AI facilitates the use of existing business models by Google, it enables to customize them thus helps in integrating machine learning capabilities into the existing business.

Cloud AI consists of three components: Hub, Building Blocks, and Platform. Hub is a kind of library with AI ready-to-use components that can be used or shared for experimenting on the models.

Building block component helps developers with speech processing, computer vision, language and structured data, to be added to their application.

The AI ​​Platform enables scientists, engineers, and data developers to quickly deploy their ideas through multiple services such as AI Platform notebooks, deep learning containers, labeling services, and others.

10. Cloud AutoML

Many popular brands like Disney, Imagia, and Meredith use Cloud AutoML. The niche of the tool is affordable machine learning for businesses with little experience in this area, but initially well-labeled data.

Conclusion

The advances in AI technology solutions are staggering. Google's involvement in AI innovation is really high, several tools like ML Kit, TensorFlow, Fire Indicators have been developed by Google. And you can search for datasets not only on Kaggle, but also through a specialized search.