10 Top Open Source Artificial Intelligence (AI) Tools

10-top-open-source-artificial-intelligence-tools

Now-a-days open source AI applications or tools or software are on the cutting edge of artificial intelligence,machine learning and deep learning  technology research. Organizations like IBM, Google, Microsoft, Facebook and Amazon are putting intensely in their own Research and development, and additionally purchasing up new companies that have gained ground in areas like machine learning, neural networks, natural language and image processing. 

Artificial intelligence is winding up more imbued with the consumer market. Microsoft has Cortana, Apple has Siri and Amazon has Alexa as self-learning artificial intelligence projects. Self-driving cars are presently turning into a reality on account of AI driving technology. Even the promoting business is exploiting self-learning AI, as appeared by Andy Fox of Element 7 Digital.

The disillusioning reality of mainstream artificial intelligence is that it is being ruled by proprietary software. Businesses may not surrender their privileged insights so effectively, which is the reason the open source group needs to help free AI projects that right now exist.

These below Top 10 Best open source Artificial Intelligence (AI) Tools/applications are on the bleeding edge of artificial intelligence research.

 

1.) TensorFlow

TensorFlow™ is an open source programming library for numerical computation utilizing data flow graphs.. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

 

2.) Caffe

Caffe boasts as being one of the speediest of the deep learning framework. It is perfect for look into projects requiring quick processing of data and hardware acceleration. Its particular design enables it to effortlessly be forked or extended and it is now been deployed in a huge number of different projects.

The brainchild of a UC Berkeley PhD candidate, Caffe is a deep learning framework in light of expressive architecture and extensible code. It’s specialty is its speed, which makes it well known with the two scientists and venture clients. As indicated by its site, it can process more than 60 million pictures in a single day utilizing only one NVIDIA K40 GPU. It is overseen by the Berkeley Vision and Learning Center (BVLC), and organizations like NVIDIA and Amazon have made grants to help its development.

Site Connection : http://caffe.berkeleyvision.org/

 

3.) H20

H20 is intended for advanced decision making for large industries. It bolsters AI strategies like gradient boosting, random forests and generalized linear.

H20 is an open-source deep learning platform. It is artificial intelligence tool which is business oriented and help them to make a decision from data and enables the user to draw insights. There are two open source versions of it: one is standard H2O and other is paid variant Sparkling Water . It can be utilized for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.

Concentrated more on enterprise utilizes for AI than on research, H2O has big organizations like Capital One, Cisco, Nielsen Catalina, PayPal and Transamerica among its users. It claims to make is workable for anybody to utilize the power of machine learning and predictive analytics to solve business problems. It can be utilized for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence.

It comes in two open source versions: standard H2O and Sparkling Water, which is integrated with Apache Spark. Paid enterprise support is likewise accessible.

 

4.) Deeplearning4j

Deeplearning4j is one of the main open source AI libraries for Java and Scala. It is appropriate for business applications and might be accelerated by CPUs or GPUs.

Deeplearning4j is an open source deep learning library for the Java Virtual Machine (JVM). It keeps running in distributed environments and integrates with both Hadoop and Apache Spark. It makes it possible to configure deep neural networks, and it’s compatible with Java, Scala and other JVM languages.

The project is overseen by a business organization called Skymind, which offers paid help, training and an enterprise distribution of Deeplearning4j.

Site Connection :http://deeplearning4j.org/

 

5.) Mahout

 

An Apache Foundation project, Mahout is an open source machine learning framework. As indicated by its site, it offers three noteworthy highlights:

  • a programming environment for building scalable algorithms,
  • pre-made algorithms for tools like Spark and H2O, and
  • a vector-math experimentation environment called Samsara

Organizations utilizing Mahout include Adobe, Accenture, Foursquare, Intel, LinkedIn, Twitter, Yahoo and numerous others. Proficient help is accessible through third parties listed on the site.

 

6.) MLlib

 

MLlib is an open-source Start’s scalable machine learning library. Known for its speed, Apache Spark has turned out to be a standout amongst the most prominent tools for big data processing . MLlib is Spark’s scalable machine learning library. It integrates with Hadoop and interoperates with both NumPy and R. It incorporates a large group of machine learning algorithms for classification, regression, decision trees, recommendation, clustering, topic modeling, feature transformations, model evaluation, ML pipeline construction, ML persistence, survival analysis, frequent itemset and sequential pattern mining, distributed linear algebra and statistics.

 

7.) NuPIC

 

NuPIC is an open source artificial intelligence project in view of a hypothesis called Hierarchical Temporal Memory. Numenta is handling a standout amongst the most critical scientific challenges of all time: reverse engineering the neocortex.

