All the conversations around artificial intelligence are hot and trending. And this isn’t simply a buzzword within AI development companies, it is here to stay for some time until it becomes loud and clear amongst common people. AI works in tandem with many other emerging technologies like ML, Blockchain, NFT, Neural Networks, 5G, and many more. AI is beyond GPT 4.0 and virtual assistants that act as virtual customer care representatives in every other mobile application. Generative AI is a new viral sensation and individuals and businesses are taking note of it, even if it is experimental. However, the information generated by Generative AI is not conducive to banking, insurance, and financial advice. Some of the killer apps empowered by artificial intelligence are ChatGPT (Highly successful), Dall-E 2, Stable Diffusion 2, Lumen 2, Soundraw, Looka, Podcastle, Gen-1, Lalal.ai, Deep Nostalgia, Murf, Legal robot, Cleanup. Pictures, Fireflies, Krisp, and (many) others.
What is AI?
AI is a disruptive technology that has changed the way people anticipate common tasks. It has imparted speed to mundane jobs, and automated work that rededicated human effort has made it easier for e-commerce businesses in particular to anticipate customer needs. It is known for giving a listening ear, being a problem solver, acting as a brainstorming partner, providing more intelligent services and products, and achieving desired business outcomes.
What are AI tools?
AI software AI development software or Artificial Intelligence (AI) tools can be categorized into many types including (1) prototyping, (2) Debugging, (3) Code explanation, (4) Refactoring code, (5) Predictive analysis, (6) Automated testing, (7) Documentation, (8) Copilot Tools. Before we get into the details it is important to identify and prioritize what they bring to the table, what problems they solve, and how they help humans in decision-making. Also, they propel and nurture business factors to include in software development plans.
What are Open-source AI tools?
AI tools whose source code can be seen, read, changed, and distributed by anyone are open-source AI tools. Here are some examples of Open Source Artificial Intelligence Software: Tensor Flow, IBM Watson, Apache Mahout, OpenNN, Scikit-learn, Accord.NET, and Torch.
TensorFlow: It is an Artificial Intelligence Software library by Google known for numerical computation, machine learning, data science, AI, and deep learning. It focuses on deep neural networks and has a wide variety of use cases. It is being used to create commercial products like Google Translate and Google Photos. It can be used on desktops, laptops, servers, and mobile devices and makes use of a variety of programming languages like C++, Python, and Java.
H2O AI Website Link: This platform is used to bring machine learning models to life in real-time. It is open source, free, and allows developers to build exquisite AI models and applications. It is suitable for enterprise app development because it makes use of leading algorithms, auto ML, distributed in-memory processing, and simple deployment. It makes use of R, Python, and Flow. It works on big data infrastructure, or top of Hadoop, Kubernetes, and Spark clusters. It takes data input directly from Spark, HDFS, Azure Data Lake, S3, or any other data source. H2o.ai models are easily deployable into production with Java (POJO), and binary formats (MOJO).
PyTorch: It is a popular machine-learning framework that is relatively easy to use. It is another open-source machine learning library for creating AI applications. It is operated on a CUDA-capable NVIDIA GPU. It is popular because of its flexibility and computation power. It has a large and active community and is a good place to start AI development. Tensorflow is still better than PyTorch as it has limited visualization. Tensorflow is used in deploying trained models to production.
OpenNMS: It is short for open neural networks library that is free and open source. It is written in C++ and is being implemented in solving real-world problems in energy, marketing, and health. It can analyze large data volumes and train models faster than its competitors.
OpenCV: It is short for open-source computer vision library that is written in C++, Python, Java, and MATLAB programming languages. It is being used to develop commercial and non-commercial applications including image processing, object detection and tracking, gesture recognition, and machine learning.
ClearML: It is an open-source MLOperation platform that automates and eases the management and development of machine learning solutions.
IBM Watson: It is a supercomputer created by IBM used to answer questions on AI, ML, and Analytics. It makes use of natural language processing to check human speech, syntax, and meaning.
Keras: It is a Python-based free and open-source library that provides an interface for developing artificial neural network applications. It is a high-level application programming interface (API) for the TensorFlow Platform, designed to solve a machine learning platform, focusing on deep learning.
AWS AI Services: Amazon Web Services (AWS) is an umbrella concern that assimilates various AI and ML services like Amazon Rekognition, Amazon SageMaker, Amazon Comprehend, Amazon Transcribe, and Amazon Lex. Some other use cases (along with benefits and features of AWS AI include: computer vision, automated data extraction and analysis, language AI, Business metrics, Code and DevOps, Industrial AI, and Healthcare)
Scikit-learn: Like the 9 AI development tools discussed above, this one is also a free and open-source machine learning library for the Python framework. It is built upon SciPy and NumPy scientific computing libraries. It is used for data set loading and creating workflow pipelines that combine estimators and data transformer objects. It does not support deep learning, GPU, or reinforcement learning.
What should you consider while choosing AI tools for a specific use case?
A mix of one or more of these features should be considered while choosing AI tools for any specific use case: (1) Scalability, (2) Accuracy and reliability of the model, (3) Cost, (4) Ease of use, (5) Training and inference speed, (6) Jupyter Notebooks, (7) ChatGPT Software, (8) Support availability, (9) Viso Suite Platform, (10) Azure Machine Learning Studio, (11) Chorus.ai, (12) Google Cloud AI Platform, (13) Salesforce Einstein, and (14) Infosys Nia.
Conclusion
The list of 10 top AI development tools is illustrative only and is subject to change. Various sources of information across the search engines have listed their perceptions, and your understanding is solicited before implementing any of these. Each of these comes with its unique set of features and functionalities and has empowered custom software development companies that follow the AI regime to a great extent.
