Introduction to AI

What is Artificial Intelligence :

Artificial intelligence is a branch of computer science concerned with creating machines that can think and make decisions independently of human intervention.

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

What is AI Algorithms? :

The definition of an algorithm is “a set of instructions to be followed in calculations or other operations.” This applies to both mathematics and computer science. So, at the essential level, an AI algorithm is the programming that tells the computer how to learn to operate on its own.

An AI algorithm is much more complex than what most people learn about in algebra, of course. A complex set of rules drive AI programs, determining their steps and their ability to learn. Without an algorithm, AI wouldn’t exist.

How does a AI algorithm work? :

While a general algorithm can be simple, AI algorithms are by nature more complex. AI algorithms work by taking in training data that helps the algorithm to learn. How that data is acquired and is labeled marks the key difference between different types of AI algorithms.

AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No single programming language is synonymous with AI, but Python, R, Java, C++ and Julia have features popular with AI developers.

At the core level, an AI algorithm takes in training data (labeled or unlabeled, supplied by developers, or acquired by the program itself) and uses that information to learn and grow. Then it completes its tasks, using the training data as a basis. Some types of AI algorithms can be taught to learn on their own and take in new data to change and refine their process.

Cognitive skills that are more focused in AI Programming:

  1. LEARNING: This aspect of AI programming focuses on acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
  2. REASONING: This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.
  3. SELF-CORRECTION: This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
  4. CREATIVITY: This aspect of AI uses neural networks, rules-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.

Why AI is important? :

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail-oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors.

Because of the massive data sets it can process, AI can also give enterprises insights into their operations they might not have been aware of. The rapidly expanding population of generative AI tools will be important in fields ranging from education and marketing to product design.

Types of Artificial Intelligence:

There are three major categories of AI algorithms: 1. supervised learning, 2. unsupervised learning, 3. semi-supervised learning and 4. reinforcement learning. The key differences between these algorithms are in how they’re trained, and how they function.

Supervised Learning:

In supervised learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs.

While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions. The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high level of accuracy/performance.

Under the umbrella of supervised learning fall: Classification, Regression and Forecasting.

  1. CLASSIFICATION: In classification tasks, the machine learning program must draw a conclusion from observed values and determine to what category new observations belong. For example, when filtering emails as ‘spam’ or ‘not spam’, the program must look at existing observational data and filter the emails accordingly.

2.REGRESION: In regression tasks, the machine learning program must estimate and understand the relationships among variables. Regression analysis focuses on one dependent variable and a series of other changing variables making it particularly useful for prediction and forecasting. 3.FORECASTING: Forecasting is the process of making predictions about the future based on the past and present data, and is commonly used to analyse trends.

Unsupervised Learning:

Here, the machine learning algorithm studies data to identify patterns. There is no answer key or human operator to provide instruction. Instead, the machine determines the correlations and relationships by analysing available data. In an unsupervised learning process, the machine learning algorithm is left to interpret large data sets and address that data accordingly.

The algorithm tries to organise that data in some way to describe its structure. This might mean grouping the data into clusters or arranging it in a way that looks more organised.As it assesses more data, its ability to make decisions on that data gradually improves and becomes more refined.

Under the umbrella of unsupervised learning, fall: 1. CLUSTERING: Clustering involves grouping sets of similar data (based on defined criteria). It’s useful for segmenting data into several groups and performing analysis on each data set to find patterns. 2. DIMENSION REDUCTION: Dimension reduction reduces the number of variables being considered to find the exact information required.

Semi-supervised Learning:

Semi-supervised learning is similar to supervised learning, but instead uses both labelled and unlabelled data. Labelled data is essentially information that has meaningful tags so that the algorithm can understand the data, whilst unlabelled data lacks that information. By using this combination, machine learning algorithms can learn to label unlabelled data.

Reinforcement Learning:

Reinforcement learning focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters and end values. By defining the rules, the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal.

Reinforcement learning teaches the machine trial and error. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result.