10 Top Open Source AI Technologies For Startups

In the area of technology research, Artificial intelligence is one of the hottest trends. In fact, many startups have already made progress in areas like natural language, neural networks, AI, machine learning and image processing. Many other big companies like Google, Microsoft, IBM, Amazon and Facebook are heavily investing in their own R&D. Hence, it is no surprise now AI applications are increasingly useful for small as well as large businesses in 2019. In this blog, I have listed top 10 open source AI Technologies for small businesses and startups. 1) Apache SystemML It is the machine learning technology created at IBM that has reached one of the top-level project levels in the Apache Software Foundation and is a flexible and scalable machine learning system. The important features are: For machine learning, it is considered as SQL. You can run on Apache Spark, where your line of data is automatically adjusted per line, determining if your code should run on the controller or on an Apache Spark group. Future SystemML developments include additional deep learning with GPU capabilities, such as importing and executing neural network architectures and previously trained training models. 2) OpenNN It is an open source class library written in C ++, which implements neural networks. It has been designed to learn from data sets and mathematical models. It provides an effective framework for the research and development of algorithms and applications of neural networks. OpenNN implements data mining methods as a set of functions. These can be incorporated into other software tools using an application programming interface (API) for the interaction between the software tool and the tasks of predictive analysis. The main advantage of OpenNN is its high performance. It is developed in C ++ for better memory management and higher processing speed and implements the parallelization of the CPU using OpenMP and acceleration of GPU with CUDA. Datasets and Mathematical Models are given below: -> Function regression -> Optimal control -> Time series prediction -> Inverse problems 3) TensorFlow It is an open source software library, which was originally developed by researchers and engineers working in the Google Brain Team. TensorFlow is for numerical calculation using data flow graphs. The nodes in the graph represent mathematical operations, while the edges of the graph represent the arrays of multidimensional data (tensors) communicated between them. The flexible architecture allows you to implement computing on one or more CPUs or GPUs on a desktop, server or mobile device with a single API. TensorFlow provides multiple APIs. The lowest level API, TensorFlow Core, gives you complete programming control. Top-level APIs are built on TensorFlow Core. These higher level APIs are usually easier to learn and use than TensorFlow Core. In addition, higher level APIs make repetitive tasks easier and more consistent between different users. A high-level API like tf.estimator helps you manage data sets, estimators, training and inference. Some of the apps using Tensorflow are given below: -> SmartReply -> RankBrain -> On-Device Computer Vision -> Inception Image Classification Model -> Massively Multitask Networks 4) Neuroph It is an object-oriented neural network framework written in Java. Neuroph can be used to create and train neural networks in Java programs. Neuroph provides a library of Java classes and the easyNeurons GUI tool to create and train neural networks. Neuroph is a framework of light Java neural network to develop architectures of common neural networks. It contains an open source and well designed Java library with a small number of basic classes that correspond to the basic concepts of NN. It also has a nice GUI neural network editor to quickly create Java neural network components. It has been published as open source under the Apache 2.0 license. Neuroph supports common neural network architectures, such as the multilayer perceptron with Backpropagation, Kohonen and Hopfield networks. All of these classes can be expanded and customized to create custom neural networks and learning rules. Neuroph has built-in support for image recognition. 5) OpenCog OpenCog is a diverse set of cognitive algorithms, each one incorporating its own innovations, but what makes the overall architecture powerful is its careful adherence to the principle of cognitive synergy. OpenCog was originally based on the 2008 release of Novateen LLC’s proprietary “Novamente Cognition Engine” (NCE) source code. The original NCE code is described in the PLN book (ref. Below). The continuous development of OpenCog is supported by the General Artificial Intelligence Research Institute (AGIRI), the Google Summer of Code project and others. 6) Caffe It is a deep learning framework which is developed with speed, modularity and expression factors keeping in mind. The Caffe project was initiated at UC Berkeley and then developed by Berkeley AI Research (BAIR) and community partners. It focuses mainly on convolutional networks for artificial vision applications. Caffe is a solid and popular choice for tasks related to computer vision, and you can download many successful models made by Caffe users from the Caffe Model Zoo (link below) to use immediately. Some of its major advantages are given below: -> Extensible code -> Speed -> Expressive architecture -> Large Community 7) Torch It is an open source machine learning library, a scientific computing framework and a scripting language based on the Lua programming language. The torch is used by the AI Research Group of Facebook, IBM, Yandex and the Idiap Research Institute. It has been extended for use on Android and iOS and has been used to build hardware implementations for data flows such as those found in neural networks. PyTorch is an open source machine learning library for Python, used for applications such as natural language processing. It is developed mainly by the artificial intelligence research group of Facebook, and the software “Pyro” of Uber for probabilistic programming is based on it. Some of its features are listed below: -> Fast and efficient GPU support -> powerful N-dimensional array -> numeric optimization routines -> Embeddable with ports to iOS and Android backends 8) Mycroft It is the first open source wizard in the world and can be used in anything from a scientific project to an enterprise software application. It can run anywhere on a desktop computer, in a car or on a Raspberry Pi. This is an open source software that can be remixed, extended and improved freely. Mycroft can be used in anything from a scientific project to an enterprise software application. 9) Apache Mahout It is the framework of distributed linear algebra and the mathematically expressive Scala DSL. Mahout was designed to allow mathematicians, statisticians and data scientists to quickly implement their own algorithms. Apache Spark is the recommended distributed backend or can be extended to other distributed backends. It has great support for multiple distributed Backends (including Apache Spark). Apache Mahout currently implements areas that include collaborative filtering (CF), clustering, and categorization. Some of its useful features are listed below: -> Matrix and vector libraries -> Taste CF -> Distributed fitness function capabilities -> Clustering implementations 10) Deeplearning4j It is a deep open source and distributed learning library written for Java and Scala. Its objective is to be cutting-edge plug and play and more conventions than configurations, which allows the rapid creation of prototypes for non-researchers. DL4J is customizable to scale. You can import neural network models from most major frameworks through Keras, including TensorFlow, Caffe and Theano, and close the gap between the Python ecosystem and the JVM with a set of tools for data scientists, data engineers and DevOps. Keras is used as the Python API of Deeplearning4j. It’s major features are listed below: -> Scalable on Hadoop -> Java, Scala and Python APIs -> GPU support for scaling on AWS -> Micro-service architecture Conclusion: So far we have seen some major and new open source AI Technologies for startups. Because of these rapid technological advancements, arrival of new technologies & massive amounts of talent and resources are dedicated to accelerating the growth of the technologies. Hence, implementing project using various open source AI technologies can assist you to make a good decision to get success in the future.