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API stands for “Application Programming Interface.” It is a set of standards, instructions, and requirements that enables a computer program to import or access data, features, and services of another program. In layman terms, APIs are a gateway for apps or software applications that allow them to communicate with each other.

The most famous and common API used by mobile developers is Facebook’s API. It is primarily used at the time of user registration. Facebook’s API gives mobile developers limited access to the profile of Facebook members. This helps mobile developers to simplify the user registration process.

Another example is the Google Maps API. It is very popular for the apps that require mapping and location services. Unlike Facebook, Twitter and LinkedIn’s social APIs, Google charges for its map API if the requesting application is paid, or exceeds 100k API queries within a 24 hours period.

Integration of APIs is crucial for the mobile app development process, as it abolishes the need for creating every app program from scratch. Instead of reinventing the wheel, the development teams can utilise existing apps or platforms that allow API integration. It boosts their mobile app’s time to market and reduce the cost of development.

Terminology Used Frequently
API Key: The API key is an authentic code passed by the mobile app owner to the API requester. It is to recognize the receiver of the API.

Endpoint: The API communication happens between two software systems. One end of the channel is coined as Endpoint.

POST: It is an HTTP method to build resources using the REST ful API.

JSON: JSON (JavaScript Object Notation) is a data format used for request parameters and response body.

GET: GET is the HTTP method of obtaining resources from RESTful API.

OAuth: It is an open standard authorisation framework to access from the user’s side without sharing the credentials.

Latency: Latency is the time taken by an API from request to response.

Rate-limiting: It is the process of limiting the number of requests a user can make on the API.

API Throttling: The process of regulating API usages by the users is termed API throttling. So, when the user crosses its daily or weekly API request limit, the server alerts the user.

REST: Representational State Transfer or REST is a type of programming architectural implementation that enhances the communication efficiency between both the endpoints. As the name itself elaborates, the REST programming shares only references of the data instead of sharing the data itself. Systems built on this architecture are denoted with ‘RESTful’ systems. The most successful example of a RESTful system is the World Wide Web.

SOAP: Simple Object Access Protocol or SOAP is a protocol to share structured information on computer networks SOAP is a messaging protocol and works and XML information set for message format and application layer protocols for message negotiation and transmission.

How API Development Works?
Suppose that you are on a website to book a flight. The site asks you to fill up a form with details such as departure date, preferred time, domestic or international flight preferences etc. Right after you fill-up the form, you get the list of several flight options with details like pricing, seat availability, timings etc.. Ever wondered how it happens?

Artificial Intelligence (AI) technology is reshaping the way enterprises extract insights from data. The Gartner’s most recent hype cycle report on Types of AI points out that AI is the most favourable CIO technology initiative for the next five years as a source of business transformation.

PwC predicts that by 2030, AI can potentially contribute around $15.7 trillion to the global economy.

Many organizations believe that AI is not just a business enabler, but that it is having fundamental impacts on the function itself. AI is automating some long-standing functions to deliver upon their demand for innovative approaches and greater involvement from the IT departments.

Long story short, AI is a big deal. Various flavors of cognitive capabilities make AI a success. It becomes critical for enterprises and business leaders to understand the types of AI and the impact that it will have on the IT operations and market.

Five types of Artificial Intelligence 1. Machine Learning
In the current scenario, machine learning (ML) is the most relevant and popular subset of AI. The Executive’s Guide to the real-world AI, a recent report by Harvard Business Review Analytic Services, stated that ML has been around for years and has matured into a technology.

ML allows computing devices to self-learn from data and implement those findings without any human intervention. Often when a solution/result is hidden in a huge data set, ML is really very helpful. ML is outstanding at data processing and pattern recognition as it takes a fraction of the time that a manual process would take.

Use Cases
Just to list a few of the ML use cases in the real world, ML is used in fraud detection, portfolio management, and risk analysis for financial services. It is also used for targeted marketing campaigns, GPS-based fleet tracking solutions and travel predictions.

2. Deep Learning
Any computer program that does something smart is powered by AI, which is an umbrella term that holds ML as a subset. Here, Deep learning is a subset of ML that works towards mimicking the human mind really closely.

CompTIA explains that deep learning allows computer programs to process the problems in multiple layers, simulating the human brain analytical capabilities. The deep learning technologies extract the meaning out of information to build context. While, to increase the chances of getting the correct conclusion, deep learning empowers computer applications to understand the various components of the inputs such as text or visual images.

One explanation by deep AI says that this technology learns from processing the labelled data that is provided during the training. To process the labelled data, deep learning uses neural networks (yeh, that’s what they call them). The output of this processing is used to learn the attributes of the input that were required to come to the correct output. As soon as the sufficient number of inputs have been processed as examples, the so-called neural network can start processing new data.