10 Popular Artificial Intelligence Applications For Business Settings

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10 Popular Artificial Intelligence Applications For Business Settings

These are several popular Artificial Intelligence (AI) applications that have entered into our daily lives and made them easier than ever before.

Artificial Intelligence can be thought of as many technologies that enable ‘instinctive’ machines to provide human capacities and mimic human intelligence. These automated tools may be trained to function like a human brain and understand, analyze, act and evolve using futuristic capabilities like natural language processing, machine learning, data analytics, voice recognition, amongst others. When AI technologies are incorporated into a business setting, they may provide wide-ranging benefits.

The Perspective As To Why AI Keeps Expanding

  • Accenture estimated: “By 2035, AI can double economic growth rates in 12 developed countries and accelerate labor productivity by around 40 percent.”
  • When used along with human effort, AI can allow people to spend more time on work that requires a person’s touch. In absolute terms, AI can take over 80% of their routine tasks and allow people to cater to the “20 percent of non-routine jobs that drive 80 percent of value creation.”
  • Gartner forecasts that “80 percent of emerging technologies will have AI foundations by 2021.”

AI is the future, and the future is here!

What’s “Open Source” A.I. Software?

As complex as this sounds in the beginning, these tools have become simple to use–thanks to engineers from technology giants like Google, Facebook, Uber, amongst others, in addition to developers who routinely bring about the open-source AI community and, consequently, help churn out AI tools which describe high-speed innovation.

More importantly, an open-source framework offers increased visibility into how programmers think and manage datasets, find patterns, all of them while retaining the feedback loop living. Overall, comparatively inexperienced professionals can leverage a constellation of AI tools, frameworks, libraries, and documentation and utilize these open-source artificial intelligence technologies at no cost.

Perspective On Open Source Artificial Intelligence Software Usage

“The Global artificial intelligence market is predicted to grow at a compound annual growth rate of 42.2percent from 2020 to 2027 to reach USD 733.6 billion by 2027.”

Did you know that according to research by Stanford University, there has been a 14X increase in the development of AI startups in the past two decades? Open-source artificial intelligence tools are driving innovation at lightspeed.

Here are the top-10 open-source AI platforms to consider that drive innovation by the second and change the world of open-source AI software as we speak.

Top-10 Open Source AI Software for 2020 (And Beyond)

We’ve segregated the open-minded artificial intelligence software depending on the platform, purpose, and performance for your convenience. Let’s begin.

AI-Based Platforms

An AI-based platform offers a wide selection of tools to help you in designing algorithms or creating intelligent applications. Additionally, typically, they come equipped with numerous functionalities–graphical user interface (GUI), pre-built workflows, existing libraries, etc.

1. TensorFlow

One of the Most Well-Maintained & Popular End-to-End Open-Source Frameworks for Machine Learning

Credit: github.com

Used by global companies including eBay, Airbnb, Intel, DropBox, Twitter, etc., TensorFlow – an open-source AI software – has been launched by Google Brain in 2015. This platform helps companies leverage critical data to construct advanced predictive modeling applications and create and train Machine Learning models. Besides, you can write libraries for dataflow programming. Moreover, developers can use it for numerical computations.

It comprises tools, libraries, and tools that simplify and enhance AI development. To make things simpler, the instrument makes extensive use of visual models and flowgraphs:

For instance, Airbnb utilizes TensorFlow to classify apartment listing photos. This enables the brand to demonstrate every record as correctly (and realistically) as you can.

This open-source AI tool also finds applications in voice/sound/image recognition, text-based applications, video detection, and time-series information (used in finance, accounting, and business/resource planning).

