Top 14 Artificial Intelligence Tools and Frameworks You Need to Know

Artificial Intelligence has become an essential tool for every business and industry. Experts using these AI tools & frameworks properly will bring a transformation in all industries, from healthcare, banking, education, etc.

Caffe is a versatile machine-learning framework that is capable of building complex neural networks for image classification. It runs comparatively fast on GPUs and works efficiently for image processing.

• TensorFlow

TensorFlow is a software library developed by Google that supports machine learning and deep learning. Its flexible architecture allows easy deployment of computation across different platforms like CPUs, GPUs(Graphics Processing Units), and clusters of servers.

It works based on data flow graphs that have nodes and edges. These graphs explain how data moves through the computation process.

In the era of Artificial Intelligence, this open-source library is a must-have. It is a powerful framework for working with multi-dimensional arrays and mathematical expressions, which are essential in machine learning applications.

TensorFlow is an efficient and robust tool for building and executing models, especially at the production scale. Moreover, it enables developers to use a wide range of programming languages. Its features make it the best choice for building large-scale, multi-layered neural networks.

• Keras

Keras is a library that sits on top of other frameworks for rapid prototyping deep neural network models. Developed by Francois Chollet, Keras was designed to be modular and user-friendly.

It is used on top of Theano or TensorFlow to create fast, easy-to-use machine-learning projects in Python. Its guiding principles are simplicity and usability, which is why it has grown rapidly with more than 250,000 developers, with a 2X growth in the last year alone!

Models in Keras are understood as a sequence or graph of standalone, fully-configurable modules. They include neural layers, cost functions, optimizers, initialization schemes, activation functions, and regularization schemes.

• PyTorch

PyTorch is an open-source framework that is used for the implementation of neural networks. It was developed by the artificial intelligence teams at Facebook and is a popular choice for deep learning.

It has many features make it a good choice for research and production. It also has a dense ecosystem of tools and libraries that extend its functionality to other areas like computer vision, machine learning, and reinforcement learning.

It supports dynamically created graphs, which is important for some applications. This feature makes it easier to develop deep neural networks and offers flexibility and speed.

• Ayasdi

Ayasdi is a machine intelligence platform that combines automation, machine learning, and topological data analysis to simplify extracting knowledge from large and complex data sets. It also enables the deployment of intelligent AI-based applications across an enterprise.

The company was incubated at Stanford’s mathematics department and focused on using a powerful branch of math called topological data analysis (TDA) to automatically calculate categories and similarities within broad data sets. This enables the platform to produce network maps that help analysts comprehend the relevance of data clusters and variables.

Many industries use Ayasdi’s machine intelligence platform. Among them are financial services, healthcare, and the public sector.

• Viv

Viv, an open-source machine learning platform, allows third-party developers to build conversational assistants. It’s built using modern web technologies and can work on various devices, from mobile phones to TVs and cars.

Unlike other personal assistants, which are programmed to respond to user requests, Viv’s AI is self-programmed. So it can handle requests from users who don’t know what to ask.

The company hopes to incorporate Viv into every kind of device and service, including cars. It’s also exploring new business models, including a service that lets users pay fees to negotiate their bills.

• BlockChain

Blockchain is a digital ledger that helps you record and track your transactions. It also makes it impossible for hackers to change the data on it.

This technology can be used to track anything from monetary transactions to food products. It allows for easy data traceability, which can help you identify any troubling issues and give you the tools to take action against them.

Another key benefit of Blockchain is its time efficiency. It enables faster settlements and avoids the need for intermediaries such as brokers, banks, or lawyers who increase transaction times and costs.

This technology is also known for its robust features like data immutability, decentralization, single source of truth, and more. It is a powerful tool that is becoming increasingly popular for various applications, such as healthcare, fraud detection, and smart contracts.

• BigML

BigML has many features that help users create enterprise-grade predictive applications. According to Dallas SEO Companies, It is also easy to use and visually appealing. It is perfect for business development services, business analysts, technical managers, and engineers.

BigML can be used for Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, and Topic Modeling tasks. It can be deployed as a remote service or locally, embedded into applications to make predictions.

It also allows data imports from Cloud storage and other file repositories. This key feature will benefit organizations once public Cloud infrastructures become a commodity.

