Generative AI: My Experience

I wrote some LinkedIn posts about Generative AI and am now consolidating them into a more cohesive article. The article below delves into Generative AI, illustrating how I applied it with real-life examples. The use cases will illustrate how this technology can be harnessed effectively in different scenarios. Lastly, I'll discuss how this technology has significantly enhanced my passion for continuous learning.

So, what exactly is Generative AI?

An intriguing article from the Wall Street Journal discussed how companies are leveraging Generative AI, with real-world applications. It's important to recognize that while AI has been around for some time, it's the generative aspect that truly sets it apart and opens up new possibilities.

Generative AI, exemplified by tools like ChatGPT, has gained popularity due to its ability to comprehend and converse like humans, marking a significant departure from traditional AI. OpenAI's widespread distribution of ChatGPT led to increased usage and a growing dataset. This iterative process has resulted in models becoming more intelligent and accurate over time. As more users engage with these models, they become even more refined, prompting companies to explore applications using their own datasets.

Here's how I've been leveraging AI:

My primary use of Generative AI centered around improving my research and learning endeavors, with a focus on tools like ChatGPT and Bard. While experimenting with DALL-E by OpenAI for generating images, I felt like my natural inability to draw was finally remedied, as the tool generates images from textual descriptions. However, my artist mom, raised an eyebrow at its potential impact on artistic mojo and creativity, and I felt obliged to stop.

From a technical perspective, using Hugging Face for code snippets really helped me get things done faster. The extensive library of pre-trained models and user-friendly interfaces simplified the natural language processing algorithms that I would otherwise had to research and write.

Finally, I played with TensorFlow to understand deep learning models. I built a simple machine learning model that classified images of cats and dogs. Starting with the basics is key, as TensorFlow can get quite complex. I encourage others to explore this powerful tool step by step.

Here are some practical ways I've applied ChatGPT:

Blog Writing: I draft blog posts in my own words and then task ChatGPT with rewriting them from multiple perspectives. This approach enables me to combine various viewpoints or tailor the content for different audiences.

Answering in User Terms: I use ChatGPT to answer questions in terms that resonate with the audience. For instance, when my UX/UI partner needed help with design-related questions, I sought ChatGPT's input to ensure my responses aligned with their expertise.

Email Communication: At first, I tried using ChatGPT to make my emails sound nicer when I was upset, but then I realized it's better to just be myself. Now, I use it to help me get my point across clearly and make sure my messages are easy to understand.

How it has helped me learn:

For me, this tool has been a game-changer in my learning journey because it adapts to my style. Unlike traditional teaching methods, it allows me to ask questions, has patience with me, and categorizes historical perspectives that enable me to reason from first-principle. My learning curve has soared since using this technology. However, it's important to note that the tool has biases and ultimately relies on 'human-generated' data, so I cross-reference information I genuinely care about. Stepping outside my usual SaaS writing, AI has meaningfully changed and accelerated my passion for learning, so I had to share.

Summary:

This article explores how Generative AI can make a big difference, from improving work tasks to changing the way we learn. It emphasizes the importance of accepting this technology for its ability to bring great benefits in our fast-changing work world.