Key Characteristics:

  • Available in multiple languages like Haskell, Java, Python, C++, Go, Rust, etc.
  • Predictive analytics
  • Flexible and speech recognition
  • Robust ML production
  • Transfer learning
  • Data input
  • Intuitive high-level APIs
  • Workflow automation
  • Easy model building
  • Multi-languages

Pros:

  • Free AI software that allows easy installation of computation because of flexible architecture
  • Developers, researchers, and information scientists can instantly construct neural networks and machine learning models and leverage all-important data.
  • It provides an easy-to-use interface and can be deployed across platforms
  • Compatible with Unique TPUs, CPUs, and GPUs, as well as with your PC, mobile, and laptop
  • Provides third party suites for other programming languages
  • Boasts of a lively community of programmers who can provide help (if needed)

Cons:

  • Missing symbolic loops
  • It doesn’t offer support for windows
  • Lacks in speed and use for routine tests as compared to rivals
  • No GPU support for Nvidia and OpenCL; provides only complete language support of Python
  • Computation speed can be improved
  • Requires basic knowledge of advanced calculus, linear algebra, and machine learning
  • Hard to find an error and to debug due to the unique structure
  • No need for the super low-level thing

2. Apache SystemDS

The King of Big Data

Apache SystemDS, initially developed by IBM, is a unified research engine that focuses on Big Data and is excellent for complex mathematical issues. It can scale to Spark and Hadoop clusters and operates on an R Python-like syntax. Mostly, it finds applications in profound learning with GPUs and in neural network architectures for training.

Credit: systemds.apache.org

Key Characteristics:

  • Swift processing and cost-efficient
  • Dynamic in nature
  • In-memory computation
  • Integration with Hadoop
  • Reusability
  • Fault tolerance
  • Real-time stream processing
  • Spark GraphX
  • Multi-language and advanced analysis support
  • Lazy evaluation
  • Active, innovative, and expanding Spark community

Pros:

  • Automatically optimizes usage based on cluster and data characteristics to improve efficiency.
  • Biggest open source project in the data processing
  • Flexible, scalable open-source AI platform
  • Accelerates algorithm installation using its high-speed programming language–declarative machine learning (DML) and PyDML
  • Assists in querying, data processing, and creating analytics reports in a much better and quicker way
  • Features speed and multilingual support
  • Dynamic in nature and provides increased accessibility to Big Data

Cons:

  • No support for real-time processing
  • Not helpful for a multi-user environment
  • It has a little file issue, particularly when using Apache Spark with Hadoop.
  • Has fewer calculations
  • No dedicated file management system
  • Expensive

Machine Learning-based Libraries

A machine learning library saves programmers valuable time and effort in writing code for each project. An ML library includes a set of predefined, reusable features usually written in Python or R.

3. Scikit Learn

Ideal for Data Mining & Analysis

Credit: analyticsvidhya.com

Used by established brands like Spotify, J.P. Morgan, and Evernote, Scikit-learn provides ultra-useful functionalities like predictive analysis, customized recommendations, along with other data tasks. In fact, Spotify utilizes sci-kit-learn libraries to train models and create custom playlists – a much-loved attribute among the users.

Scikit-learn is an open-source python-based machine learning library that provides data mining and analysis capabilities. It was launched in 2007 and is used by commercial and research-based organizations. If you would like to bring machine learning into a production system, this tool is for you.

Key Characteristics:

  • Adaptive and data intake
  • Clustering and classification
  • Pre-processing
  • Model choice
  • Dimensionality reduction
  • Regression
  • Cross-validation
  • Datasets and electronic assistants
  • Dimensionality reduction
  • Manifold learning
  • Process/workflow automation
  • Virtual Personal Assistant (VPA)

Pros:

  • Designed on three open-source projects–NumPy, Matplotlib, and SciPy–using premium machine learning models covering the way for popular use-cases
  • Features support for supervised and unsupervised learning algorithms
  • Free AI tool that is reusable and available to everybody
  • A convenient platform to predict user behavior
  • Properly documented and endorsed by the Worldwide community
  • Easy-to-use software that enables performing adaptive processes

Cons: NA

Credit: medium.com

4. PyTorch

An End-to-End Machine Learning Network With a Useful Ecosystem of Tools & Libraries

Built by Facebook, PyTorch is an ML library based on Torch. What makes it advantageous? Is it is simple to use? This greatly reduces the learning curve for programmers in addition to the opportunity for deployment. PyTorch has emerged since the go-to tool for researchers working on machine learning issues as it provides simplicity, functionality, and a better-designed API.

