Source:- theindianwire.com

“THE THING THAT’S GOING TO MAKE ARTIFICIAL INTELLIGENCE SO POWERFUL IS ITS ABILITY TO LEARN, AND THE WAY AI LEARNS IS TO LOOK AT HUMAN CULTURE.”
-DAN BROWN

Artificial intelligence(AI) is gradually making its way into business software and will continue to do so for the foreseeable future. Artificial Intelligence software is now being utilized progressively in the market as a part of business programming to computerize procedures. Artificial Intelligence software can be used by enterprises to automate their tasks and enable their employees to work more productively.

What is AI Software?

Artificial Intelligence (AI) Software is a computer program which imitates human behavior by learning various data patterns and insights. These platforms then simulate the cognitive functions that a human mind performs by incorporating deep learning, machine learning algorithms and speech recognition combined with other AI technologies in their functionality to automate business tasks. AI software helps companies to save energy and time by automating their processes and enabling their employees to work more productively and efficiently.

The year 2019 has seen increasing adoption of Artificial Intelligence in pretty much every field. Therefore, in this list, we will be discussing the most popular Artificial Intelligence Software. So guys let’s check out some awesome Artificial Intelligence software out there helping enterprises to ease off the load for their employees.

Most Popular Artificial Intelligence Software:

1.Google Cloud Machine Learning Engine:

Google Cloud Machine Learning Engine is a cloud-based predictive analytics modeling platform for data of all sizes. This application is equipped with a framework that powers various Google services primarily Google Photos and Google Cloud Speech. It helps you to build what’s next with secure infrastructure, developer tools, APIs, data analytics and machine learning.

Overview and Features:

User Base:

Data scientists and developers.

Interface:

Web UI with command line.

Scripting Languages/Formats Supported:

Uses Jupyter Notebooks along with Python-based toolsets.

Formats Supported:

Most of the major data formats can be used but datasets must be first converted to use in the ML engine. This may require services such as BigQuery, Cloud DataProc, Cloud Dataflow, and Cloud Dataprep.

Integration:

Works with Scikit-learn, XGBoost, Chainer, Keras, and TensorFlow. Uses REST API for managing projects.

Reporting and Visualization:

Extensive audit logging, including admin activity logs and data access logs. Google offers internal tools and connects it to third-party tools that can provide rich visualizations of the data. These include Java and Python.

Pricing:

Google has established a pricing model that is based on the resources used and training hours. It uses categories that range from less than 1 cent per hour to more than $31 per hour.

2. IBM Watson:

IBM Watson is another Cloud-based platform that helps organizations to make more informed decisions and is designed for enterprises from various sectors, including finance, healthcare, energy, IoT, consumer engagement, education, work and transportation. It is an enterprise artificial intelligence platform that provides companies with the power they need to accelerate research and discovery, predict disruptions and enrich interactions.

Overview and Features:

Userbase:

Assists workflows in every field whether it is healthcare, education, finance, transportation or any other field.

Interface:

Graphical drag-and-drop and command line.

Scripting Languages/Formats Supported:

Supports Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.

Formats Supported:

Most major data and file formats are supported through open source Jupyter Notebooks.

Integration:

IBM Watson Studio connects several IBM products, including SPSS Modeler and Data Science Experience (DSX) along with other open source tools, in order to deliver a robust Predictive Analytics and Machine Learning (PAML) solution.

The environment accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version features interactivity with Notebook servers and R Studio, along with Python, R, and Scala coder for data scientists.

Reporting and Visualization:

Visualization through SPSS Modeler. Strong logging and reporting functions are built into the product.

Pricing:

IBM has adopted a pay-as-you-go model. Watson Studio Cloud – Standard costs $99 per month with 50 capacity unit hours per month included. Watson Studio Cloud – Enterprise runs $6,000 per month with 5,000 capacity unit hours. Watson Studio Desktop costs $199 per month with unlimited modeling. Watson Studio Local – for enterprise data science teams N/A.

3. Azure Machine Learning Studio:

Azure Machine Learning Studio is an interactive programming tool for predictive analytics. It is a professional tool that allows users to easily drag-and-drop objects on the interfaces to create models that can be pushed to the web as services to be utilized by tools like business intelligence systems.

With Azure Machine Learning Studio, you can explore new ways to build innovative apps on a cloud and edge powered by AI. Azure offers AI services, infrastructure, and tools that are easy to use and scale, accompanied by resources and certifications for developers building intelligent solutions.

Overview and Features:

User Base:

Business analysts, developers and data scientists.

Interface:

Graphical drag-and-drop. Command line.

