DigiFutura Earns Recognition on Techviewer

In the innovation race of today, you cannot take the back seat by just developing a website or mobile app. To beat the business competition and proliferate your customer base, you have to incorporate new technologies into your marketing solutions. One of such technologies that are a hype is the market is Artificial Intelligence (AI).

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In recent years the field of AI has influenced the mobile app development sector. So just go through this guide and take a look some valid reasons why your business should invest in AI enabled mobile apps. For more information- http://bit.ly/2FWbE9H

For instance, their predictive models are predicting Sepsis in Pediatric patients, and the real-time audio signal analysis helps in delivering more audible sounds to people using hearing aids.Retail — AI is being used in the entire retail product cycle from product designing to selling.

Online merchants and retailers are investing in AI to enhance their customer experience.

Each of them is aimed at specific applications and uses specific technologies for them.COGNITIVE COMPUTINGCognitive computing refers to the imparting of the human thought processes in a computerized model during complex situations where the answers can be equivocal and undetermined.

It imitates the way humans think, learns, and adapt, thus, allowing a broader range of real-time insights and actions.For instance, cognitive computing helps the HR department in making hiring decisions, assists in determining the diagnosis and treatment based on the patient’s data provided by doctors, and helps the call centres in improving their customer experience.The technologies that Cognitive Computing uses to enable these actions are:Cognitive Intelligence — It comprises tools and technologies that enable bots, applications, and websites to view, listen, speak, and understand the user requirements via natural language.Emotion Detection — It tries to determine people’s feelings by utilizing capabilities such as expression analysis in picture or video, change of pitch in speech, and much more.Sentiment Analysis — It uses natural language processing, text scanning, and computational linguistics to recognize, extricate, measure, and study affective conditions and personalized information in messages, images, voice notes, and videos.MACHINE LEARNINGThis application enables systems to learn and upgrade automatically from experience without being directly programmed.

The goal of machine learning is to develop computer programs that can obtain data and use it for learning on their own.Basically, machine learning is used in:Machine Vision — It uses machine-learning-based image analysis and detection or automated AI to give image detection capabilities to systems like humans.Pattern Matching — Its algorithms examine the existence of a given series of data in a bigger set of data.DEEP LEARNINGDeep Learning is a sub-unit of machine learning which has networks capable of unsupervised, semi-supervised and supervised learning from untagged and unformed data.

These systems continue learning and directing themselves as new data moves in.Deep Learning can aid in interactive real-time applications that include machine translation and visual and voice recognition using the following technologies:Neutral Network — These are data and computational systems that imitate the human brain for interpretation of data and patterns recognition.Fuzzy Logic — It uses arithmetic methods and tries to estimate human reasoning to make conclusions on raw and equivocal data.Image Recognition — It uses data to recognize objects such as building, people, places, etc.