Artificial Intelligence is the term coined by John McCarthy in 1956. It refers to the reproduction of human Intelligence into machines that are designed and programmed to think like humans and imitate humans. Therefore, it is inferred that any such machine that shows the traits associated with a human mind, such as Learning and Problem-solving.
What is Intelligence?
Intelligence is the capability to acquire and apply Knowledge and skills. In other words, it is the ability to learn, understand, deal with new or trying situations, or apply knowledge to manipulate their environment. Intelligence can also be defined as the ability to perceive or infer information and retain it as Knowledge to be applied toward adaptive behavior within the environment.
It has been defined in varieties of ways. Intelligence is the capacity for abstraction, logic, reasoning, understanding, planning, self-awareness, creative thinking, critical thinking, emotional Knowledge, and problem-solving. It is the process that entails a set of problem-solving skills that are enabled to resolve genuine problems in the environment.
Therefore Intelligence is learning from experience, recognizing problems, and resolving the issue with the obtained Knowledge.
What is Artificial Intelligence?
Artificial Intelligence is the homogenization of computer technology and physiology. Artificial Intelligence focuses on making computers or machines to behave like a human. The Ideal characteristic of AI is its ability to rationalize and take steps to achieve more specific goals. It primarily focuses on the transmission of anthropomorphic Intelligence and thinking into machines.
AI proposes that machines can acquire Intelligence. It allows the machines to understand and learn to achieve specific goals. AI encompasses machines that can learn on their own, adapt to specific situations, and auto-correct their own mistakes. That is, a machine can think on its own without being encoded with external commands.
AI allows machines to learn automatically from existing data without the help of a human. It is based on the principle of human Intelligence that can be defined in a way that machines can easily mimic and execute complex tasks. The objective of AI is to mimic human cognitive activity. Human cognitive ability is laced with value judgments that are subject to previous experience; therefore, that objective is very tough to achieve, but experiments are underway.
It helps the researchers to gain Knowledge for doing tasks such as translation, NER, speech recognition, relationship extraction, and topic segmentation
The AI translator helps researchers gain knowledge for performing tasks such as translation, named entity recognition NER, speech recognition, relationship extraction, and topic segmentation.
Advantages of Artificial Intelligence:
- Artificial Intelligence’s Judgments are based on evidence rather than feelings.
- Rational decision maker
- Not Selfish, tireless with no breaks
- It doesn’t need rest, and therefore it eliminates the fundamental drawback of human fatigue.
- It can disseminate Knowledge more easily.
- AI can readily duplicate the data from other system environments
- It can handle complicated tasks that are difficult or impossible for human
- AI can complete the task faster than human
- It can commit minimal errors and flaws.
- Its function is limitless.
- Faster and unbiased decision making
- New Invention
- AI can easily handle and process Big Data
- Medical Applications and Treatments
- AI introduces a new technique to solve new problems.
- The success ratio is high
- Discover unexplored things
Disadvantages of Artificial Intelligence:
- AI may become a direct competitor to humans, and human jobs may get affected.
- The creativity will become dependent on programmers
- Lack of personal touch
- Technical reliance may be on the rise
- A robotic repair could reduce the amount of time it takes for people to fix things, but it would cost more money and resources.
- If placed in the wrong hands, machines may quickly bring havoc
- Make humans lazy
- Unemployment
- Lack of emotion
- Lack of Out of the box thinking
- No Ethics
- Less creative and innovative in challenging situations
- Degradation
- No improvement with experience
- AI Classification:
- Artificial Intelligence and AI-enabled machines are classified into four
- Reactive Machines
- Limited Memory Machines
- Theory of Mind
- Self Aware AI
Reactive Machines:
IBM’s Deep Blue is the best example of a Reactive Machine. In 1997 it beat the Chess grandmaster Garry Kasparov. These machines can automatically respond to a limited set of inputs. However, they are not memory-based operations. Therefore they have limited capability. And they cannot learn. Its previous experience cannot be used as input to inform the machine’s future actions, which means it cannot learn from past experiences.
Limited Memory Machines:
These types of Machines have the capabilities of purely reactive machines. Further, they are capable of learning from past historical data and making fine decisions. Current chatbots, self-driving vehicles, and some AI applications fall under this category of AI.
Theory of Mind:
Theory of Mind is the next level of artificial intelligence systems. It is currently considered either a concept or experimental work in progress. They better understand the entities with which they interact by discerning their needs, emotions, beliefs, and thought processes.
Self-aware Artificial Intelligence:
This is the last stage of Artificial Intelligence development. It currently exists hypothetically and in Research. It involves AI systems that have evolved to the point that they are comparable to human brains in which they have self-awareness.
