Before discussing the best AI tools in the market, let us know about “AI software” and “what AI can do?”.
AI is an interdisciplinary branch of data science that has also been considered a high-value technology. This technology aims to make computers smart enough to carry out tasks that generally need human intervention. Therefore, computers grow smart by “learning” from “experience,” i.e., a large amount of data.
Also, having critical uses in many sectors, AI is used in numerous use cases in businesses and large organizations. Hence, some examples are, AI can enhance customer service, fraud prevention, infrastructure security solutions, supply chain management, etc.
Best AI Tools in Market
Artificial Intelligence software is a computer program that mimics human behavior by studying various data patterns and insights.
Therefore, top features of AI software include Machine Learning, Speech & Voice Recognition, Virtual assistants, etc.
AI and Machine learning provide users with the required functionality and make the business process more straightforward.
AI software builds and develops an intelligent application from scratch with the help of Machine learning and deep learning capabilities.
Artificial Intelligence Platforms
This provides the platform for producing an application from scratch. Thus, many built-in algorithms are present in this. Hence, the drag and drop facility makes it easy to use.
This software will provide the effect that a human or person is doing in a conversation.
Deep Learning Software
It involves speech recognition, image recognition, etc.
Machine Learning Software
It is the technique that will make the computer learn through data.
With AI, one can develop intelligent systems that will help in businesses or offices and at home. Thus, smart systems can perform many tasks, from setting the alarm to switching on/off the lights.
Therefore, with AI, gathering or managing data from different portals becomes much more accessible. With ML, one can apply various algorithms to data to get it in its required form.
While doing online shopping, one gets recommendations based on surfing or purchasing. Therefore, this, in turn, will help in getting more business. All this is possible just because of AI.
To buy some products or services, one probably visits the concerned website, where one gets help through online conversation or chatting window that is always available. This 24*7 help is possible only because of AI (Chatbot).
Microsoft Azure is a cloud platform that hardly needs an introduction. Azure has made notable progress with its capabilities. Therefore, it is a popular choice for AI development.
The Azure AI Platform offers all key AI capabilities, such as:
- Machine learning
- Vision capabilities
- Speech capabilities
- Language capabilities
- Knowledge mining
Azure ML runs with popular open-source AI frameworks like TensorFlow.
The Azure AI Platform has knowledge mining capabilities, and one can unlock insights from documents, images, and media using it. Thus, this includes the following:
- Azure Search, which is a cloud search assistance with built-in AI;
- Form Recogniser, which is an AI-powered extraction service to transform documents and forms into usable data.
The Azure AI Platform offers AI apps and agents, and one can customize them for application. Therefore, this includes Azure Cognitive services, which provide a broad collection of domain-specific pre-trained AI models. The Azure Cognitive Services include AI models for the following:
- Holds any operating system, language, tool, and framework
- Computer resource and memory flexibleness
- Dynamic both IaaS and PaaS
Google is another cloud computing giant that offers its AI platform. The Google Cloud AI Platform presents all the key AI capabilities, e.g.:
With the Google Cloud AI Platform, one can quickly develop the ML project and deploy it to production. Thus, the Google AI platform gives an integrated toolchain, promoting the development and deployment.
Also, with this platform, one can build portable ML pipelines using Kubeflow, an open-source platform from Google. Cloud storage and BigQuery are the leading options to store the data.
The Google Cloud AI Platform offers pre-configured Virtual Machines for building deep learning applications. Hence, one can provision this VM quickly on the Google Cloud, and the Deep Learning VM image contains popular AI frameworks.
Therefore, one can launch Google Compute Engine cases where installed TensorFlow, PyTorch, scikit-learn, and other popular AI frameworks.
Natural Language Processing (NLP)
The Google Cloud AI Platform has NLP capabilities, which one can use to find out the meaning and structure of the text. Also, the Google NLP API helps with text analysis.
The Google AI Platform has APIs for speech-to-text and text-to-speech capabilities.
Its speech-to-text API can help one convert audio to text, and it uses neural network models and vast datasets for this. Hence, the speech-to-text API supports 120 languages and their variations.
