Artificial Intelligence Future ?

Why do we need Artificial intelligence??

We people in today’s world have been hearing the word artificial Intelligence.  But most people don’t know exactly what does this Artificial Intelligence means? and moreover why it is necessary in today’s world. But today the people do not know that this Artificial Intelligence has been everywhere in today's world. For every decade, the population is being increased quickly. And the hidden fact that we need to know is that we were already using Artificial Intelligence. But the people still think that term is new and we need to apply. And the major reason for why do we need artificial Intelligence is that because of the population.

So do you think that population is the reason for the involvement of Artificial Intelligence? Yes, because, the population is being increased day today. And the requirements of the client are also day-to-day. So today the organization cannot afford the on-demand client requirement in a stipulated amount of time. So in this scenario, they need an alternative to over the problem. The alternative, that we have today is the application of Artificial Intelligence. Click the Artificial Intelligence Github Code to know how does it solve the problem.

Handling Data

A hallmark of machine learning is the massive amounts of data curated when it is exercised. Managing this data requires significant responsibility to safeguard the consumer’s privacy and other rights, whether their data is simply stored or used to make decisions, automated or not. Any initiative to harness data for machine learning must include methods for identifying and protecting sensitive data within large datasets, both structured and unstructured. Our industry is already aware and conforming with the requirements of HIPAA and PCI, but it’s important to remember that when business decisions are made that affect consumers, meaningful information about those decisions must be provided, care must be taken to treat protected classes equally (ECOA), and EU citizens must be provided with a way to opt-out and information into the decision process. The power and scale of AI’s capabilities should not be taken lightly as these factors are brought under consideration.

Compute Power:

Machine learning, especially deep learning, runs well on hardware using GPU chips for massive parallel instruction processing and extremely fast memory. As a result, GPU chip demand has skyrocketed and the major cloud services suppliers have purchased a large percentage of available GPUs. And while AI platforms can be installed onsite, businesses who build this within their own data centers are generally creating AI technology in house. There’s good news though: Companies provide solutions that deliver intelligence or intelligent functions commercially. These companies draw the data they use to train and execute machine learning models into their data centers or to their virtual private cloud (VPC) in a commercial or FEDRAMP cloud platform. There are a few vendors who reluctantly allow the customers’ data to stay on-premise for an additional charge if the customer purchases their own hardware. Because latency matters with today’s expectations of the technology, AI at the Edge is the next frontier, focusing on processing AI functions on edge devices: smartphones, wearables, cars, traffic lights, factory machines. In other words, AI will be embedded in the Internet of Things (IoT).

Selecting Tools for the Purpose:

Artificial intelligence replaces or augments human presence in business processes, allowing us to pull redundant and tedious tasks out of human hands. Machine learning and deep learning applications make those tasks done by the system intelligent, learning with more experience and data, without rules written by humans. Here’s where we depart from earlier automation of business processes in receivables. For our industry’s entire history, we have relied on those long-tenured or deeply-experienced managers to translate their smarts into dialer and correspondence strategies, what to say on the phone, how to respond to denials and other processes. Machine learning promises to create good outcomes by reviewing all of what has gone before and charting the path to successful actions. Replacing a human with a computer requires the computer to be fast enough to make the process work. Some use case results can wait a day or can be allowed to take minutes or seconds, while others must feel instantaneous.

Takeaways

Artificial intelligence is an exciting opportunity for our industry to improve results while keeping a sharp eye on margins. With all the hype, it’s important we keep our heads, understand the context and uses for artificial intelligence in its various forms, and continue to monitor the regulations that will govern behavior influenced by automation. As with several of our delivered solutions, Ontario Systems will seek good technology and partners to provide what they do best, embedded in our best-in-class workflow and integrated contact management. Each time, we will examine the business case to ensure the solution delivers value and enables you to take advantage of opportunities or solve problems in your real world.

Reactive machines :

A good example of this Reactive machine is Deep blue. And this is usually applied in Chess Board. This can identify the pieces of the chessboard and make predictions. But the drawback is that it does not have memory. It means, it cannot use past experiences to predict the future. So it just uses the current situation and just moves the pawn. It is intended for the application of a small situation. The situation could be handled within a moment itself.

Limited Memory :

This type of AI is applied where there is a need for memory. Some machines work based on past experience. It means information about the same thing that happened in the past and does the current work accordingly. This type of AI majority used in the areas of self-driving cars. It uses its memory to act in situations like traffic collisions.

Theory of mind :

This type of AI refers to the understanding of other behaviors and do the work according to It includes the feeling.  the intention, moves of the other person. In general, this kind of AI still does not exist. And today most of the scientists ware working hard to get it practically.