Information Technology and Ethics/Generative AI Ethics

What is Generative AI?

Generative Artificial Intelligence (Generative AI) is a type of technology that uses advanced algorithms to create new content. This can include text, images, videos, music, and even code. Unlike traditional AI, which is used mostly for analyzing or organizing information, generative AI produces original content by learning patterns from existing data.

The History and Background of Generative AI

Generative AI has come a long way, but its roots are closely tied to the broader development of artificial intelligence as a whole. In the beginning, AI systems were created to perform tasks that typically required human intelligence. One of the most influential figures in this early stage was Alan Turing. In 1950, he proposed the famous Turing Test, which aimed to measure whether a machine could "think" like a human. This idea became a foundation for future AI research. Turing’s early work helped shape what became known as symbolic AI, or “Good Old-Fashioned AI” (GOFAI), where machines followed strict, rule-based logic to make decisions. While these early systems were important, they lacked the flexibility and learning ability seen in today’s AI models.

For a long time, AI could only do what it was explicitly told to do. It could follow instructions well but couldn’t learn from experience or improve over time. That began to change in the 1980s with the introduction of neural networks. Inspired by how the human brain works, neural networks use artificial “neurons” that pass data through layers to recognize patterns. One of the first models was called the perceptron, which could complete basic tasks like recognizing letters or shapes. However, due to its limitations, interest in neural networks faded for a while. That changed with the development of the backpropagation algorithm, which helped AI systems learn from their mistakes. Researchers like Geoffrey Hinton played a big role in improving these methods. Combined with better computer hardware, this allowed deep learning—neural networks with many layers—to become more powerful and capable of handling complex tasks like speech and image recognition.

Generative AI started becoming more of a reality in the early 2000s. Earlier AI could spot patterns, but it couldn’t actually create anything new. Some of the first generative models, such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs), were used for tasks like speech recognition and simple image generation, but they had limitations in producing diverse or detailed content. A major turning point came in 2014 with the invention of Generative Adversarial Networks (GANs). These systems use two networks: one generates content, and the other checks how realistic it is. This method helped AI produce much more convincing and lifelike images, videos, and more.

In 2017, another breakthrough happened with the introduction of transformers. These models changed the way AI handles language by using something called "self-attention" to process entire sentences at once, rather than word by word. This allowed AI to better understand context and relationships between words, making the responses sound more natural and human-like.

One of the most well-known examples of this is GPT-3 (Generative Pretrained Transformer 3). It’s a powerful AI model that can take a simple prompt and generate detailed, relevant responses. Tools like GPT-3 show just how far generative AI has come in creating text that feels genuinely human.

Today, generative AI models are more advanced than ever. Tools like DALL·E can take a short description—like “a surfing dog”—and turn it into an entirely new image. This ability to combine language and visuals shows how generative AI is pushing the boundaries of creativity. As these systems continue to improve, they are not just helping us make content—they are reshaping how we think about imagination, art, and human expression.

Fields in which Generative AI has been used

Generative AI has had a noticeable impact across many industries, leading to changes in how work is done and how information is created and shared. In education, it is being used to design personalized worksheets, flashcards, diagrams, and even games or simulations. These tools help teachers create more engaging lessons and allow students to learn in a way that suits their needs. Game-based learning, for example, uses both digital and physical games as a way to improve classroom participation and make learning more interactive and immersive.

In healthcare, generative AI plays a role in improving both patient care and medical training. It is used to generate medical diagrams that compare a patient’s current condition to expected outcomes, helping patients better understand their treatment. AI-generated procedure videos and AR/VR simulations are also used to train medical professionals. One advanced technique called diffusion modeling uses AI to denoise and enhance medical images. Researchers tested this method on datasets like chest X-rays, MRIs, and CT scans, and found that it outperformed previous methods in terms of clarity and accuracy.

The automotive industry uses generative AI to create technical diagrams, generate concept car designs, and explore new approaches to engineering problems. These systems can suggest innovative design options by working within specific constraints, offering ideas that may not have been considered before. This concept of AI-generated design has also appeared in the tech world, where people use AI to imagine future versions of iPhones, gaming consoles, and other upcoming technologies.

In construction, generative AI is used to produce digital blueprints and explore structural concepts. It can also generate safety materials, such as training videos and posters, to improve communication on job sites and help prevent accidents.

Social media is one of the most noticeably affected areas. Many content creators now use AI-generated images, voiceovers, and music in their posts, especially on platforms like TikTok and Instagram. While this has opened the door for new kinds of creativity, it has also raised concerns. The rise of deepfakes—videos that appear real but are artificially created—has led to misinformation and ethical debates. These manipulated videos can spread false information quickly and convincingly, making generative AI a growing concern in digital spaces.

How accessible is Generative AI?

Generative AI is widely accessible online. Many platforms offer free tools, while others charge for advanced features. You can run many of these tools on basic devices, including phones. Some popular examples include:

ChatGPT – for general use

DALL·E 3 – for AI image generation

Grammarly – for writing

ElevenLabs – for audio work

Framer – for website building

Wondershare Filmora – for video work