Introduction
Artificial Intelligence (AI) encompasses a wide range of subfields, used for different purposes and in different contexts. Let us have a more in-depth look at some of these subfields of AI.
Source: https://www.youtube.com/watch?v=2ePf9rue1Ao
Machine Learning
As we have already discussed, Machine learning (ML) is the process through which a computer learns to think logically and draw conclusions based on available data, without being explicitly programmed to do so. ML is currently one of the most in-demand and demanding disciplines in advanced technology. It is actively employed in everyday life, including ML applications that many people are unaware of. As such, it has become the science that allows machines to interpret, execute and examine data in order to solve real-world issues.
To create a complete ML system, programmers use complex mathematical expertise to construct machine learning algorithms that are programmed in a machine language. Machine learning enables us to categorise, analyse, and estimate data from a dataset in this approach. ML has enabled the development of self-driving cars, picture and speech recognition, demand forecasting models, useful web search and a variety of other use cases. It essentially converges on applications that learn from their mistakes and improve their decision-making ability or forecast accuracy over time.
A number of ML techniques exist:
- Supervised Learning: Data professionals supply labelled training data to algorithms and defined variables to algorithms for accessing and detecting correlations in this form of learning. The algorithm's input and output are both particularised/defined.
- Unsupervised Learning: Algorithms that train on unlabeled data and analyse datasets to generate meaningful connections or inferences are examples of this sort of learning. Cluster analysis, for example, is a technique that employs exploratory data analysis to uncover hidden or grouping patterns or groups in datasets.
- Reinforcement Learning: This is a technique for teaching a machine to complete a multi-step process with precisely stated criteria. Programmers create an algorithm to fulfil a task and provide it with positive and negative signals to act as the algorithm executes. Occasionally, the algorithm will decide for itself what step to take in order to proceed. This differs from other types of ML techniques as in Reinforcement Learning, the system isn’t trained on a sample data set, but is instead learning through a trial-and-error process.
Source: https://www.youtube.com/watch?v=f_uwKZIAeM0
Consequently, Deep Learning, as a subset of ML, structures algorithms in layers to create Neural Networks that are able to learn and make independent intelligent decisions, without the need of human input. These networks function in the same way that human neural cells do. They're a set of algorithms that capture the relationship between several basic factors and interpret the data in the same way that a human brain does. Consequently, a Neural Network identifies fundamental linkages across enormous volumes of data using an approach that mimics the operation of the human brain. These are widely used for fraud detection, risk analysis, stock-exchange prediction and sales prediction.
Natural Language Processing
As we have already discussed, Natural Language Processing (NLP) is the science of machine reading, understanding, and interpreting a language. Consequently, when a machine recognises the user's intent, it responds appropriately. Consequently, NLP allows computers and humans to communicate using natural language in layman's terms. It's a method for searching, analysing, comprehending, and extracting information from textual input. NLP libraries are used by programmers to teach computers on how to extract meaningful information from text data. For instance, a popular application of NLP is found in email filtering, whereas computer algorithms can assess whether an email is garbage or not by looking at the topic of a line or the text of an email.
Using NLP has a number of advantages, including:
- increase in document correctness and efficiency
- automatic generation of readable summary textual output
- provision of framework for personal digital assistants and chatbots
- facilitation of sentiment analysis
As such, text translation, speech recognition and sentiment analysis are examples of NLP applications. Twitter, for example, employs NLP to filter terrorist language from various tweets, while Amazon uses NLP to read customer feedback and improve the overall user-experience.
Computer Vision
As we have already discussed, Computer Vision is an important subfield of AI, as it allows the machine to detect, analyse, and explain real-world visual inputs. It uses Deep Learning and pattern recognition to extract visual information from any data, including images or video files within text documents and images, amongst others. For instance, robots use Computer Vision to see the world around them and make real-time decisions.
Cognitive Computing
As we have already discussed, the purpose of Cognitive Computing algorithms is to think and function in the same way and alongside humans. Cognitive computing can communicate this information and can thus aid humans in real-time decision-making. This is achieved through the ability of machines to learn and comprehend human behaviour whilst engaging with humans, and then duplicate the human thought process in a computer model. The most common applications of Cognitive Computing are found in AI-powered virtual assistants found in mobile and smart home devices.