Introduction to Artificial Intelligence Levels
Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale. According to McKinsey, AI has the potential to create 600 billion dollars of value in retail bring 50 per cent more incremental value in banking compared with other analytics techniques. In transport and logistics, the potential revenue jump is 89% more.
Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on relationship building, lead nurturing, etc. A company named Gong provides a conversation intelligence service. Each time a Sales Representative makes a phone call, the machine records, transcribes and analyzes the chat. The VP can use AI analytics and recommendation to formulate a winning strategy.
In a nutshell, AI provides cutting-edge technology to deal with complex data that a human being cannot handle. AI automates redundant jobs allowing a worker to focus on the high level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.
History of Artificial Intelligence
Artificial Intelligence is a buzzword today, although this term is not new. In 1956, avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).
Goals of Artificial Intelligence
Here are the main Goals of AI:
It helps you reduce the amount of time needed to perform specific tasks.
Making it easier for humans to interact with machines.
Facilitating human-computer interaction in a way that is more natural and efficient.
Improving the accuracy and speed of medical diagnoses.
Helping people learn new information more quickly.
Enhancing communication between humans and machines.
Subfields of Artificial Intelligence
Here, are some important subfields of Artificial Intelligence:
Machine Learning: Machine learning is the art of studying algorithms that learn from examples and experiences. Machine learning is based on the idea that some patterns in the data were identified and used for future predictions. The difference from hardcoding rules is that the machine learns to find such rules.
Deep Learning: Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, the Google LeNet model for image recognition counts 22 layers.
Natural Language Processing: A neural network is a group of connected I/O units where each connection has a weight associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon the human nervous system. You can use this model to conduct image understanding, human learning, computer speech, etc.
Expert Systems: An expert system is an interactive and reliable computer-based decision-making system that uses facts and heuristics to solve complex decision-making problems. It is also considered at the highest level of human intelligence. The main goal of an expert system is to solve the most complex issues in a specific domain.
Fuzzy Logic: Fuzzy Logic is defined as a many-valued logic form that may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may encounter a situation where we can’t decide whether the statement is true or false.
Types of Artificial Intelligence
There are three main types of artificial intelligence: rule-based, decision tree, and neural networks.
Narrow AI is a type of AI that helps you perform a dedicated task with intelligence.
General AI is a type of AI intelligence that can perform any intellectual task efficiently like a human.
Rule-based AI is based on a set of pre-determined rules that are applied to an input data set. The system then produces a corresponding output.
Decision tree AI is similar to rule-based AI in that it uses sets of pre-determined rules to make decisions. However, the decision tree also allows for branching and looping to consider different options.
Super AI is a type of AI that allows computers to understand human language and respond in a natural way.
Robot intelligence is a type of AI that allows robots to have complex cognitive abilities, including reasoning, planning, and learning.
AI Vs Machine Learning
Most of our smartphone, daily device or even the internet uses Artificial Intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.
AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.
Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is a large science that mimics human aptitudes.
Machine learning is a distinct subset of AI that trains a machine to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.
Where is AI used? Examples
Now in this AI for beginner’s tutorial, we will learn various applications of AI:
AI has broad applications-
Artificial Intelligence is used to reduce or avoid repetitive tasks. For instance, AI can repeat a task continuously, without fatigue. AI never rests, and it is indifferent to the task to carry out.
Artificial intelligence improves an existing product. Before the age of machine learning, core products were built upon hard-code rules. Firms introduced artificial intelligence to enhance the functionality of the product rather than starting from scratch to design new products. You can think of a Facebook image. A few years ago, you had to tag your friends manually. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.
AI is used in all industries, from marketing to supply chain, finance, food-processing sector. According to a McKinsey survey, financial services and high tech communication are leading the AI fields.