What are the six main branches of Artificial Intelligence (AI)?

Nine in ten CEOs say artificial intelligence (AI) is now mainstream technology being deployed directly in the workplace on a day-by-day basis, and not just a mythical future application. AI, and the use of machines, is a multifaceted discipline with numerous exciting use cases. Within this field there are six core branches which all involve computer systems mimicking and demonstrating human intelligence in some way. Key to every subfield is data which these systems use to learn and act.

Artificial intelligence is already enhancing the speed, quality and efficiency of once human-led processes and the potential for further improvement is vast and exciting. If you want to be at the cutting edge of these data-driven advancements, completing an online degree in computer science will give you the skills and knowledge to use machine learning and AI. The Baylor data science program is perfect for students with a bachelor’s degree who want to secure data science roles in the future. The Bureau of Labor Statistics expects significant job growth in this field through 2030.

Machine learning

Machine learning is fundamental to AI as it allows systems to learn things and complete tasks without them being programmed explicitly by a third party. Machine learning models sift through masses of data to find patterns which can then be acted on to help them to learn and improve, and empower AI to make better decisions. All of this is done automatically without assistance, which is what makes it so impressive.

The recent buzz about ChatGPT, a chatbot with the ability to provide human-like answers and responses to simple text prompts, highlights how vital machine learning is to the advancement of AI. ChatGPT relies mainly on machine learning models to generate these responses. As machine learning capabilities advance, AI will develop in tandem, which is why it will continue to be a main area of investment for many leading tech corporations.

Neural networks

As the name suggests, neural networks are modelled on the human brain. Specifically, these networks aim to mimic how the brain works in order to make sense of data sets and determine specific, underlying relationships. Because the brain has a staggering 86bn neurons and 85bn nonneuronal cells, the processes involved are remarkable in scale and complexity. Neural networks aim to code all of these neurons and replicate them in computer systems. The networks rely on a range of statistical techniques to help them with this process such as regression analysis.

Data is again fundamental to the operation and development of neural networks. Data is fed into the input layer before being processed in the hidden layer where nodes combine the data with coefficients that assign significance to each input. It’s obviously a very complex process and one that has a multitude of use cases in real-world settings. Neural networks are used for fraud detection, market research, and risk analysis, among other tasks. For fraud detection, neural networks can identify suspicious signals and patterns of behaviour. These networks are also part of machine learning.

Robotics

Robotics is one aspect of AI that most people have some familiarity with due to the use of robots in fictional works in movies and TV shows. The design and construction of these robots continues to develop at a startling pace. Robotics relies on various aspects of engineering including mechanical and electrical, as well as science, to design and build computer systems that can process information and interact with the physical world in some way. Robots process information and use sensors to act in real-world environments.

Robotics isn’t just about humanoid robots though. It can involve any form of robot that has been mechanically constructed, contains electronic components and is directed by programming and coding. Industrial robots are often the most productive and efficient as they are capable of completing tasks in a way that humans struggle to replicate consistently. Assembly lines at vehicle manufacturers have a number of robotics, for example.

Experts systems

One of the earliest forms of artificial intelligence involved expert systems, which aim to replicate a human’s decision making. These systems were first designed back in the 1970s and feature two main subsystems – a knowledge base and an inference engine. The base sets out the facts and rules for the engine to apply to the situation and then learn from. Experts systems have been widely used in the finance sector where they regularly conduct loan and investment analysis. Medical facilities also use expert systems to detect viruses.

Natural Language Processing

Data again is central to a branch of AI called natural language processing (NLP) which is concerned with building systems capable of understanding text and voice data. Several of the main branches of AI are involved here including machine learning. This helps computers to analyse and process the human language to an advanced level. This deeper understanding allows systems to grasp nuances, context, intent and sentiment, for example, unlocking its “full meaning”.

NLP is used in a number of different applications but one of the most mainstream examples is for digital assistants like Siri and Google Assistant. Recent advancements in NLP have made these assistants more conversational and capable of delivering fast, human-like responses. ChatGPT has also taken NLP to the “next level” according to experts as it’s capable of generating accurate, in-depth responses and pages of text just from simple prompts. This is an incredibly exciting development for AI and testament to how vital NLP is for powering advanced forms of artificial intelligence.

Fuzzy logic

The final main branch of AI that attempts to imitate human behaviour is fuzzy logic. This branch deals with reasoning and cognition, and as the name suggests, doesn’t attempt to define singular or binary cases of truth but varying degrees of it. Fuzzy logic is built from the theory that humans often try to make informed decisions without having full access to the right information. Fuzzy set theory recognises and represents this uncertainty with the aim of coming up with a solution. This is useful in instances where there are continuous variables. Again, data and information is critical to decision making. Fuzzy logic is used in a range of industries including automotive construction.

To conclude, there are six core branches of AI, but there are many models, systems, and forms of tech that are powering the simulation of human intelligence by machines. The development of these models and systems is only going to gather pace as AI deployments increase and businesses continue to invest more in this space to drive growth and potentially transform how people live and work.