A closer look at AI
To understand AI accurately, it is important to define it properly. AI is a subset of information technology (IT) that aims to develop systems and “robots” capable of emulating and even surpassing human intellect.
AI comprises five key components:
- Computer vision
- Expert systems
- Natural language processing
- Machine learning (which we will explain in detail shortly)
These AI components have been implemented across various domains, and we have witnessed their capabilities firsthand. Prominent examples include Sophia, the humanoid robot developed by Hanson Robotics, which offers us a glimpse into the future. You may have come across Sophia’s remarkable feats on YouTube. Another noteworthy example is ChatGPT, a chatbot that has captivated students and posed challenges for teachers.
Although AI is still evolving, it has already become an integral part of many present-day technologies. For instance, Siri, the popular virtual assistant on Apple devices, relies on AI to automate tasks, simplify processes, and provide instant access to information that would otherwise be time-consuming to gather.
Exploring machine learning
Now, let’s delve into the relationship between AI and machine learning.
Machine learning is a subfield of AI that enables machines to learn automatically from experience rather than being explicitly programmed. Machine learning algorithms analyze data, extract insights, and make decisions based on that information. With continued training, these algorithms improve their performance. It is important to note that machine learning primarily relies on structured or semi-structured data.
Machine learning has also given rise to deep learning, a technique inspired by the human brain. Deep learning models attempt to process data in a manner similar to humans, making them particularly effective in complex pattern recognition and in-depth analysis of different types of data, including text, sound, and images.
The 3 subcategories of machine learning
Similar to AI, machine learning can be further classified into three main subcategories:
Reinforcement machine learning: This approach involves learning through trial and error. The system explores various sequences of actions and receives rewards when it makes correct decisions.
Supervised machine learning: In supervised learning, machines learn from labeled datasets. For example, an algorithm can be trained using labeled photos of different trains to enable it to identify trains autonomously.
Unsupervised machine learning: Unlike supervised learning, unsupervised learning leverages unlabeled data. The machine explores this data to discover hidden patterns that may not be explicitly sought. This approach can be useful, for instance, in identifying patterns in customer purchasing behavior.
How AI & machine learning are used in 3 industries
Now that we have explored AI and machine learning in detail, let’s delve into their practical applications across various industries.
AI and machine learning have significantly transformed numerous sectors, revolutionizing the way we work and enhancing efficiency while saving resources.
Let’s explore three notable areas:
In the manufacturing industry, AI is utilized in machine monitoring systems that evaluate equipment performance and provide insights into maintenance requirements, optimizing manufacturing processes.
AI has revolutionized risk assessment in the finance sector by precisely evaluating risk through data analysis. This enables businesses to make faster and more informed decisions while saving time and costs compared to traditional approaches.
AI has found valuable applications in healthcare as well. Companies like PathAI are leveraging machine learning technology to assist pathologists in making more accurate diagnoses, with the goal of reducing errors in cancer diagnosis and developing personalized treatment methods.
AI & machine learning: Setting the precedence for the future
AI and Machine Learning: These technologies are paving the way for an unimaginable future. Many tasks currently performed by humans have the potential to be successfully automated by AI. However, it is crucial to consider the broader implications and challenges associated with the widespread adoption of these technologies, ensuring responsible and ethical practices.