AI is increasingly shaping various aspects of society, from education, healthcare, and finance to entertainment and transportation. Being informed about AI advancements helps you understand how these technologies might affect your life and the world around you.

Before delving further into AI, let’s explore how Artificial Intelligence differs from traditional hard coding.

Let’s illustrate this with an example: Imagine we have some data (excel file) about fruit information with attributes like size, color, and shape. Our task is to categorize these fruits into different types, such as apples, bananas, grapes, etc. Initially, a programmer could write code to achieve this by applying specific logic. However, as the dataset expands to include information about fruits worldwide and new attributes like country of origin, taste, and texture are added, hard coding becomes impractical. This is where we can leverage artificial intelligence to automate the categorization process.

Definition of AI: Artificial intelligence, or AI, is a technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.

In simple terms, Artificial Intelligence (AI) is about teaching machines to think and act like humans in problem-solving and decision-making. While humans learn from family, teachers, and experiences, machines primarily learn from data humans create. This data could be anything from images and text to numbers and patterns. With AI, machines can recognize faces in photos, translate languages, and even predict what you might want to buy online. Despite these capabilities, AI still lacks the creativity, emotions, and adaptability that make humans unique.

Now that we have an understanding of Artificial Intelligence, let’s delve a little deeper and explore its various domains in a simplified manner.

We often hear people discussing terms like machine learning, deep learning, data science, generative AI, along Artificial Intelligence. Why do we need all these terms instead of just using one term, AI, to encompass everything? You’ll soon understand the reason behind this.

Machine Learning is the field of study that equips computers with the capability to learn without requiring explicit programming. It operates on data-driven technology, leveraging the substantial amount of data generated by organizations daily. By identifying notable relationships within this data, organizations can make more informed decisions. Machines can autonomously learn from past data and continuously improve. By analyzing the given dataset, they can detect various patterns within the data, enabling them to extract valuable insights.

Scientists coined the term “Machine Learning” in the 1950s. However, why has Machine Learning become such a trend now, despite the limited progress over the past 70+ years? The answer lies in the scarcity of digital data. Machine Learning thrives on large volumes of digital data for training and refining its models. The accuracy of these models hinges on the quality and quantity of input data. Consequently, a substantial amount of data is essential for developing more accurate models capable of predicting outputs with precision.

So, it’s evident that machines learn from data to perform tasks. Based on this learning process and the labeling nature of data, there are three main types of Machine Learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Machine Learning typically requires structured data, such as Excel or CSV files, without duplicates or null values. However, our data isn’t always structured; it can come in various formats like English paragraphs, pie charts, arrays, etc. In such cases, traditional machine learning may struggle to handle the data effectively. To address this challenge, Deep Learning has been introduced.

Deep Learning is a subfield of machine learning characterized by multiple layers situated between input and output layers, designed to learn representations of data. The hierarchical structure of these multiple layers bears a resemblance to the neural networks of our brain. When provided with a vast amount of information, the system begins to comprehend it and generate useful responses. These layers consist of interconnected neurons organized hierarchically, comprising an input layer, one or more hidden layers, and an output layer.

Deep learning excels at learning features or representations directly from raw data, eliminating the need for cleaning the data to remove null and duplicate values. Additionally, it can handle data in various formats such as English paragraphs, pie charts, tables, etc. This capability enables the model to learn intricate patterns and relationships within the data.

1. Using AI chatbots to answer questions from students about career opportunities, academic guidance, student housing, scholarships, and financial aid. Beyond typical academic or administrative hours, students can receive additional support and information from this round-the-clock service.
2. Research support: leveraging AI tools to sift through vast volumes of data, identifying patterns, and predicting insights. These tools aid in efficiently sorting through extensive datasets, enabling researchers to extract valuable information and anticipate trends hidden within the data.
3. Familiarize yourself with current AI models like ChatGPT and undergo training to leverage their capabilities effectively.
4. Engage with AI communities, attend workshops, and discuss with colleagues to brainstorm ideas on integrating AI into your workflow while ensuring ethical usage.
5. Our overarching goal is to integrate an AI model tailored to our organization or department, trained specifically with our university’s/department data and only accessible to university staff and students. This initiative aims to not only enrich the experiences of our customers and alumni but also streamline and elevate our internal operations.

Think about the repetitive stuff you do very often. Could some of it be done automatically using artificial intelligence? Discuss your thoughts with us or any IT experts you know.

• Department of Electrical & Computer Engineering Research Advancements.
• AI-Based Literature Review Tools.
• Making AI Safer: Texas A&M Joins National Consortium.
• LinkedIn Learning.

Find out what ChatGPT or Copilot can do and how to use them well. Read the Revolutionizing Productivity with Copilot for more tips.