Goal Met: 50 hours of AI learning

This was an interesting one as the sources of learning ended up being pretty varied. There were two books I read “Co-Intelligence” and “The AI Driven Leader”, I watched a ton of YouTube videos and took a few training classes (some virtual, some in person). I came into this thinking AI could be a useful tool for some automation tasks or research and came out of it with a tool set that would let me use AI to improve almost every aspect of what I do. Learning advanced prompt engineering was the biggest takeaway. AI responds to what you prompt it with – a weak prompt will get you weak results. You can create specific projects that have long detailed instructions that allow you to have custom assistants that behave in a predefined pattern. When I started creating images the results weren’t great until I dug in and learned what the AI needs to create stunning visuals.

I’m going to let ChatGPT take the rest of this post – and explain what AI is and the current AI landscape. Take it away!
What is Artificial Intelligence?

Artificial Intelligence (AI) is a type of technology that allows computers to think and learn like humans. Instead of just following strict instructions, AI can analyze data, recognize patterns, and make decisions on its own. Think of AI as a super-smart assistant that can understand questions, find answers, and even create new things like artwork, music, or writing.

At its core, AI is powered by something called machine learning, which is a way for computers to learn from experience. Instead of being programmed with every possible answer, an AI system is trained using massive amounts of data. It looks at patterns, makes predictions, and adjusts itself over time. This is similar to how we learn—by seeing, practicing, and improving. AI also uses neural networks, which are computer systems inspired by how the human brain works, allowing it to make more complex decisions.

There are two main types of AI: narrow AI and general AI. Narrow AI is what we use today—it’s specialized and good at specific tasks, like recommending movies on Netflix, translating languages, or helping doctors detect diseases. General AI, which doesn’t exist yet, would be a machine that can think and learn just like a human across different topics, but we’re still far from that.

Why Prompt Engineering is So Important

Since AI relies on learning from data and responding to inputs, the way we ask it questions—called “prompts”—is incredibly important. If we give AI a vague, confusing, or misleading prompt, we’ll get an answer that isn’t useful. This is where prompt engineering comes in.

Prompt engineering is the skill of crafting the right kind of questions or instructions to get the best possible response from an AI system. It’s like giving directions to a friend—if you’re too vague, they might get lost, but if you’re clear, they’ll find their way easily.

Let’s look at some examples:

Bad prompt: “Tell me about history.”

This is too broad. AI won’t know if you mean world history, American history, or the history of pizza. It might give a general answer that isn’t useful.

Good prompt: “Explain the major causes of World War II in simple terms.”

This is much better because it specifies the topic and asks for a simplified answer, making it more likely that AI will give useful information.

Another example:

Bad prompt: “Write a story.”

AI could generate anything—maybe a horror story, maybe a children’s fairy tale. It’s unclear what you want.

Good prompt: “Write a short science fiction story about a robot that discovers emotions.”

Now AI has a clear direction, and the output will be more relevant and interesting.

Prompt engineering is essential because it helps users get precise, useful, and creative answers from AI. As AI tools become more advanced, those who know how to ask the right questions will get the best results.

Five Cutting-Edge AI Use Cases

AI is already transforming industries in amazing ways. Here are five cutting-edge uses of AI:

  1. Medical Diagnosis and Drug Discovery – AI helps doctors detect diseases like cancer earlier and faster. It can also predict how different drugs might work together, speeding up the creation of new medicines.
  2. Self-Driving Cars – AI allows cars to “see” the road, recognize traffic signs, and react to other vehicles, making autonomous driving safer and more reliable.
  3. AI-Powered Art and Music – AI can now create stunning paintings, realistic music, and even deepfake videos that look just like real people.
  4. Smart Assistants and Chatbots – Virtual assistants like Siri and Alexa use AI to understand speech and answer questions, while advanced chatbots handle customer service for businesses.
  5. Climate Change Predictions – AI is helping scientists analyze weather patterns and predict climate changes, leading to better strategies for protecting the environment.

Five Non-Obvious AI Use Cases

Beyond the flashy tech, AI is also being used in unexpected ways:

  1. Personalized Learning – AI tutors can customize lessons for students based on how they learn best.
  2. AI in Farming – AI-powered drones and sensors help farmers track crop health, predict weather changes, and maximize food production.
  3. Fraud Detection – Banks use AI to recognize suspicious transactions and prevent fraud before it happens.
  4. Mental Health Support – AI chatbots are being used as virtual therapists, providing emotional support to those in need.
  5. Sports Analytics – AI analyzes player movements and strategies to help teams improve their performance and predict game outcomes.

The Benefits and Risks of AI

AI brings enormous benefits, making life more convenient, boosting productivity, and even saving lives in medicine. It helps automate repetitive tasks, provides better recommendations for movies or products, and assists in solving complex problems. It can also improve accessibility, such as voice assistants helping visually impaired people navigate the world.

However, AI also comes with risks. One major concern is bias—AI learns from human data, and if that data is flawed, AI can make unfair decisions, like discriminating in hiring or loan approvals. Another risk is job displacement. As AI becomes more capable, certain jobs may become automated, meaning some workers will need to adapt or retrain. There are also ethical concerns around deepfakes, misinformation, and privacy, as AI can be used to manipulate information and invade personal data.

Where AI is Going in the Next 5 Years

AI is evolving rapidly, and in the next five years, we’re likely to see major advancements. AI could become much better at understanding emotions, making conversations feel more natural. We might also see more AI in creative fields, like writing, filmmaking, and game design. In medicine, AI could help develop personalized treatments based on a person’s genetics. AI will also play a bigger role in automation, from self-driving trucks to fully automated smart homes.