Furthermore, some experts classify Artificial Intelligence into three categories they are:
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
ANI or Artificial Narrow Intelligence:
This type of Intelligence represents all existing AI models. Their functionality of these AI machines is based exactly on what they are programmed to do. Besides, they do have a narrow range of capabilities. These machines fall under the Reactive and Limited-Memory AI categories discussed in the previous part of the post. These Artificial Narrow Intelligence models are also termed as Weak AI.
The ANI or Artificial Narrow Intelligence is the only type of AI we have successfully realized till date. ANI is designed to perform unit ask, such as speech recognition and facial recognition. It is very intelligent and successful in completing that specific task for which it is programmed to do. Since these machines operate under a narrow set of constraints and limitations, it is generally referred to as weak AI. It doesn’t replicate human Intelligence. It just merely mimics or simulates human behavior based on a narrow set of parameters and contexts.
With the advancement of Machine Learning and Deep Learning, Narrow AI has advanced many breakthroughs in recent days. The AI systems used in Cancer diagnosis are the best example of it. ANI machine intelligence utilizes Natural Language Processing to perform tasks. Chatbots are the best examples that employ NLP technology. Chatbots interact with humans in a natural, personalized manner.
Narrow AI:
Narrow AI can be reactive or limited memory machines. Reactive Machines are very basic. They do not have memory or data storage capabilities. It emulates the human mind and responds to different kinds of stimuli without previous experience. Whereas the Limited Memory AI is a little advanced and equipped with data storage and learning capabilities that enable these machines to utilize past historical data to manipulate current decisions. Nowadays, Deep Learning helps AI machines read large volumes of data. Deep Learning enables a more personalized user experience.
Examples of ANI:
- Self Driving Cars
- Apple ChatBot Siri
- Microsoft Cortana
- Amazon Alexa
- Google Search Rank Brain Algorithm
- Face Recognition
- Voice Recognition
- Disease Mapping and Prediction Tool
- Social Media Monitoring tool for hate Speech
- Email Spam Filter
AGI or Artificial General Intelligence:
AGI is the ability of an AI model to learn, understand, perceive and behave completely like a human being. Though the Artificial General Intelligence model has the ability of the machine to perform general intelligent actions, the ANI is about particular problem-solving skills. The AGI refers to the models or machines that exhibit human Intelligence, and therefore it is called Strong AI.
AGI, or Artificial General Intelligence, is called Deep AI or strong AI. It is the concept of a machine with general Intelligence that mimics Human behaviors. Artificial General Intelligence has the capability to learn and apply its Intelligence to solve problems. It can understand, evaluate and act in a way that is similar to a human in any given environment.
Still, scientists are working on strong AI machines. They need to design-conscious machines with a full set of programming of cognitive abilities. And, They need to take their experiential Learning to the next level. They need to improve their efficiency on single tasks as well as gain the ability to apply their experiential Knowledge to a wider range of problems. Strong AI needs to use the theory of mind AI frameworks. The Theory of Mind framework refers to the ability to discern other intelligent entities’ desires, emotions, beliefs, and thought processes.
ASI or Artificial Super Intelligence:
ASI is the reach of the zenith of AI research. If achieved, ASI will change our way of life. The primary objective of ASI is to develop a machine with higher cognitive function than a normal human. The terminator movie character is an excellent example of ASI.
Artificial Super Intelligence is so far hypothetical AI. It doesn’t mimic or understand human Intelligence and behavior.
Artificial Super Intelligence machines will become self-aware and surpass human Intelligence and ability.
ASI concept foresees AI evolving to be so akin to human emotions and experiences; in addition to replicating the Intelligence of human beings, the ASI can theoretically exceed human Intelligence better.
The ASI will have larger memory and a faster ability to process and analyze the data and stimuli in decision-making. In the near future, super-intelligent machines’ problem-solving and decision-making capabilities can be far better than those of human beings.
Branches of Artificial Intelligence:
Here are the following that are branches of Artificial Intelligence.
- Machine Learning
- Deep Learning
- Natural Language Processing
- Robotics
- Expert Systems
- Fuzzy Logic
Machine Learning:
Machine Learning is a component of Data Science. It is another branch of Computer Science and AI that focuses on using data and algorithms to imitate humans. It mimics the human brain in understanding, Learning and improves the end results in the past experience to accuracy. Machine Learning is a milestone in the field of Artificial Intelligence. Using statistical methods, computer algorithms are trained to make classifications and predictions to get insights into data mining. It subsequently drives decision-making within applications and businesses that impact growth metrics. In addition, machine learning algorithms are created to use frameworks to accelerate problem-solving.
Machine Learning is further classified into three categories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning:
Supervised machine learning is the use of datasets to train algorithms to classify data or accurately predict outcomes. Once the data is fed into the model, it will adjust its weights and parameters until it has been fitted appropriately. Then, it further cross-validates the process of the model to avoid under-fitting or over-fitting. Supervised Learning helps to solve real-world problems on a large scale. A supervised Learning model uses labeled train data.
