Top 8 Google AI Tools

There is no doubt that Google is an absolute giant in the IT world. It creates various software tools for almost any imaginable area of activity existing today.

Top 8 Google AI Tools

Whatever you could want, Google, probably, has a solution. Either it is a smart voice helper or an intelligent shopping list, it doesn’t matter. Seriously, even special streaming platforms, music tools, and advanced culture applications - Google reinvented the internet and proposed an absolutely new ecosystem for users.

But what about the IT community? Most likely, it is useful to have your thoughts in the Keep, or remember about appointment via Calendar, but does Google have any software for programmers? Especially, if we are talking about Artificial Intelligence (could there be a better moment, than a Terminator 1 anniversary?).

Luckily, Google cares about those, who are interested in AI. Today we will talk about the most interesting tools in this area. The article will be split by categories of people, that may be interested in some specific kinds of tools.

For developers

First of all, we can find software development. Google AI provides various tools for creating artificial systems, neural networks, and multilayered projects. Let’s take a look at some of them.

TensorFlow (TF)

If you want to develop high-precise and well-maintained Machine Learning (ML) systems, you have to know about TF. This open-source ML package was created for a Google system (namely, speech recognition), but right now its main task - to help the artificial intelligence community in product development. Here are some basic advantages of the TensorFlow:

Of course, if you are an absolute newbie in this subject, it may be quite difficult for you to start with TF. However, it has a widespread community, stable and regular updates (recently Google presented 2.0 version), and free-to-use full code of the product.

Right now TensorFlow supports many programming languages, but the basic one is Python. The main advantage of this package - all processes operate on the C++ modules, which is significantly faster and almost invisible for a Python user. Also, TF explores all-powerful ‘players’ in the Machine Learning world and tries to incorporate these projects inside itself. Bright example - Keras and all it’s distributives.

This library allows creating a graph of actions, where each node (tensor) stores data in different shapes and sends it via branches (flows). You can set all variables and functions beforehand, and finally run the project only after prerequisites handling. Also, TF gives the ability to manage and investigate the final model via visualization instruments. In other words, the more complicated system would need to be built, the more often TensorFlow may be a solution.

Use case: image detection for Zyl

This is an example of a real enterprise project. The main idea of the application - saving and recommending the most important photos in the gallery. But how is it possible to define the importance of different pictures? Zyl creators decided to label things inside of the image: faces, smiles, animals, etc.

They incorporated already operable API for image detection and this Machine Learning model improves results on 50%.



As you may see, Google doesn’t spare time and efforts for the artificial intelligence tools. No matter who you are (or want to be): developer, researcher, the commercial worker - anyone could have something to profit from. A bigger part of commercial and scientific tasks and use cases are covered by these products.

The main advantage of the Google AI stack consists of direct integration of all tools between each other. It means, for example, that data could be stored in the Big Query, processed by a custom TF model, boosted by TPUs and shared through AI Hub.

However, the mentioned tools aren’t all available. Day by day, developers and those who are interested in the ML could open something interesting and new from Google expert systems tools. It may not only help them to improve software development and data storing, but also create a more accurate and swift Machine Learning models.

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