Intelligent apps, sometimes referred to as intelligent personal assistants, are software programs that are designed to perform specific tasks or activities. These can be applications that perform general functions, like taking pictures, scheduling appointments, analyzing data, commenting on various topics, etc. On the other hand, these may also be complex programs that are specifically designed for particular tasks or activities. Intelligent apps can be categorized into two major categories: artificially intelligent and social. While the former are typically found in businesses, the latter is commonly seen on the web, in social networking websites and in the apps that many individuals download from the internet.
Intelligent apps are those that make use of real-time data and historical and current information from user activities to make intelligent suggestions and recommendations, delivering highly customized and adaptable user experiences. The most common intelligent apps. Chat Bots, virtual assistants and recommendations engines on social networking sites are some examples of highly intelligent apps. With the rise of smartphones and smartphone apps, developers are also coming up with smarter and more capable mobile apps that make use of the power of real-time data.
Most of the time, users do not need to interact with an application to make it intelligent. Instead, they will only be able to tell if the app is ready or not. Although there are third-party applications available that let users tap into their phones to let them know if the app is ready or not, most of the time, these apps are not free. Developers have to pay a certain amount before they can let the app run on a smartphone. However, there are also some intelligent apps that do not need to download any extra apps, so users will not have to worry about downloading and installing additional apps for it to become intelligent. In this manner, users will not only be able to determine if the app is ready or not, but also will be able to determine what features the app should have to make it more intelligent.
However, there are many differences between normal web applications and i Apps. One of the biggest differences is that there are no long-term relationships with information sources. Rather, i-Apps rely on crowdsourcing (i.e., users submitting short answers to questions) for their information-driven experience. They also use highly advanced algorithms, coupled with machine learning technology to filter out extraneous information, and only present the necessary pieces of data that are relevant to the user’s current needs.
Another difference is that normal web applications are typically integrated with web-based services, like email, search engines, social networks, calendars and more. On the other hand, most intelligent apps do not integrate with existing systems. Users will only need to access the app through its URL, which will always be displayed at the top or bottom of the user’s browser window. Because it is not integrated with existing systems, the information-driven experience in an intelligent application is far more engaging than the same experience in a web browser. For example, a Facebook user can scroll down the page in a separate tab, view the information on another platform and still continue to be engaged with the app.
The biggest difference between web-based apps and i-Apps is the design. Web-based applications are designed as a single webpage that interacts with the user through web-browser windows. In contrast, most intelligent apps are designed as multiple web pages that each interact with the user via their URLs (which are URLs that connect the browser to specific web services). This means that web-based intelligence will be more limited in the types of features available to the user, whereas intelligent apps can integrate a wide range of different services and formats.
Perhaps the biggest differences between these two types of apps lies in the fundamental difference between information science and artificial intelligence. Artificial intelligence pertains to applying algorithms to large databases of unsupervised data in order to achieve some kind of intelligent function. Information science is the discipline that deals more with supervised information. This includes things like biological and chemical information, and financial and industrial information. While both are useful for application in the information science and business domains, they diverge when it comes to intelligence.
Information science makes use of databases and allows for the extraction of statistical data. Machine learning uses unsupervised data sources such as the internet and Deep Learning projects that feed on natural language processing algorithms and are capable of pre-trained software agents. While these are useful for providing inputs into an artificial intelligence system for decision making, they do not provide any kind of predictive analytics. Thus, while both are useful for providing inputs for a given solution or task, they do so in very different ways.