NuPIC is an AI learning framework that is implementable into Python, C++, Java, Clojure, Go, or JavaScript. It gathers analytics from from live data streams to perceived time-based patterns. It is perfect for detecting anomalies within live data. Their HTM design is motivated by neuroscience.

Notwithstanding the open source license, Numenta likewise offers NuPic under a commercial license, and it additionally offers licenses on the patents that underlie the technology.

 

8.) OpenNN

OpenNN is additionally an open-source class library written in C++ for deep learning, it is utilized to prompt neural systems. OpenNN is an open source class library written in C++ programming language. It implements neural systems, the fundamental area of machine taking in research. It’s portrayed by a deep architecture and high performance.

Site: http://www.opennn.net/

The Open Neural Systems Library (OpenNN) is an open source C++ library utilized particularly for deep machine learning. It’s architecture utilizes a few layers of processing units for analytical learning. It bolsters speeding up by OpenMP and NVIDIA’s Cuda.

Intended for researchers and developers with cutting edge comprehension of artificial intelligence,  Its key highlights incorporate deep architectures and fast performance. Extensive documentation is accessible on the site, including an introductory tutorial that explains the basics of neural networks. Paid support for OpenNNis accessible through Artelnics, a Spain-based firm that works in prescient investigation.

9.) OpenCyc

OpenCyc is one of the more seasoned AI projects and has been underway since 2001. It is a general knowledge AI that is especially valuable for question and answer contests, understanding text, and learning knowledge within specific domains.

OpenCyc is an open-source portal, it is essentially general knowledge base and commonsense reasoning engine of the world. It has Rich domain modeling, Domain-specific expert systems, Text understanding, Semantic data integration,  AI games and more.

Site Link: http://www.cyc.com/stage/opencyc/

Created by an organization called Cycorp, OpenCyc provides access to the Cyc knowledge base and commonsense reasoning engine. It includes more than 239,000 terms, about 2,093,000 triples, and about 69,000 owl:same As links to external semantic data namespaces. It is helpful for rich domain modeling, semantic data integration, text understanding, domain-specific expert systems and game AIs.. The organization likewise offers two different forms of Cyc: one for analysts that is free however not open source and one for big business utilize that requires a fee.

10.) SystemML

Apache’s SystemML is an artificial intelligence framework that is accessible for R and Python. It is intended for big-data systems using high-level mathematical equations . It is at present being utilized by vast businesses like automotive or airport traffic control.

Apache SystemML is open-source artificial intelligence platform for machine learning perfect for big data. Its fundamental highlights:

  • Runs on R Python-like syntax
  • Concentrated on big data
  • Designed specifically for high-level math

To start with created by IBM, SystemML is now an Apache big data venture. It offers a highly-scalable platform that can implement high-level math and algorithms written in R or a Python-like syntax. Enterprises are as of now utilizing it to track client benefit on auto repairs, to coordinate air terminal activity and to connect online networking information with saving money clients. It can run on top of Spark or Hadoop.

Website Link: http://systemml.apache.org/

 

 

Further exploring Open Source AI Tools which might interests you:

 

Torch 

Torch describes itself as “a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.” The emphasis here is on flexibility and speed. In addition, it’s fairly easy to use with packages for machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking. It relies on a scripting language called LuaJIT that is based on Lua.

Mycroft AI

Mycroft is the first project that aims to be an open source competitor to assistants like Siri or Cortana. Dubbed as the “AI For Everyone”, it is designed to run on any platform including automobiles or a Raspberry Pi. The framework is designed to learn from voice commands and will share the information with the project to help develop a better AI. The source code can be ran on any device that has a Python interpreter.

PredictionIO

In February this year, Salesforce bought PredictionIO, and then in July, it contributed the platform and its trademark to the Apache Foundation, which accepted it as an incubator project. So while Salesforce is using PredictionIO technology to advance its own machine learning capabilities, work will also continue on the open source version. It helps users create predictive engines with machine learning capabilities that can be used to deploy Web services that respond to dynamic queries in real time.

Oryx 2

Built on top of Apache Spark and Kafka, Oryx 2 is a specialized application development framework for large-scale machine learning. It utilizes a unique lambda architecture with three tiers. Developers can use Oryx 2 to create new applications, and it also includes some pre-built applications for common big data tasks like collaborative filtering, classification, regression and clustering. The big data tool vendor Cloudera created the original Oryx 1 project and has been heavily involved in continuing development.

CNTK

Short for Computational Network ToolkitCNTK is one of Microsoft’s open source artificial intelligence tools. It boasts outstanding performance whether it is running on a system with only CPUs, a single GPU, multiple GPUs or multiple machines with multiple GPUs. Microsoft has primarily utilized it for research into speech recognition, but it is also useful for applications like machine translation, image recognition, image captioning, text processing, language understanding and language modeling.

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