Key Characteristics:

  • TorchScript gives flexibility and ease-of-use in eager mode
  • Native ONNX support
  • Allows for distributed training
  • TorchServe deploys PyTorch versions at scale
  • Easy user interface
  • Hybrid front-end
  • Python-first
  • Cloud partners
  • C++ front-end

Pros:

  • Allows fast, adaptive experimentation and efficient production
  • Comprises of a user-friendly front-end
  • Provides end-to-end workflow from Python to installation on iOS and Android
  • Supported on important cloud platforms
  • Allows for easy development and effortless scaling
  • Provides a host of useful features like running models in a manufacturing scale environment, a repository of prebuilt images, amongst others

Cons: NA

Deep Learning Tools

Based on artificial neural networks, profound learning tools find a broad assortment of applications across areas like speech recognition, image recognition, recommendation engines, etc. A profound learning tool allows for the easy, easier, and more efficient development of deep learning applications.

5. Keras

A High-Level Machine Learning API

Credit: keras.io

Keras was released in 2015 to simplify the creation of profound learning models. Brands such as Netflix, Uber, and Yelp have incorporated Keras’ profound learning capabilities in their offering. Simply to give you an example, here is how Netflix uses this open-source artificial intelligence platform to forecast customer support. It is common knowledge that Netflix experiences hills’ value of customer information daily.

Based on the critical information gathered, the subscription-based streaming platform utilizes Keras to identify clients who are on the verge of canceling their subscriptions. Netflix then attempts to decrease attrition by providing relevant discounts/incentives to clients and maximize conversion rates.

Key Characteristics:

  • Written in Python
  • It can be used with TensorFlow, Microsoft Cognitive Toolkit, and Theano
  • made for fast processing of data through advanced mathematical models
  • Provides a high-level framework that hides the backend computation and allows for speedy creation of a neural network model

Pros:

  • User-friendly and provides fast deployment, modularity, and ease of extensibility.
  • Supports convolutional and recurrent networks
  • Provides pre-trained versions
  • Runs optimally on GPUs (graphics processing units) and CPUs (central processing units)
  • The main focus on the programmer experience makes it the go-to tool for rapidly prototyping new apps.

Cons:

  • Problems in low-level API because it poses constant errors sometimes
  •  Clustering, poor data-preprocessing tools, and PCM capacities as compared to competitors
  • It does not support dynamic chart creation
  • Computation takes longer

6. Caffe

A Deep Learning Library Built for Speed

Credit: g2.com

Launched in 2017, Caffe (Convolutional Architecture for Fast Feature Embedding) is increasingly being used by academic researchers for varied projects and has been used across vision and multimedia domains. Developed by the Berkeley Vision and Learning Center, it provides deep learning capabilities. This open-source artificial intelligence software is mainly used for image processing applications that require higher computing power and model precision. To this end, surprisingly, it can process over 60 million images each day!

Key Characteristics:

  • Written in C++ and contains a Python interface

Pros:

  • Provides an expressive design that promotes innovation and application. Models and optimization are defined by configuration without a hard-coding structure that allows you to switch between CPU and GPU
  • It focuses on speed, expressiveness, and modularity, making it perfect for startup prototypes, research experiments, and large scale industrial applications in vision, speech, and multimedia
  • Provides extensible code which fosters active growth
  • Features speed by processing 60mn pictures a day in 1 ms/image for inference and 4 ms/image for studying
  • Offers community support for stimulated expansion

Cons:

  • Not useful in a recurrent domain; hence variety is a problem
  • Deployment for creation is not simple
  • The learning curve is steep
  • Static configuration files are hard

Mobile-Friendly AI Building Tools

While most AI tools can be utilized to deploy machine learning algorithms for phones, but here are the top-two open-source mobile-friendly artificial intelligence tools:

7. Apple Core ML

Integrate Machine Learning Models into Your App

Apple Core ML is an open-source artificial intelligence platform that can help build machine learning and deep learning models for the iOS ecosystem. It allows for the easy integration of machine learning models into your app. It features a unified representation of models. By taking advantage of Core ML APIs and user information, it is possible to make predictions and fine-tune versions on your iOS device. Keep in mind you could train a model to categorize photos or find certain objects within a photo straight from its pixels. You will be amazed to know that Core ML 3 has been the drive behind the wildly popular iPhone attributes – FaceID and animoji.

