Artificial Intelligence is one of the fields that has opportunity for growth and has immense potential to produce high tech computing interconnecting cyber security, Internet of Things and exploration of neural networks in depth.
Table of Contents
What is AI?
Artificial intelligence is the idea of human-like intelligence that can be found in machines. It is a branch of computer science and information technology.
AI is a term that has been around for decades but it is only recently that it has been on the rise. AI has been used in many different fields such as healthcare, robotics, and advertising. The idea behind AI is to create a machine that can think and behave like humans do.
According to the founding father of Artificial Intelligence (AI), “the science and engineering of making intelligent machines”. John McCarthy, Stanford Professor who coined the term Artificial Intelligence (AI) in 1955.
Components of Artificial Intelligence (AI)
- Learning
The initial and essential step of building Artificial Intelligence (AI), similar to learning in humans, is that machines use trial and error methods to obtain a solution that is rightly fit and gives optimized needful results. The learning component includes memorizing items like problems, language, vocabulary, training data set.
- Reasoning
The ability to observe, learn and derive inference from a situation is reasoning.
- Problem Solving
It refers to forming efficient algorithms, analysis, using predictive, classifying and regression-based models to solve certain problems.
Popular algorithms include: – Hill climbing, N Queen, Breadth First Search, Depth First Search, Depth Limited Search.
- Perception
Any component of the sensory system of the AI that perceives the environment and the changes that take place in it.
- Language
Artificial Intelligence (AI) is designed in such a way that it can understand the most widely used human language, English. As a result, the platform enables computers to readily understand the many computers programme that are executed on them.
Artificial Intelligence (AI) Agent
Artificial intelligence agents are a type of software that can perform tasks and make decisions like a human would. For example, these agents are often used to automate customer service.
In the future, artificial intelligence agents will be able to do more than just customer service and will help humans with all sorts of tasks. One example is scheduling meetings with other people.
The concept of AI agents is not new. There have been many examples of AI agents in literature, movies, and TV shows. Some examples are HAL 9000 from 2001: A Space Odyssey, KITT from Knight Rider, and JARVIS from Iron Man.
It is an element that is capable of perceiving information from the environment obtained via sensors, process the data and take actions accordingly. Agents also compose actuators which convert the data and instructions into action through motion.
Types of Learning of Artificial Intelligence (AI) Agents
- Supervised Learning:
The way of training in which the model undergoes the process of fitting data through establishment of relation between several variables, deriving the correlation between them and forming a mathematical equation or function that represents the observations producing solutions to the values to be predicted. It is suitable for classification and regression-based models.
Example: – Recognition of writing, speech, objects, actions.
Algorithms usually used include linear regression, multiple regression, logistic regression, decision trees, random forest.
There are two basic types of regressions that we use: –
Linear Regression
The definition of relationship between an independent and dependent variable. Involves only two variables to derive results for a given situation. You can also use linear regression to estimate a salesperson’s total annual sales (the dependent variable) based on independent variables like age, education, and years of experience.
Multiple Regression
The mathematical way of representation of more than one independent variable in a weighted format to process the value of a dependent variable. Representation of the equation: –
it can be applicable while predicting the expected crop yield with the consideration of climate factors such as a certain rainfall, temperature and fertilizer level, etc.
2. Unsupervised Learning
Unsupervised learning, also known as unsupervised machine learning, is a type of machine learning in which the algorithm is not given any feedback about its performance. It is comparable to the learning that occurs in the human brain while learning new things. The unsupervised learning algorithm’s goal is to recognize visual features on its own.
An example of unsupervised learning is clustering, where the algorithm groups data into clusters based on their similarity.
3. Reinforcement
According to Wikipedia we can define the above mentioned as: –
“Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.”
Robot navigation, Robo-soccer, walking, juggling, and other applications require RL.
AI is an evolving concept that witnesses upgrades periodically. Every generation of the AI can be categorized according to its capabilities. On the basis of capabilities, we can classify AI into: –
- Narrow Intelligence
Also named as weak Artificial Intelligence (AI), it is the most realistic technology developed. It is goal based. The term “weak AI” refers to the fact that, while the machines appear intelligent, they operate within a limited set of limits and limitations. Narrow AI does not attempt to mimic human intelligence; rather, it simulates human behavior based on a limited set of settings, parameters, and circumstances. Examples: – Cortona, Siri, Alexa, Google Assistant.
- General Intelligence
The theory of mind Artificial Intelligence (AI) framework is used by strong AI. It’s the ability to predict other intelligent entities’ wants, beliefs, emotions, and mental processes. It focuses on actually understanding persons rather than simulation or reproduction.
- Super Intelligence
This can be termed as the state of Artificial Intelligence (AI) where it surpasses the level of intelligence of humans, can process emotions and are conscious about their surroundings.
Related Concepts to AI
- Machine Learning
According to GeeksForGeeks we can define Machine Learning as: –
“Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: “The ability to learn.”
Machine learning is a field of computer science and artificial intelligence concerned with the design and development of algorithms that allow computers to “learn” from data, without being explicitly programmed.
The term “machine learning” was coined by Arthur Samuel in 1959. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. The process of machine learning involves giving a computer data, such as pictures, and letting it analyze the data to find patterns on its own.
A machine-learning algorithm builds a mathematical model from example inputs in order to make predictions or decisions based on new inputs. Some machine-learning systems use statistical techniques such as regression or clustering; others use rule-based logic, while some use both approaches or other techniques.
- Deep Learning
Deep Learning is an important branch of machine learning. It is a type of neural network that has many layers. Neural networks are machine learning models that are inspired by the human brain.
Deep Learning was originally developed for speech recognition and language processing tasks, but it has been applied to other domains such as computer vision, natural language processing, social media analytics and bioinformatics.
Deep Learning algorithms can be trained on large amounts of data and learn from the data without being programmed what to do.
The first Deep Learning research was done in the 1950s through 1960s by Frank Rosenblatt and his students at Cornell University. Now, there are many neural networks like CNN, RNN, and ANN.
Job Opportunities in Artificial Intelligence: –
- Data Analyst
In the new age where data is the new currency, the analysis of the data of consumers and companies help the leaders to make decisions that affect the market and businesses. This role mainly analyzes the data and represents it in a form that is easily interpreted and understandable.
- AI Engineer
Deals with building of models and generation of products that help the managers and stakeholders understand the results.
- Data Analysis and Mining
Tasks include making forecast outcomes, looking for anomalies, trends, and other patterns in massive data sets.
- Big Data Analyst
Create technologies that enable firms to communicate and share information.
- BI developer
Analyze large data sets to spot market and business trends.
- Research Scientist
Exploring the depth of mathematics and computation deriving new models and functions that generate optimum results. Hope this article motivates you to learn more about AI!
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Reference Articles: –
- https://www.simplilearn.com/data-analyst-job-description-article
- https://onlinedegrees.sandiego.edu/artificial-intelligence-jobs/
- https://towardsdatascience.com/understanding-the-difference-between-ai-ml-and-dl-cceb63252a6c
- https://www.geeksforgeeks.org/machine-learning/
- https://www.deccanherald.com/brandspot/pr-spot/what-are-the-3-types-of-ai-853275.html
- https://hai.stanford.edu/sites/default/files/2020-09/AI-Definitions-HAI.pdf
- https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp
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Ms. Aaditee D. Pate is a Third year Computer Engineering student from Fr. C Rodrigues Institute of Technology, Vashi. She remains interested in opportunities in the field of Business Development, Management and Entrepreneurial ventures.
Useful Content. Thanks for sharing