AI Tools

Apache PredictionIO is an open source machine learning server built on top of an open source stack for developers and data scientists to create predictive engines for any machine learning task.

It consists of three core components:

• PredictionIO platform - its open source machine learning stack for building, evaluating and deploying engines with machine learning algorithms
• Event Server - an open source machine learning analytics layer for unifying events from multiple platforms
• Template Gallery - a place for you to download engine templates for different type of machine learning applications

Eclipse Deeplearning4j is an open-source deep-learning library for the Java Virtual Machine. It can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems. It allows developers to configure deep neural networks and is designed to be used in business environments on distributed GPUs and CPUs.

The project was created by a San Francisco company called Skymind, which offers paid support, training and an enterprise distribution of Deeplearning4j.

Torch is a scientific computing framework, an open source machine learning library and a script language based on the Lua programming language. It provides an array of algorithms for deep machine learning. Torch is used by the Facebook AI Research Group and was previously used by DeepMind, before it was acquired by Google and moved to TensorFlow.

Torch is an open source machine learning library, a scientific computing framework, and a script language based on the Lua programming language. It provides a wide range of algorithms for deep machine learning, and uses the scripting language LuaJIT, and an underlying C implementation.

Claiming to be 'biologically inspired intelligence', ai-one lets developers create intelligent assistants within most software applications.

Ai-one's 'Analyst Toolbox' provides a document library, building agents and APIs for developers. Ai-one can essentially turn data into generalised rule sets, enabling lots of in-depth AI and machine learning structures.

Although enterprise-focused, Protege has a suite of open source tools ideal for developers to create 'knowledge-based applications with ontologies'.

Aimed at both experts and (somewhat) beginners, Protege lets developers create, upload, modify and share applications. Protege also houses an active community, making troubleshooting simple and collaboration optimised.

TensorFlow is an open source software platform specifically designed for machine learning projects.

TensorFlow works by providing a library comprising of numerical computation using data flow graphs. This lets developers deploy deep learning frameworks over multiple central processing units (CPUs), on mobile, desktop and tablet devices.

TensorFlow includes lots of documentation, tutorials and online resources, so for those not familiar with the platform, or Python, TensorFlow provides lots of support for developers.

Nervana and Intel have joined forces to build the next generation of intelligent agents and applications and Neon is its open source Python-based machine learning library.

Founded in 2014, Neon lets developers build, train and deploy deep learning technologies in the cloud.

Neon has lots of video tutorials and a 'model zoo' which houses pre-trained algorithms and example scripts.

OpenNN is a C++ programming library for experience developers implementing neural networks.

It includes plenty of documentation and tutorials including an introduction to neural networks, although OpenNN is aimed at developers with lots of experience with artificial intelligence.

OpenNN has also built a tool for advanced analytics called Neural Designer, which aims to simplify and interpret data entries by creating visual content such as graphs and tables.

Apache Spark MLlib is an in-memory data processing framework. It features a large database of algorithms focusing on classification, regression, clustering and collaborative filtering.

Within the Apache incubator there is also an open source framework called Singa which provides a programming tool for deep learning networks across numerous machines.

Caffe is another deep learning C++ framework created to provide developers with an imaging-based automatic inspection tool. It was developed by the Berkeley Vision and Learning Center (BVLC) as well as a community of developers.

The framework is already currently being used as part of 'academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia'. Notable users include Facebook and Pinterest.

Part of Samsung's distributed deep learning platform, Veles is written in C++ and uses Python for coordination between nodes.

Veles provides developers with an API which is ready for immediate use and consists of trained models and can be used for data analysis.

Designed for developers wanting to create scalable machine learning applications, Mahout allows users to use its pre-formed algorithms for Apache Spark, H20 and Apache Flink.

Like most tools, Mahout has a variety of useful tutorials and resources, ideal for learning developers.

Apache MXNet is a modern open-source deep learning framework used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple languages (C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl, Wolfram Language).

The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by major Public Cloud providers including AWS and Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS. Currently, MXNet is supported by Intel, Dato, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology.

Theano is a numerical computation library for Python. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures.

Theano is an open source project primarily developed by a machine learning group at the Université de Montréal.

On 28 September 2017, Pascal Lamblin posted a message from Yoshua Bengio, Head of MILA: major development would cease after the 1.0 release due to competing offerings by strong industrial players. Theano 1.0.0 was then released on 15 November 2017.

Microsoft Cognitive Toolkit, previously known as CNTK and sometimes styled as The Microsoft Cognitive Toolkit, is a deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.

Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or MXNet. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer.

In 2017, Google's TensorFlow team decided to support Keras in TensorFlow's core library. Chollet explained that Keras was conceived to be an interface rather than a standalone machine-learning framework. It offers a higher-level, more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. Microsoft added a CNTK backend to Keras as well, available as of CNTK v2.0.

Microsoft launched three new machine learning tools at its Ignite conference in September 2017: the Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench and the Azure Machine Learning Model Management service.

These are aimed at developers wanting to build their own AI agents or build-upon existing models.

Microsoft also launched a tool for non-developers to use AI functionality within their Microsoft Excel spreadsheets.

Microsoft provides three AI tools for developers: Custom Speech Service, Content Moderator and Bing Speech APIs in an attempt to make AI 'accessible for all'. This is on top of Azure's machine learning offering, the 'Azure Machine Learning Studio, which lets developers drag and drop datasets and deploy predictive analytics. Microsoft also provides two open source AI tools: the Computational Network Toolkit (CNTK) and the Distributed Machine Learning Toolkit (DMTK).

Microsoft already houses around 25 tools designed for developers wanting to add emotion detection, vision and speech recognition and language understanding to their applications.

Microsoft also offers Cortana Intelligence, a suite which fully manages big data and analytics, meaning you can transform your data into meaningful information and actions.

IBM is a big player in the field of AI, with its Watson platform housing an array of tools designed for both developers and business users.

Available as a set of open APIs, Watson users will have access to lots of sample code, starter kits and can build cognitive search engines and virtual agents.

Watson also has a chatbot building platform aimed at beginners, which requires little machine learning skills. Watson will even provide pre-trained content for chatbots to make training the bot much quicker.

Founded by Daniel Kroening at the University of Oxford, DiffBlue is a dedicated code automation platform. And it's a simple but extremely useful one at that.

Its aim is to locate bugs, refactor code, perform test writing and find and fix weaknesses in code, all done via automation.

Amazon Web Services (AWS) provides a number of artificial intelligence toolkits for developers, including Amazon Rekognition Image, Amazon Lex and Amazon Polly.

Rekognition uses AI to add image interpretation and facial recognition to apps, which is often used for biometric security features.

Polly uses AI to automate voice to written text across 47 voices in 24 languages. Lex is the open source engine behind Amazon's personal assistant Alexa, allowing developers to integrate chatbots into web and mobile applications.