The term “artificial intelligence” was coined at a conference in Dartmouth College in 1956. But the genesis of the concept can be traced back to the attempts of classical philosophers to define the system of human cognitive ability. AI has become one of the significant technological advancements of the 21st century. According to Grand View Research, the global market size of AI was estimated at USD 39.9 billion in 2019 which is expected to accelerate at a CAGR (Compound Annual Growth Rate) of 42.2% between 2020-2027.
The constant growth and development in industries such as healthcare, finance, manufacturing, retail, has brought AI to the center of focus. As a result of the galloping growth and acceptance of AI by Tech-giants such as Google LLC, Apple Inc, Facebook, Microsoft, numerous AI software are currently in vogue.
AI Software- Definition and Types
A computer program to learn and imitate human behavior in the form of data patterns leads to the utilization of AI Software. Machine Learning, Virtual Assistant and Speech and Voice Recognition are some of the features of AI.
Types of AI Software
- Artificial Intelligence Platforms- The in-built algorithms present on these platforms enable users to create and develop an application from its base.
- Chatbots- these give an impression of a human in a conversation with the user.
- Deep Learning Software- it facilitates face recognition and image recognition.
- Machine Learning Software- enables the computer to learn via data.
Leading (AI)Artificial Intelligence Software
The perfect software solution for business intelligence, safety, and security, Deep Vision has incorporated the Facial Demographics Model for deciphering the variation in demographics of a specific area. Widely used for facial recognition, the software bridges the communication between brands, advertisers, and target audience for the products. The facial demographics are useful in the analysis of an individual’s age and gender. It also harbors the ability to detect the user’s location.
- Google Cloud Machine Learning Engine
The inception of a new business and the growth of an existing one can be led with Google AI Technology. The Google Cloud Machine Learning Engine is efficient for training and analysis of one’s business model. The software enables users to monitor predictions of their business models. Compute Engine, Cloud SDK, and Cloud Storage are part of this software.
- Azure Machine Learning Studio
The software, an advanced tool for users to shift from object to interface, allows one to build AI applications on a cloud. Interactive programming software is created for business intelligence systems. Data collected from various sources allows the user to make an efficient predictive analysis. The creative tools offered by Azure Machine Learning Studio enables users to prepare data by writing R scripts.
Named after the founder of IBM- Thomas J Watson, the software uses a powerful technique that incorporates cognitive computing through the combination of- machine learning, reasoning, natural language processing, AI, and others. Catalysing business processes this software is made with a processing rate of over 80 teraflops is twice a human’s ability to answer queries. With an in-depth understanding of industries and businesses, IBM Watson is appropriate for healthcare, finance, education, and transportation.
Software designed by the research team of Google’s AI engineers, Tensor flow facilitates deep learning and machine learning. It is a source of an advanced solution to computation and numerical analysis. It features natural language processing, image recognition, chatbot, decision making, machine learning, workflow automation, multi-language, virtual assistant, and speech recognition.