Key Characteristics:

  • Vision
  • Natural language processing (NLP)s
  • Speech recognition
  • Transcription/synthesis
  • Audio analysis

Pros:

  • Models help perform tasks that would be difficult or impractical to write in code.
  • Optimized for on-device functionality, thereby minimizing memory footprint and power consumption
  • Privacy of consumer data is cared for
  • Delivers a practical and responsive app, even with poor network

Cons:

  • Lack of national learning

8. OpenCV

An Open Source Machine Learning and Computer Vision Software

Credit: medium.com

World-renowned brands like IBM, Google, Toyota, etc., use OpenCV for diverse tasks. Firstly developed by Intel, OpenCV is a machine learning library for real-time computer vision applications. It gives access to over 2,500 advanced and traditional algorithms which could be used across various machine learning and computer vision capabilities such as object identification and facial recognition. This allows users to perform varied tasks such as removing red eyes, obtaining 3D models of objects, after eye movements, etc. In fact, many face detection apps on smartphones are powered by OpenCV.

Key Characteristics:

  • Multiple ports
  • Programming functions library
  • Community forums enable users to request assistance and assist other programmers, providing invaluable insights into codes and libraries.

Pros:

  • Free-to-use library
  • Easy-to-use
  • Great customer Care
  • Value-for-money
  • Leverage hardware acceleration and multi-core systems
  • Vast access to algorithms
  • Provides algorithmic efficiency to process real-time applications

Cons: NA

Natural Language Processing Tools

NLP – a Subset of AI – procedures a people’s natural language, text, and video to obtain intelligence. These tools are extremely helpful in capabilities like speech recognition, object detection, computer vision, etc.

9. SimpleCV

An Open Source Framework for Computer Vision Applications

Credit: inspiredtoeducate.net

SimpleCV is a simple to use machine learning library which can help build computer vision applications. Used mostly by novices who would like to participate in rapid prototyping and simple applications, it doesn’t require knowledge of document formats, buffer management, etc.

Key Features:

  • Build optimal computer vision algorithms on-the-go

Pros:

  • Freely available
  • Ready-to-use at the business level with optimized algorithms
  • Get access to computer vision libraries like OpenCV — without gaining knowledge about bit depths, file formats, color spaces, etc.
  • Provides great support and easy-to-follow documentation with simple codes
  • Efficient and best API

Cons:

  • Not available for different programming languages like C/C++, Matlab, Java, etc., and only supports Python

10. Tesseract

A Free Optical Character Recognition Engine Sponsored by Google

Credit: morioh.com

Tesseract is Google’s free optical character recognition (OCR) engine, which translates handwritten/typed data into a familiar and editable format by machines. It’s a complicated image pre-processing pipeline and can learn new information through its neural networks. It’s thought of as among the most accurate OCR engines. Hewlett-Packard originally developed it.

Key Characteristics:

  • Features support for Unicode
  • Can recognize over 100 languages out-of-the-box
  • Used for text detection on mobile devices, in videos, and Gmail picture spam detection

Pros:

  • Supports multiple languages; may be trained to recognize other languages
  • Free software
  • Easy-to-use
  • Works well with 300 DPI files
  • Allows easy creation of sandwich PDF documents

Cons:

  • Rudimentary image processing
  • It just reads TIFF files

Conclusion

Here’s the short and long of it: Building a machine learning algorithm requires extensive research into knowing the perfect technology and open-minded artificial intelligence tool that you need to use. So try these open-source artificial intelligence platforms and select the one which best fits the bill.

At the same time, it’s important not to forget that open-source artificial intelligence software and resources are re-imagining business procedures, models, And human imagination every day. It is high-time companies invest in unlocking the vast-yet-untapped opportunities introduced by AI. What do you think?

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