BigML also helps users build intelligent applications with voice control, like Amazon’s Alexa, delivering personalized customer experiences while ensuring accurate predictions. The platform is also scalable, making it ideal for high-volume operations.

• Watson Machine Learning

IBM Watson Machine Learning enables businesses to train and deploy AI models at scale. The software automates tasks like data preprocessing, hyperparameter optimization, automated feature engineering, and model selection.

This software runs on any cloud and allows users to train and deploy AI models anywhere they are, ensuring confidence in their work. The software also enables users to track their different model versions and critical events.

IBM Watson Studio, IBM Watson Machine Learning, and Cloud Private for Data are designed to help organizations accelerate their journey to AI. They help data scientists, application developers, and business teams build models faster and more efficiently at scale.

• AutoAI

AutoAI is an AI feature inside IBM’s Watson Studio data science platform that automates data preparation and pre-processing tasks. This frees up data scientists to focus on building models.

It also includes automated feature engineering, which expands and augments the data’s feature space to optimize model performance.

Additionally, AutoAI generates candidate machine-learning pipelines customized for predictive modeling problems. These pipelines are created iteratively as AutoAI analyzes your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem set.

You can then use these pipelines to build and test machine-learning models on your data. The pipelines can be run in a Python notebook generated by AutoAI.

• Notebooks

Notebooks are a type of document that contains both the input and output of a computational session. These files are often used for archiving and versioning code.

They are similar to text documents, except that they contain many mathematical data and graphical objects. This makes them ideal for collaboration and archiving.

Jupyter notebooks are a great way to store and share your computations. They are a powerful tool for data science researchers.

• Caffe

Caffe is a fast, scalable, and modular deep learning framework. It has been used in various scientific research projects, startup prototypes, and large-scale industrial applications in language, computer vision, and multimedia.

It is maintained and developed by the Berkeley Vision and Learning Center (BVLC) and powered by a community of contributors on GitHub. The community powers ongoing research projects, startup prototypes, and industrial applications in the domain of computer vision, speech, and multimedia.

• DeepDream

DeepDream is a Google program that uses deep learning to find and enhance image patterns. The result is dream-like hallucinogenic visualizations.

It works by forwarding an image through a pre-trained deep convolutional neural network, then calculating the gradient of the image concerning the activations of a particular layer.

After this, the image is modified to increase these activations. This process over-interprets the patterns seen by the network.

• Microsoft CNTK

Microsoft CNTK is an open-source, multi-machine, GPU-based, deep-learning framework. It supports feed-forward, convolutional, and recurrent neural networks for image, speech, and text classification tasks.

CNTK converts neural networks to a computation graph that fully elaborates the training of a network and efficiently schedules computational steps on the underlying hardware. It also dynamically analyzes the compute graph and optimizes latency and resource usage.

• Theano

Theano is a Python library that lets you define, optimize and evaluate mathematical expressions involving multi-dimensional arrays. It seamlessly integrates with NumPy and provides a convenient interface for CPU and GPU.

A key feature of Theano is its ability to compile symbolic graphs into efficient C code dynamically. This results in faster evaluations of expressions and improves performance on both CPU and GPU.

Conclusion

Developers and engineers use artificial intelligence tools & frameworks to solve real-life problems. These tools make numerical computation easier and more accurate. There are many open-source AI frameworks that offer a variety of features for deploying, training, and scaling your models. 

FAQs

Which Framework is Used for Artificial Intelligence?

AI Frameworks are software libraries that support the creation, training, and deployment of AI models. They are designed to help data scientists and developers develop models that can be applied to real-world problems.

What are AI tools?

Many AI tools are available on the market that can help businesses in their data management processes. These tools are designed to help businesses better grasp their data and sort through it for actionable insights.

What Are the Main Areas of AI?

The term artificial intelligence (AI) refers to a group of technologies that use algorithms to process and analyze data for learning.

Using advanced algorithms and neural networks, these techniques help machines to learn and make decisions. Machines can then adapt to new situations and data without being programmed.

What is the Importance of AI Tools for Business?

Artificial Intelligence (AI) is a major business enabler, helping businesses make more efficient use of their data. It helps turn repetitive tasks into automated systems that reduce time spent on mundane processes and increase staff productivity.