Google isn't just using AI to enhance their own products and services. Instead, they're making it possible for small companies and individuals around the world to get involved too. For the tech experts, about having a wider degree of influence in the AI ecosystem, and that all starts with the machine learning software: "Tensorflow".
Tensorflow is a solution that gives developers a truly accessible framework when it comes to managing machine learning algorithms. In 2015, it was made available as an open-source software for anyone to use, and since then, it's emerged as the most popular software in its segment, according to official statistics. The question is, how can you use Tensorflow, and how can it be beneficial to your business?
Why Google Tensorflow is Easier for Developers
Google has taken strategic steps in recent years to make sure that the Tensorflow software is as accessible as possible. For instance, earlier this year, it added additional support for another deep learning framework in the market called Keras. According to information from the Keras creator, Francois Chollet, the Google solution continues to be the fastest-growing deep learning strategy as of 2016.
Perhaps one of the most important reasons why Tensorflow is so popular among developers is that it's excellent at what it does. With Tensorflow, you get something that works quickly and scales perfectly for almost any size of business. You can get all the benefits you need, with as little stress as possible. After all, Google offers Tensorflow as a free software that easily connects to the company servers when it comes to providing computing power and storage for data.
How Can You Use Tensorflow?
If you use the Tensorflow library, you can simply push your products into the Google cloud with more ease than ever before. In fact, the search experts have even created some AI-specific chips to help power operations. If you want to avoid building your own machine entirely, you can always buy off-the-shelf solutions from Google like object recognition and speech transcription.
Here are just some of the most common use cases for Tensorflow.
Time Series Algorithms
Image Recognition Solutions
Video Detection Software
One of the most common uses of Tensorflow is in sound or voice-based applications. Audio signals can come in the form of voice search, for telecoms and handset manufacturers, Sentiment analysis for CRM, voice recognition, and flaw detection.
The chances are that you're already familiar with the voice-activated and voice-search related assistants in the AI world like Google Now, Apple Siri, and Microsoft Cortana. Google even offers Tensorflow options for voice recognition, which makes it easier to manage a range of different business process, including CRM. For instance, voice recognition in Tensorflow could stand in for customer service agents when it comes to sending customers towards the exact information they need.
The Time Series algorithms provided by Tensorflow are perfect for analyzing data in order to extract useful statistics. These solutions allow for the forecasting of non-specific time periods, as well as opportunities to generate alternative versions of time series.
One of the most common use cases for Time series is "Recommendation" which is available already on websites like Netflix, Facebook, Amazon, and even Google, where customer activity can be analyzed to determine what the customer might like to search for, purchase, or watch. Other uses of Tensorflow Time Series are generally linked to security, IoT risk detection, and predictive analysis strategies.
Another popular use option for Tensorflow includes text-based applications that range all the way from sentimental analysis, through CRM and social media for instance, as well as the ability to track down fraud and other threats.
Language detection stands as one of the most common uses for text-based applications, and the chances are that you're already aware of Google Translate, which currently supports more than 100 languages translating from language to another in real time. Another use case for Google is "SmartReply", which is a system that automatically generates email responses for companies who want to keep up with partners and customers.
Today, the image recognition opportunities offered by Tensorflow are typically used in the Telecom, Social Media, and Handset manufacturing worlds. They include opportunities in the form of face recognition, motion detection, image search, photo clustering, and more. For instance, image recognition allows you to identify people and objects in images, as well as gathering data about content and context.
The object recognition algorithms available in Tensorflow can identify and classify the objects that appear in larger images. This is commonly used in engineering applications to help identify shapes for modeling purposes. The more we can analyze image-based information, the more machine learning solutions can be taught to recognize new visuals. This technology is even beginning to expand into the healthcare industry, where Tensorflow algorithms can begin to spot patterns for illness and disease in humans.
Finally, since the Tensorflow neural networks can work well in combination with image and audio, it makes sense that they would perform with video data too. This generally the case when it comes to using machine algorithms for real-time thread detection and motion detection in security, and gaming fields. Recently, universities have begun to work on larger-scale solutions for video classification to help accelerate research for AI in the video world.
The truth is that regardless of what your business does, it probably taps into a great deal of important industry and customer information on a regular basis in order to innovate and thrive. Tensorflow could be the innovative and simple solution for companies who want to tap into that information and use it to their advantage.
Because Tensorflow is an open source library in the machine learning world, we can only predict that innovative use cases will continue to emerge in 2018 and the years ahead.