Cognitive computing utilizes self-learning or deep-learning algorithms backed by natural language processing, artificial intelligence and extensive data resources ("Big Data") to operate in a manner similar to the way the human brain thinks and works when attempting to solve problems.
One of the key reasons enterprises have committed substantial resources to the area of cognitive computing is its potential for use in applications like healthcare, finance, law and education, where vast quantities of complex data can be efficiently and effectively processed and analyzed in order to solve complicated problems and help improve human decision making.
By providing cognitive computing platforms with Big Data, artificial intelligence and self-learning algorithms, these systems are able to "learn" more and increase their accuracy over time, as they're able to develop an elaborate neural network that provides considerably more flexibility and adaptability than a traditional decision tree-based data modeling approach.
In order to implement cognitive computing in commercial and widespread applications, a cognitive computing system must have the following features:
Adaptive: The system must reflect the ability to adapt (like a brain does) to any surrounding. It needs to be dynamic in data gathering and understanding goals and requirements.
Interactive: The cognitive system must be able to interact easily with users so that users can define their needs comfortably. Similarly, it must also interact with other processors, devices, and Cloud services.
Iterative & Stateful: This feature needs a careful application of the data quality and validation methodologies to ensure that the system is always provided with enough information and that the data sources it operates on deliver reliable and up-to-date input.
Contextual: Ability to understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal. It must draw on multiple sources of information, including both structured and unstructured digital information.
Voice recognition is a computer software program or hardware device with the ability to decode the human voice. Voice recognition is commonly used to operate a device, perform commands, or write without having to use a keyboard, mouse, or press any buttons. Today, this is done on a computer with ASR (automatic speech recognition) software programs.
Many ASR programs require the user to "train" the ASR program to recognize their voice so that it can more accurately convert the speech to text. For example, you could say "open Internet" and the computer would open the Internet browser.
Voice computing applications span many industries including:
•Voice Assistants,
•Healthcare,
•e-Commerce,
•Finance,
•Supply Chain,
•Agriculture,
•Text-to-Speech,
•Security,
•Marketing,
•Customer Support,
•Recruiting,
•Cloud Computing,
•Microphones,
•Speakers,
•Podcasting.
By providing cognitive computing platforms with Big Data, artificial intelligence and self-learning algorithms, these systems are able to "learn" more and increase their accuracy over time, as they're able to develop an elaborate neural network that provides considerably more flexibility and adaptability than a traditional decision tree-based data modeling approach.
In order to implement cognitive computing in commercial and widespread applications, a cognitive computing system must have the following features:
Adaptive: The system must reflect the ability to adapt (like a brain does) to any surrounding. It needs to be dynamic in data gathering and understanding goals and requirements.
Interactive: The cognitive system must be able to interact easily with users so that users can define their needs comfortably. Similarly, it must also interact with other processors, devices, and Cloud services.
Iterative & Stateful: This feature needs a careful application of the data quality and validation methodologies to ensure that the system is always provided with enough information and that the data sources it operates on deliver reliable and up-to-date input.
Contextual: Ability to understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal. It must draw on multiple sources of information, including both structured and unstructured digital information.
Voice recognition is a computer software program or hardware device with the ability to decode the human voice. Voice recognition is commonly used to operate a device, perform commands, or write without having to use a keyboard, mouse, or press any buttons. Today, this is done on a computer with ASR (automatic speech recognition) software programs.
Many ASR programs require the user to "train" the ASR program to recognize their voice so that it can more accurately convert the speech to text. For example, you could say "open Internet" and the computer would open the Internet browser.
Voice computing applications span many industries including:
•Voice Assistants,
•Healthcare,
•e-Commerce,
•Finance,
•Supply Chain,
•Agriculture,
•Text-to-Speech,
•Security,
•Marketing,
•Customer Support,
•Recruiting,
•Cloud Computing,
•Microphones,
•Speakers,
•Podcasting.