With its speech recognition capabilities, one can allow voice command-and-control features in the app. Moreover, the app can transcribe audio.
Vision is another critical capability of the Google Cloud AI Platform, and one can use this to derive insights from the images. It offers its vision capabilities through REST and RPC APIs, and these APIs use pre-trained ML models.
Hence, the app can detect objects and faces. Furthermore, it can read printed and handwritten texts using these APIs.
- Spam Detection
- Cloud-based machine learning
- Convenient pricing scheme
IBM, the technology giant, has outstanding AI capabilities, and IBM Watson is quite famous. There are already IBM Watson AI solutions tailored explicitly for several industries like healthcare, oil & gas, advertising, financial services, media, IoT, etc.
Thus, a key advantage of IBM Watson is that developers can utilize this platform to develop their AI applications. It’s an open AI cloud environment, and it’s pre-integrated and pre-trained on flexible information architecture. Thus, this will expedite the development and deployment of AI applications.
- It has developer tools like SDKs and complete documentation for them.
- One can integrate Watson Assistant to develop AI-powered conversational interfaces into the app.
- With IBM Watson, one can get Watson Discovery. It’s an AI-powered search technology, and it can help app retrieve information that resides in silos.
- IBM Watson has NLP capabilities, and it’s known as Watson’s Natural Language Understanding (NLU). The IBM Watson developer platform includes this.
- One can also use the IBM Watson Speech to Text capabilities to build on the Watson developer platform.
Infosys Nia is an AI platform that enables one to build AI-powered apps. It extends the following AI capabilities:
- Machine Learning: Nia Advanced ML allows a broad range of ML algorithms that operate at speed and scale. Hence, it makes building high-performing ML models easier.
- Contracts analysis: Nia contracts analysis capability involves ML, semantic modeling, and deep learning.
- Nia chatbot: One can build AI-powered chatbots with Nia, and the app can provide access to the enterprise knowledge repository. Therefore, the app can also automate actions through a conversational interface.
- Nia data: AI app can integrate Nia data, a robust analytics solution.
- Infosys information platform
- Asset efficiency
- Infosys automation platform
- Infosys knowledge platform
Dialogflow uses Google’s infrastructure, so it has all the computation power one needs. It incorporates Google’s ML capabilities. Hence, it runs on the Google Cloud Platform. Therefore, one should be able to scale the AI app quickly.
Also, it lets one build a voice and text-based conversational interface for the app. The app can operate on the web and mobile. Besides, one can connect the users on Google Assistant, Amazon Alexa, Facebook Messenger, etc. The critical capabilities granted by Dialogflow are ML, NLP, and speech.
- Build voice and text-based conversational apps
- High performance
BigML is highly deep in ML, and its development platform offers powerful ML capabilities. Also, it gives robust ML algorithms, both for supervised and unsupervised learning.
One can implement instant access to its ML platform using its REST API and do that both on-premises and on the cloud. Therefore, BigML offers interpretable and exportable ML models, and this is a crucial advantage.
- It’s programmable and repeatable. One can use popular languages like Python, Node.js, Ruby, Swift, etc., to code the app, and BigML supports them.
- BigML helps one to automate the predictive modeling tasks.
- Deployment is flexible since one can deploy the AI app both on-premises or on the cloud. BigML has intelligent infrastructure solutions that help in scaling the app.
- BigML has robust security and privacy features.
TensorFlow is a free, open-source software library that helps develop machine learning models. Therefore, it enables the users to employ their AI-powered applications in the cloud, servers, browsers, and other devices. It also uses a multi-layered centre layout that allows the users to train, create, and send fake systems with large data sets.
- It’s fast
- Cloud TPUs are made to train and run ML models
- Deep flexibility
- True probability
- Good community support
Artificial intelligence has significant potential to transform the business and projects to develop AI solutions that have vital real-world applications and, therefore, a big market.AI and ML skills are niches, and it can be hard to form competent DevOps. Therefore, this increases the complexity of such projects.
However, one can choose the best AI tools in market depending upon the organization or individual requirements for their ease.