The application of artificial intelligence software places enormous competitive advantages on the businesses that implement it. Artificial intelligence software is nothing but a computer program that learns various data patterns and insights to mimic human behavior. The artificial intelligence software and tools produced by top AI companies can give you remarkable feats if deployed skilfully. The software itself with machine learning can automate business processes, bring more productivity in human effort, and enhance the customer experience.

You can utilize a good amount of data analytics and predictive analytics with artificial intelligence software to give productive output to the management team. At the same time, it can leverage cloud computing that provides greater computing power and mining data at a faster rate.

Some of the top features that artificial intelligence software include are as follows:

– Machine Learning

– Speech & Voice Recognition

– Virtual Assistant

There is an intense use of machine learning and deep learning to building artificial intelligence software from scratch.

Types Of Artificial Intelligence Software

There are four different types of artificial intelligence software in the market:

1. Artificial Intelligence Platforms: This is used for the development of an application from scratch. The platform works with many built-in algorithms along with a drag and drop facility.

2. Deep Learning Software: These are special types of artificial intelligence software that include image recognition, speech recognition, etc.

3. Chatbots: This software leverage conversation facilities like a human can do. There are plenty of chatbot platforms available in the market that serves different purposes. Here is a list of  Top 15 Artificial Intelligence Chatbot Platforms revolutionizing the business world

4. Machine Learning Software: Machine learning applies different algorithms to make the computer learn through data.

How to Choose Artificial Intelligence Software

It needs Deep Research to select an artificial intelligence software

Artificial intelligence software is quite different than conventional software as it needs deep understanding to acquire knowledge on its functionality. Before you start working on it, you need to dig down into the particular software’s full features and review it in the marketplace. Not only that, before your IT department deploys it, an in-depth conversation with the sales team is essential. So, it’s not that simple!

Decide What You Want to Accomplish?

The AI marketplace is very confusing, as well as costly. Before you move on to artificial intelligence software, first decide your sole purpose of using the software? Do you want to automate an office process, or just looking for a chatbot solution? Or you want a sophisticated CRM software that can automate your business process? So, before jump into the AI marketplace, define your goal and budget.

Limit Your Scope to Start

Deploying an AI solution needs a proper understanding of artificial intelligence software. The market study shows that a small percentage of companies have successfully deployed artificial intelligence software in the real world. This is one tricky area that no AI vendor will warn you off during the software’s purchasing. So, if you are a first-time user, limit your purchase goal to a modest start so that your management and other staff can digest the application properly. This prevents them from grappling with confusing skill sets.

Look for what AI Software Vendors Product Offering

While purchasing artificial intelligence software from a vendor, it is required to understand what array of services they are offering you. It could be from machine learning to niche AI automation tools. Not only that, but you also need to know whether the vendor will be going to be around for product support. This is not all about buying one tool instead of establishing a long-term relationship with the AI vendor.

Concerns over In-House Talent

Getting AI pros is very expensive to obtain. Only purchasing artificial intelligence software does not address the problem. It would help if you had a talented resource to tweak AI software’s power to get the best benefit. So, it would help if you found a prospective vendor who has a good track record of AI support.

Best Artificial intelligence software

1.TensorFlow

This is a free and open-source platform developed by Google for building and deploying machine learning models. The platform offers a wide range of libraries, tools, and a vibrant community to support every problem. If you are familiar with Python and C++, you can start with TensorFlow. Beginners and experts can use the APIs of TensorFlow for development.

Features:

The solution can be deployed on:

• CPUs, GPUs, and TPUs.
• Clusters
• Desktops
• Mobiles
• Edge devices

Pros:

• Good community support.
• Easy ML model building through APIs
• Features and functionalities are good.

Cons:

• It is difficult to learn and will take the time to learn it.

Tool Cost/ Plan details: Free.

Click here for the official URL.