Scripting Languages/Formats Supported:

Python, R Script and open source Scikit-learn, TensorFlow, PyTorch, CNTK, and MXNet. Accommodates PowerShell module and PowerShell cmdlets.

Formats Supported:

Most data formats and file types works with Jupyter Notebooks, Visual Studio Code and PyCharm.

Integration:

Accommodates numerous open source tools and components. Tightly integrated with Azure cloud services.

Reporting and Visualization:

Offers strong visualization capabilities and logging functions for web services.

Pricing:

Microsoft offers a free tier with limited capabilities and usage time. The most basic plan starts at $9.99 per month per seat with an additional $1 per studio experimentation hour. Production web API pricing are based on tiers that range from $0 to just under $10,000 per month, depending on the number of transactions, compute hours and web services used. Overage rates also apply.

4. Salesforce Einstein:

Salesforce is the acknowledged leader in customer relationship management (CRM). Salesforce Einstein is an AI application for sales data processing. Salesforce Einstein is also equipped with an automation capability that lets it offer in-depth understandings of customers to connect with them on a deeper level. With Salesforce Einstein, sales representatives can easily access data science features even without having any prior knowledge, making them more efficient and more capable of handling customers.

Overview and Features:

User Base:

The Einstein Analytics Cloud is designed for a spectrum of data users, including senior level executives. Many functions are click and select, However, some functions can be too complex for casual users.

Interface:

Graphical drag-and-drop.

Integration:

Salesforce Einstein Analytics connects to the vendor’s flagship CRM solution as well as outside data sources. The platform supports R code for additional functionality. The platform also offers APIs and custom bots that augment and extend various business processes.

Reporting Formats:

The platform exports to Salesforce Analytics Platform (formerly Wave), which offers reporting features, dashboards and tools.

AI and Machine Learning Support:

Yes. Extensive tools for CRM, the Internet of Things (IoT) and other functions. The platform supports recommendations, customer scoring, social insights and sentiment and more.

User Sentiment:

Salesforce Einstein Analytics received a rating of 4.2 at Gartner Peer Review.

Pricing:

For Salesforce users, Einstein Predictions costs $75 per user per month. It’s necessary to contact the vendor for pricing on a far more advanced analytics platform with built-in intelligence features. Other modules vary. Einstein Recommendations costs between $1,250 and $3,750 per user per month (higher for a customized version). Einstein Social Studio add-on runs $1,250 per user per month and up. Other components are available for varying amounts.

5. TensorFlow:

TensorFlow is a symbolic math library for machine learning operations. Originally created by Google Brain for internal company use, it is now an open-source platform with regular updates and extensive use. TensorFlow has been adopted for a wide range of usage since it became an open source platform. It enables teams as well as individuals to develop their own smart applications and enjoy their intelligent results based on cognitive foundations.

Overview and Features:

User Base:

The primary software tool of deep learning is TensorFlow. It allows developers to create large-scale neural networks with many layers.

Interface:

The core TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs.

Integration:

• CPUs, GPUs, and TPUs.
• Desktops
• Clusters
• Mobiles and
• Edge devices
• Beginners and experts can use APIs provided by TensorFlow for development.
• Cloud Services and other libraries

Pros:

• Good community support.
• Features and functionalities are good.

Pricing:

TensorFlow is open source, you can download it for free and get started immediately.

6. Ayasdi:

Ayasdi provides AI for Finance, Healthcare, and Public sector. It provides a framework for application development that is scalable, reliable, and manageable. It lets users build enterprise-grade AI systems that meet the world’s challenges. It is unique in that its intelligence is capable of making justifications for its actions.

Ayasdi meets the criteria necessary to be an intelligent application as it makes justifications for its actions, predictions, and assertions.

Overview and Features:

User Base:

Provides AI for Finance, Healthcare, and Public sector.

Pricing:

Organizations and businesses interested in the platform can request for their enterprise pricing plans and ask for their demo via the web form, through phone, or email. It offers:

• Monthly Payment
• Annual Subscription

7. Nvidia Deep Learning AI:

Nvidia Deep Learning AI is a suite of products dedicated to deep learning and machine intelligence. It allows industries and governments to power their decisions with smart and predictive analytics to provide customers and constituents with elevated services. It lets users pull insights from big data which makes them realize their true value by utilizing them in creating solutions for current and forecasted problems.

Overview and Features:

User Base:

Health Industry, used for visual Identification and also for threat protection.

Features:

• AI Exploration Tools
• NGC Deep Learning Stack
• Workload Management
• Deep Learning Training
• Inference
• Analytics
• Deep Learning Frameworks