Domains of Artificial Intelligence
Machine learning
Machine learning is an important subset of artificial intelligence. Operational policy is based on data coming from all types of structures and formats.
For machine learning (ML) users must provide input data in its algorithms. Its decision is made on the basis of a set of input historical data. If any changes are made to the data, it recalculates and integrates the changes in decision making.
There are three main methods used in Machine Learning
✌Supervised Learning
✌Unsupervised Learning
✌Reinforcement Learning
The magic of machine learning is simple, use data to understand past events, predict the future, and suggest solution actions. Achieved by the study, interpretation and forecasting of ML
✌Logical sequencing of the input data
✌Decision making using elements and variables
✌Principle knowledge for training the system
Some of the most widely used examples suggest accessible routes to your travel destinations, great movies, best books based on your past tastes and more.
Deep learning
Deep learning is a subset of machine learning that does not require human supervision. Is the self-training process of learning by analyzing the input data of all shapes and forms. This unrefined data is collected, categorized, and labeled into subcategories based on similarity.
The machine executes programs and algorithms to relate the input data to the output decision. The only condition it must meet is that the publication decision is based on logic and rationality. It goes through countless permutations and combinations to achieve this single desired output.
However, in-depth learning leads to understanding and relating different human behaviors and attitudes. This includes learning
Reading various types of human emotions
✌Identifying and differentiating humans and animals through images
✌Knowledge of their habits, gestures, features, locations etc
✌Voice recognition of the people, trying to understand their accent, nasal tone, etc
Computer vision
Computer vision is an integral part of 'deep learning'. Human vision teaches its brain to explore for a lifetime through a variety of animate or inanimate scenes. It categorizes a person’s needs; Right or wrong, such as staying or moving, conflict or settlement. Brain decision making is possible due to assistance from the retina, optic nerve and optic cortex.
Computer vision proves the same ability and it surpasses human ability in some areas. Data and algorithms are generated using statistical data and mathematical sequencing. The computer vision system enjoys a unique position in the use of both authentication and image recognition.
CV uses visuals extracted from various sources such as digital content, graphics, and documents. This acquired capability enables the computer to instantly identify, analyze, decode and select real-time situations.
This technology is already in use and is benefiting in areas such as health and safety.
Natural Language Processing (NPl)
NLP is a set of capabilities introduced to computers to understand human languages. Like human organs (eyes, ears, etc.), computers are designed to read, hear, and speak.
Just as the human brain processes input using its components, so do input processing computers. It converts the code into comprehensible for computers.
The NLP process is facilitated through two phases:
✌Data is preprocessed to ensure the given data is in a workable format for the machine to analyze.
✌Highlights features in the stream that an algorithm can work with.
Through implementing NLP, the results are visible such as:
✌Voice activated devices (Alexa etc), in service all time.
✌Assistance in customer support through ‘chatbots’.
Neutral Network
The role of neutral networks in AI is parallel to the role of the human anatomical brain. This is the alchemy between mathematics and statistics. The use of 'neurons' to collect and classify according to labels (mathematics) and network is similar to regression analysis (statistics).
This network consists of layers of interconnected terminals that look like multiple linear regressions. There are three main layers
✌The input layer - for storing input patterns
✌ The hidden layer - for fine tuning input layers until the margin of error is reduced to zero. It is assumed that this layer triggers the input layer for predicting the outputs.
✌The output layer - for classification and mapping input layers.
NN is beneficial and used in a wide variety of fields such as forecasting, research, and fraud detection.
Cognitive Computing
The reason for this subdomain of AI is the interaction between humans and machines. AI is primarily about imitating human skills and abilities. Thus, cognitive computer mechanisms begin to observe, learn, behave, and recreate the human thought process.
Several subdomains of AI are being developed. However, we are already using AI in our lives and completely changing the way we operate. Every industry has started to use AI and even accounting firms have their share in the application of AI and accounting automation.