Top 5 Tools for Artificial Intelligence (AI) and Machine Learning (ML) Development

Andrew Zola
Storyteller
Andrew Zola on Linkedin

Artificial intelligence (AI) is no longer something that is limited to science fiction.

Today, it’s radically changing the way we think about technology. From fraud detection to virtual assistants like Siri, AI and machine learning (ML) is going through a period of significant acceleration.

According to Forrester, investment in the AI space alone has been predicted to increase by 300% this year (compared to last year).

This means that developers will be utilizing several AI and ML tools and technologies to build innovative products.

So what are the best AI and ML tools for developers? Let’s take a look at the top five.

1. Amazon Web Services

 

Amazon Web Services (AWS) comes with several AI toolkits for developers. For example, AWS Rekognition utilizes AI to build image interpretation and facial recognition into apps with common biometric security features.

Furthermore, AWS Lex is the open source tool behind Amazon’s personal assistant Alexa. This technology enables developers to integrate chatbots into mobile and web applications. AWS Polly, on the other hand, utilizes AI to automate voice to written text in 24 languages and 47 voices.

2. AI-one

 

This is a tool that enables developers to build intelligent assistants within almost all software applications. Often referred to as biologically inspired intelligence, ai-one’s Analyst Toolbox is equipped with the following:

  • APIs
  • building agents
  • document library

The primary benefit of this tool is the ability to turn data into generalized sets of rules that enable in-depth ML and AI structures.

3. Deeplearning4j

 

Deeplearning4j or Deep Learning for Java is a leading open source deep learning (DL) library written for Java and Java Virtual Machine (JVM). It’s specifically designed to run on enterprise applications such as Apache Spark and Hadoop.

It also includes the following:

  • Boltzmann machine
  • Deep autoencoder
  • Deep belief net
  • Doc2vec
  • Recursive neural tensor network
  • Stacked denoising autoencoder
  • Word2vec

4. Apache Mahout

 

This is a library of scalable ML algorithms that can be implemented on top of Apache Hadoop by utilizing the MapReduce paradigm. As a result, once all the big data is stored on Hadoop Distributed File System (HDFS), you can use the data science tools provided by Apache Mahout to identify valuable patterns in those big data sets.

The primary advantage of the Apache Mahout project is that it makes it much easier and faster to derive real value from big data.

5. Open Neural Networks Library (OpenNN)

 

This is another open-source tool that’s essentially a class library written in the programming language C++ for SL that is utilized to stimulate neural networks.

With this OpenNN tool, you can implement neural networks that are characterized by high performance and deep architecture.

Some other open source AI and ML tools to consider are as follows:

  • Distributed Machine Learning Toolkit (Microsoft)
  • NuPIC
  • Oryx 2

You can expect more AI and ML tools to hit the market in the near future to keep up with rapid development within this space. As Canada continues to grow as an innovative hub for AI, you can also expect more cutting-edge intelligent technology to come out of North America.

There are many more tools that can be added to this list. Which ones would you say are worth adding to your top 10?

Share your thoughts in the Comments section below or sound off on our LinkedIn page.