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

Andrew Zola
Storyteller
Andrew Zola on Linkedin

 

Artificial intelligence (AI) is the recreation of human knowledge forms by machines, particularly PC systems. These procedures incorporate learning (the obtaining of data and standards for utilizing the data), thinking (utilizing guidelines to arrive at rough or positive resolutions) and self-correction.

Artificial intelligence today is appropriately known as narrow AI, in that it is intended to play out a small task. While narrow AI may beat people at whatever its particular undertaking is, such as playing chess or explaining conditions, AGI (or strong AI) would beat people at about each psychological assignment.

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.

Machine learning (ML) is a programming strategy that gives your applications the capacity to naturally take in and improve for a fact without being expressly customized to do as such. This is particularly appropriate for applications that use unstructured information, for example, pictures and content, or issues with the enormous number of parameters, for example, anticipating the triumphant games group.

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

According to Amazon AWS offers the broadest and deepest set of tools for your business to create impactful machine learning solutions faster.

That’s why more than ten thousand customers, from the largest enterprises to the hottest startups, choose AWS Machine Learning - more than any other cloud platform.

Ultimately, AWS offers AI stages that customers can use to stay away from overhead connected with the in-house generation of AI situations. Amazon Machine Learning and Apache Spark on Amazon Elastic MapReduce (EMR) are possibilities for utilizing AWS to start AI ventures. The Amazon Machine Learning stage gives direction that enables less tech-to smart clients to create AI aptitudes. Amazon portrays it as a "profoundly adaptable" asset that can give constant efficiency over a web-conveyed stage. Customers can likewise utilize Apache Spark and apply it to Amazon EMR for Hadoop-related handling. This includes utilizing a range of Apache open-source devices to make a customer's ideal usefulness.

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

The Analyst Toolbox  is powered by Nathan ICE, our proprietary biologically inspired core technology for language.  The engine of the Analyst Toolbox is the BrainDocs application which provides the platform for processing document libraries, building agents and analyzing results.  The BrainDocs API is available to enterprise developers for application development and is part of our cloud service hosted on MS Azure.

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

Apache Mahout  (TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end, or can be extended to other distributed backends.

 

  • Mathematically Expressive Scala DSL
  • Support for Multiple Distributed Backends (including Apache Spark)
  • Modular Native Solvers for CPU/GPU/CUDA Acceleration

 

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)

OpenNN  (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research. The library is open-source, licensed under the GNU Lesser General Public License.

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.