04. Artificial Intelligence (AI) vs. Machine Learning

What is Artificial Intelligence?

Artificial intelligence (AI) refers to processes and algorithms that can replicate human intelligence, including cognitive abilities like perception, learning, and problem solving. There are four levels of artificial intelligence (AI), two of which we have achieved and two of which are currently theoretical. Predictions and automation are used by AI to optimize and perform complex activities that people have traditionally done. Today, artificial intelligence drives many of the technologies we use, such as smart devices and voice assistants like Siri on Apple devices. (Biswal 2023)

Types of Artificial Intelligence

Artificial Intelligence can be divided based on capabilities and functionalities.

There are three types of Artificial Intelligence-based on capabilities -

• Narrow AI
• General AI
• Super AI

Under functionalities, we have four types of Artificial Intelligence -

• Reactive Machines
• Limited Theory
• Theory of Mind
• Self-awareness

Artificial Intelligence Based on Capabilities

 What is Narrow AI?

Narrow AI, also known as Weak AI, concentrates on a single task and is incapable of performing beyond its constraints. It focuses on a single group of cognitive talents and progresses along that spectrum. Narrow AI applications are becoming more widespread in our daily lives as machine learning and deep learning methodologies advance.

As an example:  Apple Siri is an example of a Narrow AI, which performs a limited set of pre-defined functions. Siri frequently struggles with tasks that are beyond its scope of capabilities.

Google Translate, picture recognition software, recommendation systems, spam filtering, and Google's page-ranking algorithm are further instances of Narrow AI. (Biswal 2023)

What is General AI?

General AI, also known as strong AI, is capable of understanding and learning any intellectual work that a human being is capable of. It enables a machine to use information and skills in a variety of circumstances. So far, AI researchers have not been able to build robust AI. They'd have to figure out how to make machines conscious, programming a whole range of cognitive abilities. Microsoft has invested $1 billion on general AI through OpenAI. (Biswal 2023)

 What is a Super AI?

Super AI exceeds human intelligence and is capable of performing any task better than a human. Artificial superintelligence envisions AI evolving to be so similar to human sentiments and experiences that it not only understands them, but also elicits emotions, needs, beliefs, and goals of its own. Its existence is still speculative. Thinking, solving riddles, forming judgments, and making decisions on their own are some of the fundamental qualities of super AI. (Biswal 2023)

Artificial Intelligence Based on Functionalities

 What is a Reactive Machine?

A reactive machine is the most basic type of artificial intelligence because it does not store memories or utilize previous experiences to predict future behaviors. It only works with current data. They notice and respond to their surroundings. Reactive machines are given defined tasks and have no capabilities beyond those tasks. (Biswal 2023)

What is Limited Memory?

Memory Impairment To make decisions, AI learns from past data. Such systems' memories are short-lived. They can use this historical data for a specific period, but they cannot add it to their experience library. This technology is found in self-driving cars. (Biswal 2023)

 What is the Theory of Mind?

Psychology theory AI is a sophisticated type of technology that exists only as a notion. Such AI necessitates a detailed grasp of how people and items in an environment can influence feelings and behaviour. It should be able to comprehend people's emotions, sentiments, and thoughts. Even though there have been many advancements in this sector, this type of AI is not yet complete. (Biswal 2023)

What is Self-Awareness?

Self-awareness AI exists solely in theory. Such systems comprehend their internal characteristics, states, and situations, as well as human emotions. These machines will be more intelligent than the human intellect. This sort of AI will not only understand and elicit emotions in individuals with whom it interacts, but will also have emotions, needs, and beliefs of its own. (Biswal 2023)

What is machine learning?

Machine learning is a step toward artificial intelligence. This AI subcategory use algorithms to automatically gain insights and spot patterns in data, then applies that learning to make increasingly better decisions. Programmers test the limits of how much they can improve a computer system's perception, cognition, and behavior by researching and experimenting with machine learning. 

Deep learning, an advanced type of machine learning, takes a step farther. Deep learning methods use massive neural networks — networks that act logically interpret data like a human brain — to understand complicated patterns and generate predictions independent of human input. (2022)

These different types of machine learning cover a range of complexities. And while there are several other types of machine learning algorithms. (2022)

Supervised Learning

The method is trained on a labeled dataset in supervised learning, where the input data is associated with the correct output. The goal is for the algorithm to learn a mapping from inputs to outputs so that it can make predictions on fresh, previously unseen data. Classification (classifying data) and regression (predicting continuous values) are two examples. (2022)

Unsupervised Learning

Unsupervised learning involves gaining knowledge from unlabeled data. Without explicit output labels, the algorithm attempts to detect patterns, structures, or relationships within the data. Clustering (grouping related data points) and dimensionality reduction (cutting the number of features while maintaining crucial information) are two common tasks. (2022)

Semi-Supervised Learning

This method combines supervised and unsupervised learning. The method is trained using both labeled and unlabeled data from a dataset. It seeks to improve the learning process by utilizing more unlabeled data to improve the model's performance. (2022)

Reinforcement Learning

Reinforcement learning is the process through which an agent learns to perform behaviors in an environment in order to maximize cumulative rewards. The agent learns by interaction with its surroundings and feedback in the form of rewards or punishments. It's often employed in scenarios like gaming, robotics, and self-driving cars. (2022)

Deep Learning

Deep learning is a branch of machine learning that models and processes complicated patterns in huge datasets using artificial neural networks. Deep learning architectures, such as convolutional neural networks (CNNs) for pictures and recurrent neural networks (RNNs) for sequences, have demonstrated significant performance in image recognition, natural language processing, and other tasks. (2022)

AI and Machine Learning in Banking

The banking sector places an importance on data privacy and security. Financial services leaders can use AI and machine learning to secure consumer data while enhancing productivity in numerous ways: (2022)

Machine learning is being used to detect and prevent fraud and cybersecurity breaches. Biometrics and computer vision are being combined to quickly validate user identities and handle documents. Using smart technology like chatbots and voice assistants to automate basic customer care operations. (2022)

Conclusion

Ultimately, the increasing use of Artificial Intelligence (AI) and Machine Learning (ML) technology in companies has heralded a new era of creativity, efficiency, and disruptive innovation. These technologies have shown immense potential in a variety of sectors and industries, transforming the way businesses function, make decisions, and engage with their stakeholders.

Automation and optimization powered by AI have transformed regular and time-consuming tasks, allowing staff to focus on more strategic and creative endeavors. Organizations can make educated decisions, recognize opportunities, and reduce risks with unparalleled accuracy thanks to predictive analytics and data-driven insights.

Machine Learning algorithms have enabled businesses to discover hidden patterns in massive datasets, resulting in personalized customer experiences, focused marketing efforts, and superior product offerings. As a result, consumer satisfaction has grown.

Organizations that effectively integrate AI and Machine Learning into their operations while adhering to ethical principles and cultivating a culture of continuous learning will thrive in the coming years, despite the rapid evolution of technology and the dynamic demands of the modern business landscape. (Richardson 2023)

References

Biswal, A., 2023, 7 Types of Artificial Intelligence That You Should Know in 2023, Simplilearn.com.

2022, Artificial Intelligence (AI) vs. Machine Learning | Columbia AI, CU-CAI.

Richardson, D., 2023, What is AI/ML and why does it matter to your business?

Team, I.D.A.A. & Team, I.D.A.A., 2023, AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?, IBM Blog.