Unsupervised Machine Learning:
Unsupervised Machine Learning uses machine-learning algorithms to cluster unlabeled data sets and analyze them. The PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) are the two common approaches employed in Unsupervised Machine Learning. These algorithms find out the veiled data and data grouping without any external interventions. It can discover the differences and similarities in data. Machine Learning best suits exploratory data analysis, customer segmentation, cross-selling strategies, and image pattern recognition. It can employ a dimensionality reduction process. Unsupervised Learning uses unlabeled training data. Unsupervised-Learning has a limited spectrum for its application.
Semi-Supervised Learning:
It is the combination of supervised and unsupervised Learning. Semi-Supervised Learning uses a limited amount of labeled data and a very large amount of unlabeled data to train the models. It gives the benefit of both unsupervised and supervised Learning. It avoids the challenges of finding a large amount of labeled data. Therefore you can train a model to label data without having to use as much as labeled training data. The basic disadvantage of supervised Learning is it needs hand labeling by data scientists or Machine Learning specialists. In a semi-supervised model, the training data set is a mixture of both labeled and unlabeled data.
Deep Learning:
It is the subset of machine learning. Deep Learning is the process of implementing neural networks on high-dimensional data to get results. They are designed to imitate humans thinking and learning behavior.
Natural Language Processing (NLP):
NLP helps computers to understand, interpret or manipulate human language. It is trying hard to fill the gap between human communication and computer understanding. It is defined as the automatic manipulation of natural language like text and speech by computer software. Further, Natural Language Processing is the machine learning technology to make machines to understand, analyze, manipulate or interpret human language.
It helps the researchers to gain Knowledge for doing tasks such as translation, NER, speech recognition, relationship extraction, and topic segmentation. In addition, NLP helps the developer to draw insights from natural human language in order to communicate with machines. Natural Language Processing employs computer algorithms to enable computer systems to recognize and respond to human communication.
Some of the Applications of NLP:
- Chatbots
- Google Docs
- Microsoft Word
- Grammarly
- Search Engines
- Voice assistants
- Google Translate
Robotics:
Robotics is another branch of Artificial Intelligence that focuses on the application of robots. AI robots are artificial agents. They are all artificial agents acting in a real-world environment to produce results. Blending of machine learning, AI and robotics is a most powerful combination of technological innovations. AI-driven robots are more efficient. It saves time, and human efforts ensure validity, accuracy, and with minimum errors. In addition, AI provides Robots with adequate computer vision and motion controls.
When a robot incorporates AI algorithms, it can act independently after training or the trial and error phase. And they need not require any commands to make decisions. These types of robots can learn, understand concepts, solve problems and provide visual responses. Robots with AI have computer vision. With computer vision, they can navigate, access the environment and decide how to react.
Fuzzy Logic:
Fuzzy Logic is based on the data people make decisions based on rough and non-numerical information. They are the sets of mathematical means of representing precise information. These models can recognize, represent, interpret and manipulate vague data and information. Fuzzy logic is applied in many fields, such as AI and control theory.
It is a form of many-valued logic in which the truth value may vary from 0 to 1. It is employed to handle the partial truth concept, where the truth value may be completely false or entirely true. Fuzzy Logic is a computing approach based on principles of Degrees of Truth instead of normal computer logic. The AI and fuzzy logic approach is almost the same thing. They are trying to imitate the logic of the human neural network, which is fuzzy. Neural networks acquire inputs of multiple values and give them different weights in relation to each other. It does not have the situation of either or decision making. Neural networks and AI can utilize this approach and give results to more accurate models of complex situations.
Therefore, fuzzy Logic is a technique to interpret, represent and manipulate uncertain information. It is employed for reasoning about inherently vague concepts.
Expert System:
An Expert system is an Artificial Intelligence based computer system capable of Learning and reciprocating the decision-making ability of the human Expert. It generally uses “if-then” logical notations to solve complex situations. Expert System does not lay on conventional procedural programming.
It is used mainly in information management and the medical field. The expert System emulates the decision-making ability of a human expert. Expert System is designed to solve complex problems by reasoning through Knowledge, mainly as if-then rules rather than using conventional procedural codes. It is an interactive, more reliable computer-based decision-making system that uses facts and heuristics to solve very complex decision-making problems.
It is divided into two subsystems: inference engines and knowledge base. The knowledge base represents facts and rules, and the inference engine applies the set of rules to the known facts to conclude new facts. An expert system has the potential for explanation and debugging abilities. It has the capability to explain how the expert system reached a particular decision. Now a day, the term Knowledge Engineering is used to mention the process of building an Expert System, and the practitioners are called Knowledge Engineers.
