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Dynamics of Selecting your Open Source AI

The landscape of open source AI is big. To identify suitable open source tools to make your AI dream product is a herculean task. Selecting an AI toolkit for your product might turn out costly when you need to scale your software, thus it turns out to be a strategic decision. We at CereLabs have developed a criteria to choose Open Source AI Toolkit.

Vision/ Reason for open source
If you need to trust an open source platform, you need to start with the vision statement with which the open source AI platform is launched. The vision statement portrays the commitment of the company or communitytowards the toolkit.
Following are the visions of few of the reputed AI Open Source Platforms:
OpenCog: “OpenCog is a unique and ambitious open-source software project. Our aim is to create an open source framework for Artificial General Intelligence, intended to one day express general intelligence at the human level and beyond. That is: We're undertaking a serious effort to build a thinking machi…

OpenAI and future of AI

With the advent of AI in almost every industry, right from self driving cars to robot nurses, there is a general concern as to how AI might impact humanity. Although AI offers a lot including medical industry and space exploration, it is slowly making a pathway into every technology. The presence of AI can be felt on every device including mobile phones. There is a general belief that AI is a threat to humanity. The reach of AI in every aspect of our life is inevitable. So how do we make sure that it benefits humanity as a whole?

Elon Musk along with other visionaries have come together to take up the baton to help the AI community to work towards a common goal – to make AI benefit humanity. OpenAI is planning to establish itself as a leading non-profit research institution. To make its research accessible to all, OpenAI will collaborate with other institutions and researchers to make their research open source.

To know more about OpenAI follow their official website which…

Implement XOR in Tensorflow

XOR is considered as the 'Hello World' of Neural Networks. It seems like the best problem to try your first TensorFlow program.

Tensorflow makes it easy to build a neural network with few tweaks. All you have to do is make a graph and you have a neural network that learns the XOR function.

Why XOR? Well, XOR is the reason why backpropogation was invented in the first place. A single layer perceptron although quite successful in learning the AND and OR functions, can't learn XOR (Table 1) as it is just a linear classifier, and XOR is a linearly inseparable pattern (Figure 1). Thus the single layer perceptron goes into a panic mode while learning XOR – it can't just do that. 

Deep Propogation algorithm comes for the rescue. It learns an XOR by adding two lines L1 and L2 (Figure 2). This post assumes you know how the backpropogation algorithm works.



Following are the steps to implement the neural network in Figure 3 for XOR in Tensorflow:
1. Import necessary libraries
impo…

Why Tensorflow

You have a lot of data which you want to make sense of, learn patterns, but you don't have the necessary expertice to develop algorithms that will learn through the data. Ofcourse you can develop your own machine learning algorithms to make sense of the data. There might be benifits in developing your own algorithms, proprietary being one, but you might have to invest time and money.
What if you have access to ready made machine learning algorithms which you just have to use in your products? Google's Tensorflow offers such tried and tested algorithms using APIs that you just have to call in your programs. All you have to provide is data, and Tensorflow will take care of the intelligence to learn.
Tensorflow adds the following capabilities to your products 1. Access to machine learning algorithms such as Neural Networks. 2. Increase performance of your models using multiple CPUs and GPUs without change in code. 3. Do numerical computations using data flow graphs.
To learn m…

TensorFlow: A new generation of Google's Machine Learning Open Source Library

Although Machine Learning has dominated the Artificial Intelligence scene for long, easy access to open source machine learning libraries is recently made possible. With the launch of TensorFlow, Google has made it possible for corporates to add intelligence to make sense of data.

TensorFlow adds to the list of other popular open source Machine Learning libraries like Theano and Torch. The uniqueness of TensorFlow is that it has the strong support of Google, which is one of the early pioneers in AI research. Google, using DistBelief, has delivered a lot of successful tools such as Computer Vision, Speech Recognition, Natural Language Processing, Information Extraction, Geographic Information Extraction, Computational Drug Discovery, Language Translation, etc. Tensorflow is Google's second generation machine learning system.

Teaching machines was never so easy. TensorFlow lets you use most of the machine learning algorithms that Google employees use to add intelligence to their prod…