Accelerating AI capabilities will enable automation of some tasks that have long required human labour. These transformations will open up new opportunities for individuals, the economy, and society in the world. Many companies,bank and Government organisations  are now using Artificial Intelligence to reduce work load and improve out put. 

What is artificial intelligence?

According to a basic definition, AI is the science and engineering of building intelligent computer programs that can achieve complex goals, such as driving a car, identifying a cat in an image, or suggesting a job you may be interested in. Underneath the broad umbrella of AI are specialized branches, such as machine learning and deep learning.

In order to understand how AI systems help us achieve our goals, it’s important step back and look at how these algorithms work.

• You identify a broad objective for the AI system, like “provide new job opportunities for our members that match their skills and interests” or “provide recruiters with a list of candidates that both match a given search criteria and are likely to result in a successful hire.”

• Have a set of intermediate metrics (called “relevance” metrics in Figure 1) that are used as a proxy for how well the system is achieving its goal. This is often necessary because the original product metrics (for example: successful hires) are are not something that a machine learning algorithm can directly optimize easily. In our example, these metrics could include the number of members that apply to the jobs they are given, the number of confirmed hires, the number of members that click on job listings, etc.

• You create an algorithm that improves (according to your relevance metrics) upon your existing method of generating results from data. For example, a model could use a different criteria to recommend job opportunities to members that results in an increased number of members clicking on job listings, which is used as an indication that the job recommendations have improved.