TensorFlow, by Google, is the most popular machine learning tool in the world. Nearly all of Google’s tools require machine learning to operate and handle their massive data sets. From image search and image captioning to translations and recommendations.
Not only a staple of Google’s toolset, but machine learning is necessary for researchers, data scientists and programmers. The TensorFlow software is a massive compendium of machine learning.
As a library and a computer, it is the largest on earth that handles such quantity and quality of information.
On November 9th, 2015 Google has released TensorFlow under the open source license.
By having the software in an open source license, developers, researchers, data scientists and programmers can collaborate with each other to improve the quality of their data sets and work more efficiently to solve problems with cutting-edge solutions.
A TensorFlow client is available to run on multiple platforms. That requires advanced knowledge of a coding language, like Python, to operate the system through a package manager like PIP, the Python, Package Manager.
After setting all of the needed variables into PIP, the user of TensorFlow can customise the program to their particular needs.
Tensor Board is the visualisation aspect of Tensor Flow, which allows the user to view propagated information that had been worked out in code. Having coded all of the known variables into the machine learning program.
TensorFlow can learn how to identify specific data in a series of files and images and compare them to the known data. As this cycle is repeating, the machine becomes better and better at identifying, and learning.
Once the computer has learned how to sieve through massive quantities of information, to find what is needed, the programmer then can manage the data and analyse the results.
Machine Learning is the cornerstone of the science behind the TensorFlow software. It gives a computer the authorisation to learn, without being told to do so.
It specifically explores the development of algorithms that can compile data and make predictions based on that data. An example of how that works is by using Google’s image search function.
Let’s assume one types into the search bar “tree house”, the Google algorithm uses a technique as described above to look for images of a tree house. It sources the Google database feed with information from other programs.
Web crawlers also called spiders, read text descriptions on websites and images and catalogues them in its directory.
So, when a user looks for a tree house, the algorithm queries the directory and looks at images labelled “tree house” and analyses the image looking for a tree house. Once the machine has learned what a tree house is, it is going to bring back better image results over time.
Tip of the Spear
That is just one simple example of the many facets and functions of machine learning. Not that image searches are necessarily child’s play; it is just the aspect of this technology that is on display on Google’s website.
Other, much more technical and expert uses of this technology happen every day by people who speak, read and write languages less familiar than Klingon to most people.
The developers can write programs that use TensorFlow, to image and transcribe our entire world in digital space so that the everyday person can better understand their environment.
A simple example of that would be a smartphone application that has the function of translating a sign. Whatever the language the application convert that text, and the user could look at the sign through the lens of their smartphone and read that sign in their mother tongue.
The ability for TensorFlow to operate from the cloud is, in itself, an achievement. To have all of the different kinds of CPU’s, GPU’s and other types of processors and platforms is an incredibly difficult task.
But because TensorFlow has that capability, absolutely anyone can access this platform, provided they are technically competent enough to operate the program and contribute to this ever-evolving compendium of machine learning.
Tensor Processing Unit
For TensorFlow to run, Google invented a new type of processor called a Tensor Processing Unit.
The TPU is an integrated circuit specifically for machine learning.
It computes a larger volume of small-sized data packages when compared to the well known and versatile GPU. Using this type of processor, designed specifically for TensorFlow, Google was able to run a program that found all of the text visible on Google Street View in less than five days.
A single TPU is reportedly capable of processing 100 million images per day. The second generation TPU’s are now capable of calculating in floating point, which uses integers with decimals rather than being restricted to whole numbers for calculations, this expands the TPU’s capabilities.
By using neural maps that mimic organic learning methods, known as artificial neural networks, combined with human-made algorithms and virtual machines, researchers are attempting to achieve a greater level of machine learning by creating virtual neural networks.
That is the process of deep learning. The types of deep learning are as follows:
Unsupervised learning takes place when precise definitions of individual data are not available to the machine, which results in uncertain outcomes. One of the classic examples of unsupervised learning is the study of both natural and artificial neural networks.
The take away from a brief overview of such an involved and complicated system, like TensorFlow, is that the amount of actual applied technical skill and knowledge being used every day to make the reality of our 21st-century world a possibility is immense.
True respect for such a development which, unless you travel around specific circles, might escape you and the coming of better learning machines and the resulting AI might catch people unaware and unable to grasp the concepts behind the world that approaches them.
TensorFlow is one of the heavyweights behind machine learning, and there is an excellent chance that the work and learning done with this system is going to be what results in the first real AI.
Go on, go start learning today!