Top 15 Open-Source Artificial Intelligence Tools to Learn in 2023

Today, Artificial Intelligence (AI) is routinely performing tasks both mundane and miraculous. From the smart filters that keep pesky spam from clogging your email to the facial recognition system that reunited thousands of lost children with their parents in India, artificial intelligence tools and methods are changing life as we know it. And this is just the beginning.

In theory, artificial intelligence is machines that think and act like humans, by mimicking our intelligence and cognition. In practice, this is a lot more complex: It involves hundreds of thousands of processes of feeding machines with data and logic to ‘teach’ them intelligence. For the uninitiated, this can seem daunting.

It doesn’t have to be! The trailblazers of AI have established a concrete foundation of tools, frameworks, libraries, and documentation to help young professionals like you find your place. Engineers from Google, Facebook, Uber, and other large companies have open-sourced battle-tested artificial intelligence tools and made them available to you, online and for free. 

Here are the Top 15 Open-Source Artificial Intelligence Tools to Learn

1. Artificial Intelligence Tools: AI Platforms

Think of an AI platform as your personal tool shed, where you stack a wide range of tools you use to design algorithms, build intelligent applications, or even business solutions. Platforms typically have multiple functionalities — say, a graphical user interface (GUI), existing libraries, pre-built workflows, integrated development environments (IDE), etc. Here are the top three AI platforms you must learn in 2022.

1. TensorFlow, developed by Google, is the most popular platform today for building and deploying machine learning models. You can get started using TensorFlow if you have moderate familiarity with Python or C++. If you’re having teething troubles, no worries, the platform has a wide range of tools, libraries and a vibrant community of technologists to help every need.
   
2. Apache SystemML, originally developed by IBM, is a flexible, scalable platform to deploy machine learning algorithms for big data. It accelerates algorithm deployment with their high-level programming language called declarative machine learning (DML), which R programmers will find familiar; and a variant called PyDML for Python developers. It automatically optimizes usage based on data and cluster characteristics to improve efficiency. It scales to both Hadoop and Spark clusters.
   
3. H2O.ai is an enterprise-grade AI platform for deploying machine learning, deep learning and gradient boosting algorithms. It supports popular languages like Python, R, and Java and is used in over 18,000 organizations globally.

Get To Know Other Data Science Students

Hastings Reeves

Business Intelligence Analyst at Velocity Global

Read Story

Sam Fisher

Data Science Engineer at Stratyfy

Read Story

Karen Masterson

Data Analyst at Verizon Digital Media Services

Read Story

Machine Learning Libraries

A machine learning library is a set of functions, pre-written and reusable, for similar purposes. That are typically written in specific programming languages like Python or R. The fundamental advantage of a library is that it saves developers the time and energy involved in manually writing code for each task. Several AI platforms — such as TensorFlow — come pre-built with their own set of libraries. Here are a few additional ones.

1. Scikit-learn is a well known ML library, based on Python. It provides a range of unsupervised and supervised algorithms — classification, regression, random forests, gradient boosting, etc. — on an interface. The popular music app Spotify uses scikit-learn libraries for training models to create playlists. 
2. Torch is a widely used ML library, based on Lua, an easy and fast language. It is widely used in applications where speed and parallel processing are essential. Twitter’s algorithm switch to top tweets from reverse chronological tweets was powered by Torch.
3. PyTorch is an ML library based on Torch. First developed by Facebook, PyTorch is simple to use, shortening the learning curve for developers and reducing time to deployment. In fact, a study from last year found that PyTorch is the tool of choice for researchers working on machine learning problems since it combines simplicity, performance, and a better-designed API.

Deep Learning Tools

Deep Learning, a subset of AI, is based on artificial neural networks or connectionist systems. It has seen a wide range of uses across industries in speech recognition, image recognition, recommendation engines, etc. A deep learning tool is one that makes the building and deployment of deep learning applications easier, faster or more efficient. Some of the more popular ones are here.

7. Keras is a deep learning library written in Python. It is designed for fast experimentation on deep neural networks, processing data through sophisticated mathematical models. Everyone’s favorite streaming service Netflix, uses Keras for its recommendation engine.

8. Uber Ludwig is the deep learning library developed on TensorFlow, by Uber. It is used to train, test and deploy machine learning models — no coding required! Non-programmers turn to Ludwig to develop models fast and without errors. Uber employs Ludwig in a variety of ways — to improve maps, streamline communication and even predict when an Uber Eats order will be delivered.

9. Caffe, developed by UC Berkeley, is a deep learning library built for speed  — it can process more than 60 million images daily! It is predominantly deployed for image processing applications that require higher computing power and model accuracy. 

Tools for Building AI on Mobile

While most of the above tools can also be used to deploy machine learning algorithms for mobile applications, sometimes they might not be enough. For those cases, here are some mobile-friendly artificial intelligence tools.

10. TensorFlow Lite is TensorFlow’s version for on-device inference to deploy ML models on mobiles and single-board computers. You can pick a new or existing model, convert it, and download and deploy it for mobile. For instance, you can build Snapchat or Instagram-like filters using TensorFlow Lite.

11. Apple Core ML, as the name suggests, is a framework to build machine learning and deep learning models for the iOS ecosystem. The latest edition, Core ML 3 is the force behind iPhone features such as FaceID and animoji.

12. OpenCV, developed originally by Intel, is a machine learning library for real-time computer vision applications. Several face detection apps on smartphones are powered by OpenCV. 

Natural Language Processing Tools

NLP is an area under AI that processes humans’ natural speech, text, and video to gain intelligence. NLP tools tend to specialize in artificial intelligence tools, adept at performing functions such as speech recognition, computer vision, object detection, etc. Here are some of the top ones.

13. SimpleCV is an easy to use machine learning library to build computer vision applications. It is popular with beginners who want to engage in quick prototyping and simple applications, as it does not require knowledge of file formats, buffer management, etc. 

14. Tesseract is Google’s optical character recognition (OCR) engine which converts handwritten or typed data into a format that is recognizable and editable by machines. Cited as the most accurate of all the OCR engines, Tesseract supports multiple languages.

15. Detectron is an object detection algorithm. Developed by Facebook, based on Caffe2 and written in Python, Detectron is commonly used to train models for their augmented reality applications.

While our mentors and teachers have scoured the web to bring the best 15 artificial intelligence tools for your use, we must acknowledge that this is just the tip of the iceberg. There is an unlimited number of artificial intelligence examples, hundreds of tools to implement them, and we expect hundreds more in the making. The world of AI can be a bit overwhelming like that.

For further reading, find out what data scientist does and learn more about data science.