2. H2O.ai

H2O.ai aims to make “AI for everyone.” The vision of H2O.ai is to democratize AI for everyone. This is an open-source diverse suite of AI software products that includes an open-source integration with Spark, an open-source machine learning platform, and a tool called AutoML, which helps in scalable, automated machine learning. Another interesting tool it offers is H2O Q, using which you can make your own AI applications. Also, H2O.ai demands it offers a Responsible AI solution that is explainable, secure, interpretable, ethical, human cantered, and industry compliant.

Additionally, all the AI apps of H20.ai come with a feature of an array of dashboards that display real-time data, allowing a kind of data storytelling based on artificial intelligence.

H2O.ai has been implemented in multiple domains like banking, healthcare, insurance, telecom, and marketing. Using this tool, you can build models using programming languages like R and Python.

Features:

• Includes AutoML functionality.
• Supports many machine learning algorithms like gradient boosted machines, deep learning, generalized linear models, etc.
• It follows a distributed in-memory structure.
• Linearly scalable platform.

Pros:

• Easy to use.
• Provides good support.

Cons:

• The documentation part needs improvement.

Tool Cost/ Plan details: Free

Click here for the official URL.

3.IBM Watson

IBM Watson is an open-source multi-cloud platform that aims to automate the AI lifecycle. The latest innovations in machine learning power. You can build powerful AI models from scratch using it. At the same time, you can speed time-value with your pre-built enterprise apps. IBM Watson comes with a complete set of library solutions that approaches under one name. These libraries either help to build AI into applications or intend to offer an AI-fueled service.

Furthermore, the solution ranges from a small chatbot to AI-based systems to organize and analyze a large volume of data in an efficient and cost-conscious way. Besides, IBM Watson supports SUSE Linux Enterprise Server 11 OS with the help of Apache Hadoop. With proper training with Watson, it can deeply understand the concepts.

Features:

• Supports distributed computing.
• Provides an API for application development.
• It can work with existing tools.
• It can learn from small data as well.

Pros:

• Robust system.
• Helps in making business processes smarter.

Cons:

• Decentralized reporting.

Tool Cost/ Plan details: Free.

Click here for the official URL.

4.Google AI Platform

Google AI platform is Google’s open-source AI platform, which is a managed service and enables users to build machine learning models. Google’s AI solution is a full-scale artificial intelligence software that gets benefits from the Google Cloud Platform.

Google AI toolset offers various tools, including TensorFlow, Explainable AI, Notebooks, etc. This Google provides Kubeflow and other key ML and AI tools that enable companies to build different AI deployments. These deployments can run either on-premises or in the Google cloud, and you don’t need to do much code tweaks to make it workable.

Click here for the official URL.

5. Azure Machine Learning

Azure Machine Learning is an ultimate machine learning production studio. In a highly competitive AI marketplace of machine learning vendors, Azure is moving aggressively to earn the market. Azure machine learning offers an exhaustive ML platform that does not ask you for any upfront costs, and it is solely a “pay as you go” model.

The Azure toolset includes MLOps, which equivalent to DevOps for ML and greatly improves the ML workflow. Besides, protecting and governing data is an in-built feature of Azure. This put an eye to avoid biases that can distort the ML model’s results. No doubt, Azure Machine Learning solutions are fully interoperable with the Azure cloud, a major advantage for the Azure AI toolset.  

The Azure ML tool enables you to deploy your AI model as a web service. As a result, the web service will be platform-independent that can use any data source.

Features:

• Azure ML can deploy the models on-premises, in the cloud, and at the edge.
• It comes with drag and drops feature. Hence, easy to use.
• It provides a browser-based solution.
• It is scalable.

Pros:

• You don’t need any programming skill to use it.
• It supports open source technologies and can be integrated at the same time.

Cons:

• For the paid features there is a lack of transparency in pricing details.

Tool Cost/ Plan details: Please visit here to know the detailed pricing plans.

Click here for the official URL.

6. Infosys Nia

Infosys Nia is specialized in machine learning, deep learning, and data management. It has three components, i.e., Data platform, Knowledge platform, and automation platform.

