Intelligence is not a singular cognitive skill. Rather a collection of skills that can be quantified by dedicated psychological assessment tools. These skills are generativity, abstract reasoning, working memory, arithmetic, adaptive behavior, perception of time, recollection ability, and of course language. Therefore, defining intelligence in a couple of sentences is still a work in progress. But when it comes to artificial intelligence, responses and actions can be programmed according to requirements and the components of AI are not the same as human intelligence.
An artificially intelligent entity reacts based on the training. The training in this case is mostly feeding carefully curated known data. And programming the desired response. So that, the AI can act on unknown datasets and produce the desired results with much ease. Also, to understand and analyze the inputs, an AI needs independent components for perception and processing. All of the same, with all the minute details, will be discussed in this article.
Table of Contents
1.Knowledge base and learning
The knowledge base is the data we need for training an AI entity. The more data, the more accuracy can be expected from an AI. Just like a human being, an AI learns through a lengthy process of trial and error. The training initially concentrates on feeding known data sets and optimizing the entity for the desired outcome. The real-world inputs can be diverse. And a lot of data is essential for preparing an AI for this diversity. Just like a first-timer on a bicycle. Falls, mistakes, and injuries gradually lead to experience and expertise. For AI the process is the same. The developer tries to understand the errors and figures out the reason behind these errors. And based on that the training is modulated for achieving perfection.
2.Reasoning
The reasoning is an exclusively human trait. Years of evolution, a complicated set of neural networks, and experience result in reasoning for human beings. For AI entities the same can be classified into inductive and deductive reasoning. Inductive reasoning is coming up with a probable solution to a problem. But deductive is certain. And both kinds of training involve a huge amount of data. And a lengthy optimization process with minute attention to detail. Writing and grammar assist AIs are the most prominent example of inductive AIs they suggest words, rewrites, and even sentences make them more suitable. Deductive AIs are used in public sectors like traffic and healthcare. The deductions in these cases are accurate. And the AI we are discussing must be trained well with loads of data. So that the provision for mistakes is eradicated from its root.
3.Problem-solving
Problem-solving is among the most essential components of AI. The very purpose of AI is to solve problems without human intervention. The problem-solving training is conducted by evaluating the AI through known problems with known solutions. An AI is trained with such problems so that the solution created by the AI is closer to the ideal solution. Problems for an AI in the real world can be unexpected in terms of origin, nature, and even the approach of the solution. Therefore, each aspect of every possible genre of problems must receive the desired attention. And the AI trained by utilization of ample amounts of relevant data.
4.Perception
Perception for human beings is a process that involves sensory systems and sense organs. But for AI entities these organs are artificial as well. Perception of AIs depends on sensors cameras and microphones. In the case of traffic management Aiso, the perception, part is taken care of by cameras, motion sensors, and IR spectroscopic systems. In the case of an NLP tool, the same depends on microphones and cameras. The AIs perceive changes rather than specific perceptions. They perceive everything from the sensors all the time and only respond to changes. The sensors can be enhanced by computer vision, NLP, or several other machine learning and deep learning tools as per the requirements.
5.Language
Language is the fundamental mode of communication for most human beings. Thus, an adept AI must understand the turns and twists of linguistics. Natural language processing works in both directions. Text to speech and speech to text. And the same are essential components of AI entities like Google and Alexa assistants. In traffic regulation, AIs NLP is implemented for the recognition of numberplates and prosecuting the right vehicle and its driver. In addition to that writing, NLP is ushering in great changes. Writers with certain handicaps can also write by voice. And check the readability of the writeup with much ease.
