Welcome to the AI Nine O. In this series, we try to distil difficult concepts regarding AI and make it simple for the viewer in under 2 minutes.
Step Out On A Fantastic journey through the world of generative AI, where algorithms are creating art, music, text, and even innovations! This short read unfolds just how this revolutionary technology works, some truly incredible applications, and some very serious questions about ethics. Join us in a look at the future of creativity powered by AI.
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#1: What is Generative AI?
In simple terms, generative AI involves algorithms that create content from which literature, pictures, music, or code originate. Generative AI produces like the human brain does—content generation, while traditional AI systems often employ data to derive forecasts or solve a problem.
Generative AI is not a glimpse of the future; it's a game-changing driver of innovation and efficiency.
#2: How Does It Work?
Generative AI uses a specific category of machine learning models, including GAN (Generative Adversarial Network) and transformer-based models such as GPT (Generative Pre-trained Transformer). Such models are trained on large data amounts, enabling them to learn through recognising many patterns, relationships, and structures in the data.
GANs work by making two networks, a "generator" that keeps generating new content and a "discriminator" that checks if the content looks real. They work in tandem to keep improving each other until the generated content is convincing (Goodfellow et al., 2016).
Like GPT, transformer models analyse text-based data to determine how one can speak, answer, and even create content that seems almost human-like (Vaswani et al., 2017).
#3: How Can We Use It?
Generative AI has vast applications across disciplines:
AI in Content Generation: Authors and artists use generative AI to write articles or generate digital art.
AI in Education: Generative models help teachers build personalised learning content or simulations.
AI in Healthcare: Scientists use AI to synthesise atomic structures or generate synthetic medical data to analyse.
To address plagiarism issues, experts, organisations, and policymakers (stakeholders) must set up clear guidelines or rules on the use of generative AI responsibly and ethically (Russell & Norvig, 2016).
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