As AI grows more powerful, there will be more discussions about ethical use, regulations, and ensuring AI benefits everyone, not just big companies. The key will be balancing progress with responsibility, making sure AI is developed and used in ways that help rather than harm society.

Final Thoughts

AI is one of the most exciting and powerful technologies in the world today. It can do everything from answering questions and writing stories to driving cars and diagnosing diseases. But to get the most out of AI, people need to learn how to ask the right questions—this is why prompt engineering is such an important skill. As AI continues to evolve, it will bring new possibilities and challenges, shaping the way we work, create, and interact with the world. The future of AI is bright, but it’s up to us to guide it responsibly

AI – Google Notebook LM

Google’s NotebookLM is an AI-powered research and writing assistant designed to help users synthesize and interact with their own notes, documents, and ideas in a more intelligent and intuitive way. At its core, it allows users to upload files, input text, and link sources, then leverages Google’s advanced language models to provide summaries, generate insights, and answer questions based on the specific materials provided. Unlike traditional AI chatbots that rely on broad internet knowledge, NotebookLM focuses on personal and curated content, making it particularly valuable for deep research, writing projects, and knowledge management.

One of the most obvious applications of NotebookLM is for students and researchers who need to digest large amounts of information efficiently. By uploading lecture notes, research papers, or even entire books, users can get concise summaries, cross-reference concepts, and generate study guides without having to manually sift through pages of text. Writers and journalists can also benefit by using it to structure articles, generate outlines, or even fact-check details against their own sources, ensuring accuracy while streamlining their workflow.

Beyond these expected use cases, NotebookLM has the potential to be a game-changer in more unconventional ways. For example, a legal professional could use it to analyze contracts and legal documents by asking it to highlight key clauses, compare different agreements, or explain complex legal language in simpler terms. Similarly, entrepreneurs and product managers might find it useful for competitive analysis, feeding it market reports and customer feedback to extract key insights and trends that inform business strategies. Even artists and creative professionals could leverage NotebookLM in unique ways, such as using it to organize scattered notes, develop thematic connections between ideas, or even generate poetic interpretations of their own past writings.

Another less obvious but powerful application is in personal knowledge management. Users who keep extensive journals, meeting notes, or personal reflections can use NotebookLM as a kind of second brain, allowing them to surface forgotten ideas, track recurring patterns in their thinking, or even generate personalized recommendations for self-improvement based on their own writing. It could also be valuable for historians or genealogists, who could feed it letters, old documents, and historical records to uncover new insights or narratives within archival materials.

By grounding AI-generated responses in user-provided content, NotebookLM bridges the gap between personal knowledge and artificial intelligence, making it more than just a generic chatbot. Whether used for research, creative exploration, or business intelligence, it redefines how individuals engage with their own information, unlocking new possibilities that go beyond simple text generation.

I immediately had two use cases for Notebook LM. The first was feeding it all the information on the 50 for 50 program and having it provide insights on things that I accomplished. It was able to answer basic questions like how much time I spend each month on a specific task but since it could also search the web I was able to feed it information that would bolster the information I provided. It did a great job of making connections I didn’t see and the ‘make your own podcast’ feature was amazing. It spit out a 20 minute podcast as if I was a guest on it and it was pretty seamless and even let you tweak it to focus on specific things if you wanted.

The second was feeding it all my book notes and chapters and asking it questions based on the world lore and character bios. Thinks like ‘would this character actually behave this way?’ The more information you feed it the smarter it gets on the specific topic you’re developing. I can see a huge opportunity there as I build out the ideas and add characters and world lore. You can also feed it writing guides (like Orson Scott Card’s on writing fantasy and science fiction) to give it a framework to evaluate what you are writing. Really amazing stuff and I can’t wait to keep working on it and discovering new use cases!

AI – Text to Image Generation Tools

One of the first things I explored in the realm of AI was text to image generation. It wasn’t my first interaction with this type of AI – I had checked out Firefly on my work account to generate a few things and even used Midjourney when it was first introduced. I wasn’t impressed really with either tool at the time and shelved the idea of using them for any real work. Two years later as I started really leaning into learning AI I decided to revisit the major text to image tools to see how far they’ve come along. To test them out I decided to feed them straight descriptions of some of the characters in the story/book I was writing. I created a prompt for each one and fed the identical prompt into each image generator.

The first thing I noticed was they really didn’t like making unattractive people. Even if the prompt specified that a person was older some models kicked out images of people in their 20’s. Even the old weathered warrior looked like he belonged on the cover of GQ. I know I could have tinkered with the prompt and really pushed it to the image I wanted but for this experience I wanted to use the straight descriptions from the book.

The tools I used for this were DallE3, MidJourney and Adobe Firefly. I know adobe is all about cutting edge design but their tool failed on almost every prompt. It ignored critical details in the prompt like ages, scarring, hairstyles, etc. and basically served up whatever it thought was close. Coming in second was DallE which tended to spit out anime leaning images (although to be fair, I did this before I learned how to craft hyper specific image prompts) and also couldn’t seem to handle more specific prompting. The clear winner was MidJourney – it came the closest on each character and the quality of it’s output was actually inspiring to me as it was how I viewed some of these characters in my head.

I know there’s other tools out there but I settled on the ‘big 3’ for this comparison so without further ado here’s the output from each tool for the major characters (so far)

After I got these results I did a deep dive into prompt crafting for images and got a lot better at directing the tools to produce what I wanted. I didn’t get a chance to go back and recreate all these characters but here’s some random images from the book that I created using these advanced prompts