Any company can create AI architectures into its internal infrastructure using Nia. The toolset addresses the typical industry problems that can be solved with AI. Its operational toolset builds automation into IT operations and AI models. DocAI is another solution to Nia that employs natural language processing and applies smart search to process business documents efficiently, ultimately speeding up the data access.

Similarly, Contracts Analysis is another AI solution by Nia that deploys machine learning to scan and “read” legal documents within few staffer hours. Advanced machine learning techniques such as vision, semantics, language sequence are used for this purpose.

Nia is applying AI capabilities such as InfoExtractor, Computer Vision, NLP, and Cognitive Search for its DocAI solution.

Features:

• It empowers businesses by improving processes and systems.
• Provides automation for repetitive and programmatic tasks.
• It has a conversational interface.
• The automation platform combines RPA, Cognitive automation, and Predictive automation.
• Knowledge platform captures, processes, and reuses the knowledge.
• The data platform provides machine learning and advanced data analytics.

Pros:

• Infosys Nia provides Advance machine learning, Chatbot, and business applications.
• It helps in capturing knowledge from different systems and processes.

Cons:

• Difficult to learn.

Tool Cost/ Plan details: Contact them for pricing details.

Click here for the official URL.

7. BigML

BigML is an end to end solution that enables users to turn data into useful models that can be either on-premises, embedded, or remote in the cloud. The BigML platform offers an array of ML tools that enable users to build AI applications, which include all sorts of ML modeling, anomaly detection, time series forecasting, etc.

Features:

The platform includes supervised and unsupervised learning and a menu of pre-built ML algorithms that speed the production of workable systems. As an added plus, BigML offers a collaboration system so teams can work together to build their ML models.

https://bigml.com/features

8. Ayasdi

Ayasdi is a machine learning-focused software platform. It has applications that help enterprises create data-driven models for a wide range of use cases. This may cater from research to security to industrial applications to fintech uses. The company’s enterprise solution, AyasadiAI, employs statistical and geometric algorithms, data analysis, and ML to uncover solutions and understand trend lines. This AI-powered framework derives more value from data. The Ayasdi AI software solution can be deployed on-premise or in the cloud.

https://www.ayasdi.com/solutions/

https://www.ayasdi.com/enterprise-ai/

9.  Oxagile Content DNA Platform

A ready-to-use human-like machine learning platform that performs video content analysis.

Features:

• Content analysis on-scene level in contrast to a conventional frame-level DL approach.
• No need for particular training datasets: use your own or publicly available content.
• Support for professional and user-generated live and VoD content.
• Works with any content type: sports events, movies, series, TV shows, etc.
• 2 weeks on average to adapt the platform to a new use case.
• Accurate detection of content anomalies: external ads, glitches, artificial text, and more.

Marketing:

• nextgen ad placement.
• Sport highlight detection and compilation.
• hyper-personalized content recommendation.

Pros:

• Easy to learn even for not deep learning specialist.
• Uncountable opportunities for video processing analysis.
• Unsupervised machine-learning.

https://www.oxagile.com/competence/computer-vision/dna-framework/

10. Engati

Engati’s specialty is AI chatbot, which is the most common use of AI software in the business. The crucial things about a chatbot are that it must learn human interactions and the industry vertical, and Engati leads. The Engati chatbot platform offers relatively simple and fast AI fabrication to build a chatbot. This chatbot platform’s specialty is it can offer proactive chat and canned chat, which is undoubtedly an advanced nature of  AI development.

https://www.engati.com/

Final Words

Today we are more leveraged with increased internet speed, which also decreases the cost of computing power. This makes it possible for companies to leverage artificial intelligence software. Another reason behind this is technological advancement, which makes it happen to use advanced algorithms without supercomputers. Also, these AI solutions come with user-friendly interfaces.

However, when it comes time to implement artificial intelligence in business, there are many important considerations. Only choosing artificial intelligence software does not solve the problem. Along with it needs choosing proper projects and consultants. Otherwise, deriving insights from data is not possible.