Top Open Source Artificial Intelligence (AI) Tools

In technology world Artificial Intelligence (AI) is the most happening & trending term in discussion. Everyone is eyeing for its new implementations & results into IT industry. The IT world is going to evolve big time with this technology where machine language will interact with human behavioural pattern & input.  It will open a new dimension in Software Development & Mobile App development. Here we will discuss about some of the Top Open Source Artificial Intelligence (AI) Tools.

Related Topic: Future of Big Data Technology


Caffe is an open source deep learning framework. For expression, speed, modularity, features its one of the most popular frameworks for AI. Caffe is powered & managed by the “Berkeley Vision and Learning Center”. NVIDIA and Amazon are the main promoters who support its development. It mainly switches between CPU & GPU with a single flag & train GPU machine to interact with behavioral pattern & input and does the deployment in machines & mobile devices. Its main USP is its speed.

Microsoft Cognitive Toolkit:

The Microsoft Cognitive Toolkit is another popular open source tool. It was previously known as CNTK. This toolkit has scalling, speed, accuracy, commercial standards and moreover compatibility with different programming languages. Microsoft Cognitive Toolkit implements AI within datasets using algorithms. It increases outstanding performance with a single CPUs or a single GPU or multiple GPUs, machines or devices. Also it has highly optimized built-in components with efficient resource utilization.


Deeplearning for Java is one of the first open source, distributed, deeplearning library for the JVM. It’s a commercial grade tool written for Java & Scala and integrated with Spark & Hardoop. This toolkit is highly used in commercial business environments on CPUs & GPUs. It has a support community called Skymind. Moreover its adapted for micro-service architecture. It also has a feature for parallel training via iterative reduce. This toolkit also has GPU support for scaling on AWS.

H2O is also an open source AI platform in Java. It is compatible & can be integrated easily with products like Apache Hadoop and Spark. It’s very easy to configure and has a intuitive, program friendly  GUI. It interacts easily with R, Python, Java, Scala, JSON through APIs. Its USP is that during training models can be inspected visually. Its acommercial level platform for various business & financial institutes.


Mahout is also an open source framework. Moreover it offers features like: simple framework & build scalable algorithms. It has pre-build algorithms for like Spark and H2O. Moreover it has an environment called ‘Samsara’ which is a vector-math experimentation environment. Therefore it has some uniqueness & is implemented by big shots like Adobe, Accenture, Foursquare, Intel, LinkedIn, Twitter, Yahoo and so on. They also have professional support wing.


MLlib is another open source AI platform which is compatible with Java, Python, Scala & R. This is Apache Spark’s scalable library for machine learning. Its also compatible with Hardoop’s Data flow. As per the performance it has high quality algorithms which help to speed it up faster. Moreoevr its very easy to deploy. MLlib consists of many algorithms like: classification, regression, decision trees, recommendation, clustering, frequent itemsets, topic modeling which make it high performing.


NuPIC is also an open source artificial intelligence framework. This AI framework purely based on neuroscience and physiology. It implements interaction in the brain mammalian with neurocortex. Moreover it creates a computer like system in reference of human brain. Its main motto is to provide a machine which supersedes human brain performance.


OpenNN is also an open source AI class library. It’s in C++ programming language and it also implements neural networks, which is the main area of machine learning research. Its USP is its high performance. As its in C++, therefore it has better memory management interface with higher processing speed. It has deep architecture of neural library. Also it delivers high class performance for advance analytics.

Above are the top tools for AI. Big industries already started to implement this tools for various AI implementations. We are keeping an eye to watch what big thing happens with AI implementation.

Related Topic: AI IT trends in 2017

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