49,378 Companies
- United States: 10,750 Companies
- North America: 13,215 Companies
- EMEA: 19,041 Companies
- United Kingdom: 3,391 Companies
- APAC: 9,272 Companies
- Australia and New Zealand: 1,719 Companies
(MSPs, CRM Vendors, Resellers, ISVs, CRM Software Companies) in our database across the globe
What is Artificial Intelligence?
A broad area of computer science called artificial intelligence (AI) is focused on creating intelligent machines that can carry out tasks that traditionally require human intelligence. Despite the fact that AI is an interdisciplinary discipline with many different methodologies, developments in machine learning and deep learning are causing a paradigm change in almost every area of the IT sector. Machines are now able to mimic and even outperform human brain functions thanks to artificial intelligence. AI is becoming more prevalent in daily life, from the rise of smart assistants like Siri and Alexa to the development of self-driving automobiles. As a result, a large number of IT firms in numerous industries are making investments in artificial intelligence technologies.
1. Google Cloud Vertex AI
Cloud by Google Google's search results are powered on a unified artificial intelligence platform called Vertex AI. Excellent flexibility is provided to satisfy a variety of needs. It can aid in the development of complex ML models while avoiding roughly 80% of the coding necessary for other platforms. Due to the fact that the programme is Google-based, you will have access to all of Google's top-tier AI technologies, including Tensorflow, TPUs, etc., as well as Google's open-source platform Kubeflow, which allows you to build portable machine learning pipelines. Additionally, Vertex AI offers pre-trained APIs to accommodate businesses without data scientists.
2. Scikit Learn
In the community of machine learning, Scikit Learn is one of the most popular libraries. It is the preferred library for developers because of features like cross-validation, feature extraction, supervised learning algorithm, etc. On a single CPU, though, it functions. This library is based on SciPy, which also includes IPython, Pandas, Sympy, Numpy, Matplotlib, and SciPy. Instead, than modifying the data, it focuses on modelling it. With this, we've talked about some of the AI software that has been used the most recently. Other AI tools, such as the Theano, Swift AI, Deeplearning4j, and Google ML Kit, are becoming more and more well-liked.
3. Deep Vision
Deep Vision is the ideal AI solution for safety, security, and business intelligence because it was created exclusively for the analysis of human faces. The programme effectively monitors predetermined areas to identify individuals over time based on their age, gender, and other characteristics. It makes use of the Facial Demographics Model to analyse demographic shifts that occur over time in a given area or to monitor purchasing trends. Additionally, it facilitates brand connections with target audiences for product placement and promotion. The model is designed to follow people through facial recognition in order to quantify visitor frequency and assist merchants in quickly identifying potential customers.
4. TensorFlow
The most popular deep learning library is TensorFlow. This Google machine learning framework is an open-source Python library. It is one of the top AI development tools that makes numerical computation easier and more accurate for creating predictions in the future. Developers can concentrate on the application's logic rather than getting bogged down in the details of algorithms. Everything on the back end is handled using TensorFlow. Using Tensorboard, the tool enables developers to build neural networks and produce graphical visualisations. Applications for TensorFlow can be easily launched on your local computer, in the cloud, as well as on Android and iOS devices. It uses both CPU AND GPU to execute because it was designed to be deployable.
5. Azure
The ability of Azure Machine Learning studio to adapt to the user's level of experience makes it stand out. If your team includes data scientists, they may leverage their knowledge to develop complex machine learning algorithms and train and deploy ML models more quickly than other AI programmes. Azure ML does not require any programming knowledge. The platform's drag-and-drop interface enables non-technical users to create straightforward AI applications for process automation and better consumer segmentation. From the application itself, models can be deployed on the cloud. Scalable and compatible with open-source technology, this programme is useful.
6. IBM Watson
It is an artificial intelligence (AI) powered computer system made to respond to user inquiries. Cognitive computing, a combination of approaches that includes reasoning, machine learning, natural language processing, AI, and others, is combined with IBM Watson. This instrument, which bears Sir Thomas J. Watson's name and was created to integrate artificial intelligence into numerous business processes, was the first CEO of IBM. It aids in boosting an organization's productivity and efficiency so that better outcomes can be obtained. The majority of business data is unstructured and takes the form of spoken words, paragraphs, etc. Professionals can logically organise and organise the unstructured data using IBM Watson to generate the required information. Around 80 teraflops are processed each second by IBM Watson.
7. Keras
Keras is a Python-based high-level open-source neural network library. This TensorFlow-based application is incredibly user-friendly and also significantly simpler to use. Fast prototyping is done with it, making it possible to complete cutting-edge tests quickly or even instantly. Both the CPU and GPU work flawlessly with Keras. One of the most effective open-source AI tools available today is Keras. The tool's ability to handle the back end draws developers from a variety of backgrounds to try their hands at writing their own scripts because there are no restrictions on the tool's use due to this.
8.Engati
Users may easily construct chatbots using Engati that are of various sizes and levels of complexity. So that users may rapidly get started with a chatbot, it comes with over 150 templates. The platform also has a sophisticated Conversation Flow creator, superior integration features, and the ability to deploy bots on a website or through any other channel that is available. Chatbot development is now simpler than ever thanks to the platform. The bots' deployment, construction, analysis, and training are all covered in separate parts. Additionally, this programme will help you with campaigns that are aired, live chat, portal users, and chatbot user information.
9. H20.ai
An on-premises and cloud-based open-source AI platform is H20.ai. Although you can configure the platform to process batch data, it scales quickly to process information in real time. Because it can integrate with a wide variety of data pipeline technologies, such as Snowflake, Apache Spark, and H20 Sparkling Water, H20.ai is favoured by many data scientists and engineers. On this platform, models are built using programming languages like R and Python. H2O makes creating, running, and innovating applications using AI in any context simpler and faster. The platform is simple to use, scalable, and has a distributed in-memory architecture.
10. Infosys Nia
By assisting in the improvement of the system and solving complicated problems, Infosys Nia supports businesses and helps them grow. The knowledge platform, automation platform, and data platform make up the essential elements. With Infosys Nia, programming chores may be automated and have a conversational interface. The automation platform contains predictive and cognitive automation. For collecting, processing, and reusing the data, a knowledge platform is used. Data analytics and the machine learning platform are supported by the data platform.
Artificial Intelligence Software FAQs
A broad area of computer science called artificial intelligence (AI) is focused on creating intelligent machines that can carry out tasks that traditionally require human intelligence. Despite the fact that AI is an interdisciplinary discipline with many different methodologies, developments in machine learning and deep learning are causing a paradigm change in almost every area of the IT sector.
- Reactive machines
- Limited memory
- Theory of mind
- Self-aware AI.
- Online shopping and advertising.
- Web search.
- Digital personal assistants.
- Machine translations.
- Smart homes, cities and infrastructure.
- Cars.