Exploring Google AI with Tools

Google AI, formerly known as Google Research, is the company’s artificial intelligence research and development branch that works on several core AI applications. During Google I/O in 2018, Google even disclosed that Google AI would be rebranded.

TensorFlow (for machine learning and deep learning), DeepMind (for creating deep learning and artificial general intelligence technology), Google Auto ML vision (for image recognition), and Google Assistant (for Android devices) are just a few of the ancillary fields in which Google is constantly expanding its artificial intelligence division.

Google Research tackles a wide range of difficult challenges in computer science and related subjects because the company thinks artificial intelligence (AI) may provide unique solutions to concerns and improve people’s lives in targeted ways.

Tools Provided by Google AI

ML Kit

Tools for Machine Learning Kit provides mobile developers with Google’s machine learning skills in a user-friendly and attractive package. By doing this, you can make your iOS and Android apps more aesthetically pleasing, individualised, and functional in addition to providing the best possible performance for the device. It supports;

• Mobile device optimised: ML Kit transforms on the device to expedite and unhitches real-time handling scenarios, such as interacting with a camera. It is acceptable for handling texts and graphics and can also operate in offline mode.
• Designed with Google expertise: You can benefit from machine learning technologies that increase Google’s involvement in mobile devices.
• Easy to deploy: It aims to provide intuitive APIs to support impactful situations in your mobile apps by combining the best machine learning models with superior processing pipelines.

Fairness Indicators

Fairness Indicators are a simple computation of commonly accepted fairness indicators for multi-class and binary classifiers. Previously, it was not appropriate to assess fairness by using massive tools on massive datasets and models. In Google’s case, this makes it possible for products like these to function on billions of user systems.

As a result, Google is able to assess fairness measures across all data sizes and use-case scenarios using fairness indicators. It includes the authority to;

• Analyse the distribution of datasets
• Determine how well models perform and categorise across specified user groups
• Interpret each component to identify underlying causes and opportunities for improvement.

Would you not want to know how fairness indicators can be used to assess fairness interests over time for any product?

The case study, which includes full videos and programming tasks, explains how fairness indicators function.

Collaboratory

Collaboratory, or simply “Colab,” allows you to write Python and run it on your web browser, particularly with;

• No setup required
• Open and free access to GPUs
• Comfortable involvement

It’s a free Jupyter notebook environment that works flawlessly for creating, running, and sharing code on the cloud. It doesn’t require any setup.

This is the official Google Colab Research video, in which Jake Vanderplas explains the precise requirements for getting started with Colab.

Google Open Source

Google is aware that open-source software benefits all users equally. Google encourages and supports collaboration that targets the development of technology to solve real-world problems after making itself freely and publicly available.

It essentially uses a number of open-source projects to establish high-quality, genuine items. Additionally, Google has made millions of lines of code available for public usage under open-source licences.

TensorFlow.js

TensorFlow.js is a JavaScript machine learning toolkit that creates machine learning models and uses Node.js or the web browser to implement ML natively.

Here are some tutorials on using TensorFlow.js that provide great examples, frequently used case models, and real-time demonstrations and examples using TensorFlow.js.

For it to function;

1. Use pre-existing models: You can practise with pre-made JavaScript models or convince Python TensorFlow models to work with Node.js or in a web browser.
2. Retrain existing models: By controlling your unique data, you can retrain pre-existing machine learning models.
3. Manifest ML with JavaScript: By using friendly and flexible APIs, you can create and train ML models directly in JavaScript.

Google has been launching its platform globally, providing us with equal possibilities and monitoring how each individual company goes about setting up machine learning systems. Additionally, Google works very hard to develop a vast artificial intelligence platform, which it has now made available to the whole world.